More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\n
Our breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n
“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\n
Additionally, each book published by IntechOpen contains original content and research findings.
\\n\\n
We are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\n
Simba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\n
IntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\n
Since the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\n
Our breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n
“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\n
Additionally, each book published by IntechOpen contains original content and research findings.
\n\n
We are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n
\n\n
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"2277",leadTitle:null,fullTitle:"Applications of Virtual Reality",title:"Applications of Virtual Reality",subtitle:null,reviewType:"peer-reviewed",abstract:"Information Technology is growing rapidly. With the birth of high-resolution graphics, high-speed computing and user interaction devices Virtual Reality has emerged as a major new technology in the mid 90es, last century. \nVirtual Reality technology is currently used in a broad range of applications. The best known are games, movies, simulations, therapy. From a manufacturing standpoint, there are some attractive applications including training, education, collaborative work and learning. \nThis book provides an up-to-date discussion of the current research in Virtual Reality and its applications. It describes the current Virtual Reality state-of-the-art and points out many areas where there is still work to be done. We have chosen certain areas to cover in this book, which we believe will have potential significant impact on Virtual Reality and its applications. \nThis book provides a definitive resource for wide variety of people including academicians, designers, developers, educators, engineers, practitioners, researchers, and graduate students.",isbn:null,printIsbn:"978-953-51-0583-1",pdfIsbn:"978-953-51-5688-8",doi:"10.5772/2667",price:119,priceEur:129,priceUsd:155,slug:"applications-of-virtual-reality",numberOfPages:224,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"1984848a9c90105b49dc6d3662c189e9",bookSignature:"Cecilia Sik Lanyi",publishedDate:"May 2nd 2012",coverURL:"https://cdn.intechopen.com/books/images_new/2277.jpg",numberOfDownloads:29083,numberOfWosCitations:23,numberOfCrossrefCitations:14,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:20,numberOfDimensionsCitationsByBook:3,hasAltmetrics:0,numberOfTotalCitations:57,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 5th 2011",dateEndSecondStepPublish:"June 2nd 2011",dateEndThirdStepPublish:"October 7th 2011",dateEndFourthStepPublish:"November 6th 2011",dateEndFifthStepPublish:"March 5th 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"106377",title:"Dr.",name:"Cecília",middleName:null,surname:"Sik Lányi",slug:"cecilia-sik-lanyi",fullName:"Cecília Sik Lányi",profilePictureURL:"https://mts.intechopen.com/storage/users/106377/images/2769_n.jpg",biography:'Dr. Cecília Sik Lányi studied Mathematics and Computer Science (B.S. and M.S.) at the József Attila University (1981 and 1984). She became a Teacher of Mathematics at the Berzsenyi Dániel Teacher Training College in 1988. Dr. Lányi obtained the Dr. Univ. degree at the University of Veszprém, Hungary in Physical-chemistry (1993), and the Ph.D. degree at the University of Veszprém, Hungary in Computer Science (2000). She has worked as a software engineer and as an associate professor for program languages at the University of Pannonia.\nCurrently, she is focused on virtual reality and its application, user interface design, computer graphics for informatics engineering students and using multimedia in the education for teacher training courses. Ph.D. and Masters’ supervision has an emphasis on multimedia/ virtual reality for the rehabilitation of children with disabilities and patients with mental health issues. She has supervised altogether 180 BSc and MSc thesis works from 1997. Her students received numerous awards.\nDr. Lányi received several awards, the most important ones are: “Master teacher” award of the Hungarian Ministry of Education (2001), the \\"Kalmar\\" award from the John von Neumann Computer Society (2016), the “Hungarian Higher Education Plague” of the Ministry of Human Capacities (2016), the “Diamond-Award from the Association for the Advancement of Assistive Technology in Europe (2015), which is a personal recognition, granted for outstanding work in advancing assistive technology in Europe and the “King Salman Award for Disability Research” of the King Salman Center for Disability Research (2018).\nShe was the secretariat manager of EDeAN in 2009 and the representative of Hungary in IFIP Technical Committee 13: Human-Computer Interaction (TC13) in the period of 2008-2018. 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1. Introduction
It has long been known that a repulsive force arises between a magnetic field (generated, for instance, by a permanent magnet - PM) and a superconductor –Sc (Arkadiev, 1947). This force is due to the repulsion of the magnetic field away from the superconductor – the Meissner effect. Type I superconductors only can be in the Meissner state, which means that a magnetic field will be always expelled from the superconductor, independently of its poles orientation. Nevertheless, type II superconductors may be in two different states: first, provided the magnetic field is low enough, they are at a Meissner state similar to type I superconductors. In this Meissner state they absolutely expel the magnetic field and prevalent repulsive forces appear. Second, for magnetic fields larger than the so-called First Critical Field HC1, the magnetic flux penetrates the superconductor creating a magnetization which contributes to an attractive resulting force. This second state is known as mixed state.
In 1953 Simon first tried to make a superconducting bearing (Simon, 1953) using superconductors in the mixed state.The first engine using a superconducting bearing was made in 1958 (Buchhold, 1960). After the discovery of high critical temperature superconductors (Bednorz & Müller, 1986), the Meissner repulsive force has become a popular way of demonstrating superconducting properties (Early et al., 1988).For calculating forces between a magnet and a superconductor it is necessary to have models that describe both the flux penetration state and the Meissner state repulsion. The first one can be solved by using conventional methods to compute forces between magnetic elements and magnetized volumes. However, for the Meissner state the question has remained open until these last years.
Several models using the method of images to calculate superconducting repulsion forces (Lin, 2006; Yang & Zheng, 2007) have been proposed. However, this method of images is limited to a few geometrical configurations that can be solved exactly, and the physical interpretation of the method is under discussion (Giaro et al., 1990; Perez-Diaz & Garcia-Prada, 2007). Furthermore, some discrepancies within experiments still exist (Hull, 2000).
A general local model based on London’s and Maxwell’s equations has been developed to describe the mechanics of the superconductor-permanent magnet system (Perez-Diaz et al., 2008). Due to its differential form, this expression can be easily implemented in a finite elements analysis (FEA) and is consequently appliable to any shape of superconductor in pure Meissner state (Diez-Jimenez et al. 2010).
In this chapter, we present the demonstration of the model, the implementation into a finite elements program, the experimental verification and its limit of application. To finish, we show an example of usage.
2. Magneto-mechanics of a superconductor in Meissner state
A superconductor is in a pure Meissner state when it is exposed to an externally applied magnetic field, H→ap, lower than a certain value, HC1. HC1 is a characteristic of the material (Alario & Vincent, 1991), which depends on temperature. In this case, it is assumed that both H→ and B→ are equal to zero inside the superconductor. When a magnetic field is then applied –for example by moving a permanent magnet close to the superconductor - a surface current density is generated on the outermost surfaces. According to the London equation, this current is confined only to a depth of λ(T). Type II superconductors, such as the rare earth oxide high temperature superconductors, have the highest values for λ, reaching typical values of thousands of Amstrongs (Umezawa & Crabtree, 1998). Therefore, as this paper deals with macroscopic elements, it can be approximated that current density has an infinitely localized surface current
J→=j→s(x,y)δ(z)E1
where j→s(x,y) is a surface current density tangent to the surface vector field and δ(z) is a Dirac delta function on z. This current density will makeH→ discontinuous when passing from the air or vacuum (z>0) into the superconductor (z<0).The second Maxwell law (Jackson, 1975) relates the magnetic field and the current density in such a way that j→s(x,y) is determined byH→ap.Using units from the MKSA system, this second Maxwell law can be written as
∇→×H→=j→+∂D→∂tE2
In the static limit it can be assumed that∂D→∂t=0. Therefore it may simply be written as:
∇→×H→=J→E3
H→may be decomposed in that externally applied H→ap and that generated by the superconducting currentsH→sc. Furthermore, these three vector fields will be decomposed both in tangent and normal to the surface components:
H→//=H→//ap+H→//scE4
and
H→⊥=H→⊥ap+H→⊥scE5
Note that H→ap is continuous and fulfils:∇→×H→ap=0, provided the permanent magnet does not touch the superconductor surface. On the contrary, both H→ and H→sc are discontinuous at the superconducting surface. In particular, bothH// and H//scare discontinuous.
By using the divergence theorem (Jackson, 1975) on a small parallelepiped with volume V, a face just above the superconductor surface and another parallel face under it, it can be written that:
∫V(∇→×H→)d3x=∫S(n→s×H→)dSE6
where S is the surface defining this parallelepiped andnS its normal vector. By using Maxwell law (3) it can be reduced to:
∫VJ→d3x=∫S(n→s×H→)dSE7
But, taking (1) into account, and considering H→=0→ under the superconducting surface it can be written that:
∫Sj→sdS=∫S(n→s×H→//)dSE8
where H→// is evaluated at z=0+ (limit above the superconductor surface).
As this result is independent of the small parallelepiped previously chosen, the integrands must equal:
j→s=n→s×H→//(z→0+)E9
Furthermore,H→//(z→0−)=0 and H→//sc(z→0+)=−H→//sc(z→0−) implies:
H→//sc(z→0+)=H→//ap(z→0+)E10
Therefore, an expression for the superconducting current as a function of the applied magnetic field may be written:
j→s=2n→s×H→//ap=2n→s×H→apE11
All expressions shown use the MKS unit system.
Applying the divergence theorem clearly shows that the total charge is always conserved, for whichever surface shape the superconductor has, provided the source of the applied field is outside the superconductor:
∫Sj→sdS=∫S2n→s×H→apdS=∫V∇→×H→apd3x=0→E12
Furthermore,
H→⊥=H→⊥ap+H→⊥sc=0→E13
which is consistent with the previous expression for the superconducting current.
The total field thus generated for a semi-infinite plane is equivalent to that generated by the method of images (Cansiz & Hull, 2005; Hellman et al. 1988; Hull & Cansiz, 1999; Moon, 1994). As the expression (11) has been derived using only local arguments, it may be used for any shape of superconductor. It does not depend on the curvature of the surface.
2.1. Force calculation
The external force (by unit surface) experienced by the superconductor can be calculated by using Lorentz force.
dF→dS=j→s×B→apE14
Using the previous expression for the superconducting current (1) and the constituent equation of air (15) (the medium in which the field is generated)
B→ap=μ0H→apE15
it can be written that:
dF→dS=2μ0(n→s×H→ap)×H→apE16
This is a local and exact expression for the “pressure” or more precisely “stress” or force per unit surface on the superconductor, which depends only on the applied magnetic field. It is useful for any shape of superconductor. This differs fundamentally from the general expression used to calculate the magnetic stress between magnetic materials as given by Moon.
According to Newton’s law, the force exerted by the superconductor on the magnet is simply the opposite one. Therefore, for any shape of superconductor, the force exerted by the superconductor on the magnet can be written as:
F→=−∬Sc2μ0(n→s×H→ap)×H→apdSE17
where the integration extends over the whole surface of the superconductor.
2.2. Torque calculation
The torque suffered by the superconductor can easily be deduced as :
M→Sc=∬Scr→×(2μ0(n→s×H→ap)×H→ap)dSE18
where r→ is the position vector between the differential surface element and the center of mass of the superconductor bulk. Again, the integration extends over the whole surface of the superconductor.
In order to calculate the moment applied over the magnet, the r→PMmust be the position vector between the differential surface element and the center of mass of the permanent magnet. As noted previously, the force exerted by the superconductor on the external magnetic field (in this case a single permanent magnet) is simply the opposite one, and the same applies for the torque.
M→PM=−∬Scr→PM×(2μ0(n→s×H→ap)×H→ap)dSE19
3. Finite elements implementation
Due to this differential form equation (16) can be easily implemented in a finite element program. A FEM algorithm has been adapted for the commercial software ANSYS. The SURF154 element of ANSYS was used insofar as it has defined a set of useful attributes e.g. the surface normal direction. The algorithm is valid in the context of a common electromechanical simulation. The steps for the simulation were:
Select Element Type: SOLID98 (with a maximum of one degree of freedom MAG) and SURF154.
Create the different materials to be used. For the superconductor bulk, air properties were used.
Generate the geometries of the volumes for the electromagnetic system.
Assign materials’ properties to each volume, selecting air for the superconductor.
Mesh the whole system with the SOLID98 element (as fine as is considered adequate - discussed further below).
Mesh the superconductor surface with the SURF154 element.
Apply the electromechanical loads to the system.
Solve the electromagnetic equation system.
Once the system has been solved, the algorithm can be applied using a Command List. Fig. 1 shows a flow-diagram of the procedure.
This procedure has to be performed for each piece of superconductor in the system. Should there be more than one piece, a different internal SURF154 element must be created and accordingly, the number of SURF154 elements in the first step (ESEL) must be changed.
These steps provide the three components of the force vector. The torque applied on the superconductor can also be calculated, from which the torques values can then be derived.
3.1. Results provide by the post-processing
The algorithm has been tested using one of the most common experiments found in relevant literature: a permanent magnet oriented vertically over a superconductor pile in any arbitrary position.
Firstly, an electromagnetic system composed of a small magnet suspended over a superconducting cylinder was designed, as shown in Fig. 2.
The dimensions of the superconductor were: 20 mm diameter and 7.5 mm height, and the small magnet was characterized by a coercivity of 875 kA/m and a remanence of 1.18 T, with a 3.5 mm diameter and a 2 mm height. The magnet’s centroid was placed 10 mm over the surface of the superconductor. The entire system was placed in surroundings measuring 100x100x100 mm.
The results that can be obtained are the distribution of forces, torque and current densities per surface element. In fig 3, these distributions for an arbitrary position of the magnet over the superconductor are shown.
Figure 1.
Flow-diagram of the algorithm.
Figure 2.
Small permanent magnet (m=0.016 Am2) over superconductor.
Figure 3.
Force, torque and current density distributions per surface element.
The same simulation was repeated several times with different meshes, increasing the number of elements for the whole simulation. Using the parameter α, the fineness of the mesh can be defined as the ratio between the maximum of the area of the elements and the total area of the superconductor multiplied by 100.
α=max(elementsareas)totalSCarea×100E20
Different meshes along with their respective α parameters are shown in fig. 4. The number of SURF154 elements and the values of the solution are also displayed.
Figure 4.
Different meshes of the superconductor pile.
Fig. 5 shows the relative error of the calculations in relation to the analytical solution (with a magnetic moment of 0.016 A m2). The higher the number of surface elements, the smaller the relative error of the result. For example, where α is smaller than 0.1 %, the resulting relative error is less than 3 %.
Figure 5.
Relative errors of the results vs. alpha for z=10 mm.
In Fig. force versus z are shown for different values of α. The FEM results tend towards the analytical values as α decreases. It is noted that the magnetic dipole approximation made for the analytical calculation only remains valid where there is a large distance between the permanent magnet and the superconductor.
Figure 6.
Levitation force computed by the analytical expression with a point magnetic dipole and by FEM with different α.
The convergence of the algorithm has been checked in relation to the finite elements’ size, and compared to analytical solutions for simple geometries. An α parameter has been proposed to assess the relative error in the results. The results showed good accuracy, whilst not requiring high specification computing technology.
4. Experimental verification
Different experiments were carried out in order to check the validity of the model. Some of them will be summarized in the following.
4.1. Force measurement
The following methodology was used to measure the forces: a cylindrical superconductor made of polycrystalline YBa2Cu3O7-x,manufactured by CAN superconductors (Kamenice 25168, Czech Republic) was immersed in a bath of liquid nitrogen N2 (77 K) at ambient pressure. The cylinder had a diameter of 45 mm and a height of 13 mm. It was fixed to a nitrogen vessel. The vessel, containing the superconductor, was placed on a lab jack stand to adjust the height. A small cylindrical permanent magnet was used, which had a coercivity of 875 kA/m, a remanence of 1.18 T, and had a diameter of 5 mm and a height of 5 mm. All experimental measurements followed the same coordinate system shown in Fig. 7. The origin of the coordinates was set at the center of the upper surface of the superconductor.
The permanent magnet was placed over the superconductor (Z coordinate),and fixed vertically to a PVC cantilever according to its magnetization direction (θ = 90º). The cantilever had 2 pairs of strain gauges to measure vertical forces at its extremes. This strain gauge configuration is not sensitive to the lateral and axial forces. The torques were neglected due to the size of the magnet. The PVC cantilever was joined to a 3D positioning table. The position of the magnet was then fixed in relation to the superconductor surface with a precision of 0.1 mm. The strain gauges were calibrated using a dynamometer and a set of 12 references forces. The calibration constant was established by least squares fitting in K = (3.87±0.14)×10-4 N/με, with a correlation coefficient of R2 = 0.997.
Figure 7.
Coordinate system of the PM-SC configuration. The superconductor is down and the permanent magnet is over it. Figure is not scaled to real sizes.
The measurement for every position was made in zero field cooling conditions (ZFC). The vertical forces were recorded where X = 0.0, 5.0, 10.0, 15.0, 17.5, 20.0, 22.5 and 25.0 ± 0.1 mm; at 3 different heights from the surface of the superconductor: 12.0, 10.0, and 8.0 ± 0.1 mm. Furthermore, measurements were taken in the center of the upper superconductor face, X = 0 mm from Z = 7.0 to 14.0 ± 0.1 mm. The Y position was always fixed at 0 mm.
These positions were chosen in order to avoid exceeding a limit of 3.5 mT of magnetic flux density at any point of the superconductor surface. Using this limit ensures the Meissner state is retained. Regardless of this, after every measurement the remanent magnetization of the superconductor bulk was checked and in most cases no measurable magnetization was found.
In order to compare the experimental and theoretical values, expression (16) was implemented in a finite element analysis program. The following figures (Fig 8-11) show the results.
Figure 8.
Z dependence of vertical force for X=0 mm.
The FEA errors are estimated to be less than 3 %.
The figures show positive agreement between experimental and theoretical values. Only Fig. 9 shows an appreciable difference for values approaching X = 20 mm. However, it must be pointed that the radius of the superconductor is 22.5 mm. It is only in these surroundings that a very low remanent magnetization was recorded, which indicates a non complete Meissner state. This explains why some experimental values were lower than those of a complete Meissner state.
Figure 9.
X dependence of vertical force for Z=8 mm.
Figure 10.
X dependence of vertical force for Z=10 mm.
Figure 11.
X dependence of vertical force for Z=12 mm.
4.2. Equilibrium angle measurement
In addition to previous experiments, the mechanical behavior of a magnet which has the ability to tilt over the superconductor in the Meissner state was also studied in this paper. In the present experiment only one degree of freedom was permitted in the tilt angle of the magnet (θ coordinate). The equilibrium angle of the permanent magnet over the cylindrical superconductor was measured for different relative positions. The results can be used to understand not only how the permanent magnet is repelled, but also how it turns when it is released over a superconductor.
A cylindrical permanent magnet (made of NdFeB with a coercivity of 875 kA/m and a remanence of 1.29 T) was placed over the superconductor. Their dimensions were 6.3 mm in diameter and 25.4 mm in length and it had a magnetization direction parallel to its axis of revolution. A rigid plastic circular rod was fixed in the center of mass, perpendicular to the axis of revolution. This rod was used as the shaft in a plastic bearing, which was lubricated with oil. The whole bearing system was joined to a 3D displacement table. This arrangement ensured it was possible to control the position of the permanent magnet with an accuracy of 0.1 mm, and the only permitted degree of freedom was the rotation around the Y axis. Concentric to the bearing, a graduate goniometer measured the angle of rotation of the magnet. The whole experiment design is shown in Fig 12.
Fig. 13 shows the comparison between the equilibrium angles measured and those calculated by expression (19).
Figure 13.
Comparative graph between experimental and FEA calculus of the equilibrium angle versus x position. Hight z was fixed at + 15 mm.
Again, there was a good agreement between the calculus made according to our model and the experiments. These experiments were carried out in Zero Field cooling condition (ZFC), and consequently there is no remanent magnetization.
5. Limits of application
The lower critical field, Hc1, is one of the typical parameters of type II superconductors, which has been experimentally being assessed from the magnetization changes from the Meissner state slope to the reversible mixed-state behavior (Poole, 2007). Hc1 is directly related to the free energy of a flux line and contains information on essential mixed-state parameters, such as the London penetration depth, λL, and the Ginzburg–Landau parameter, κ. Measurements of Hc1 and, of course, of the upper critical field, Hc2, therefore provide a complete characterization of the mixed-state parameters of the superconductor.
Differences between the predicted Meissner forces and the experimentally measured ones indicate that a part of the sample is in the mixed-state. Establishing with precision the instant when the differences begin will permit us to determine the Hc1 mechanically. Nevertheless, many other experimental techniques have been used to determine the state transition; most of them based on some kind of d.c. or a.c. magnetic measurement, but also on muon spin rotation (μSR) or magneto-optical techniques (Meilikhov & Shapiro, 1992). The basic problem of magnetization measurements introduced by flux pinning lies in the fact that the change of slope at the lower critical field is extremely small, since the first penetrating flux lines are immediately pinned and change the overall magnetization (M=m/V) only marginally. Elaborate schemes of subtracting the measured moments from an initial Meissner slope (Vandervoort et al., 1991; Webber et al., 1983) or experiments providing us directly the derivative of magnetization (Hahn & Weber, 1983; Wacenoysky et al., 1989; Weber et al., 1989) have been employed, SQUIDS have also been used to improve the precision of these kind of means (Böhmer et al., 2007).
The method also determines the zone at the sample where transition from Meissner to mixed state occurs.
For a position of the magnet with respect to the superconductor we define the Meissner Efficacy as
η=FexFME21
where Fex is the experimentally measured force and FM is the calculated force according with the Meissner model cited above. For a certain position of the magnet a Meissner Efficacy equal to one (η =1) proves that the superconductor is completely in the Meissner state and there is not any flux penetration. On the contrary, values lower than 1 indicate that a part of the superconductor has flux penetration and is in the mixed-state.
The measurement for every position was made in zero field cooling conditions (ZFC). The origin of coordinates was set at the center of the upper surface of the superconductor. The reference point of the magnet was placed in the center of the lower surface of the magnet. Therefore, the Z coordinate is the distance between the faces of the magnet and the superconductor. X is the distance of the center of the magnet to the axis of the superconductor cylinder (radial position). We have recorded the vertical forces for X = 0.0, 5.0, 10.0, 15.0, 17.5, 20.0, 22.5 and 25.0 ± 0.1 mm; at 4 different heights from the surface of the superconductor: 12.0, 10.0, 8.0 and 6.0 ± 0.1 mm.
Fig. 14 shows the Meissner Efficacy versus the maximum of the surface current density distribution Jsurf for different positions.
We observe that for low values of the maximum surface current density, the Meissner Efficacy is just 1.
From a certain value, the Meissner Efficacy decays linearly. From this data we can derive a weighted mean value of Jc1 surf\n\t\t\t\t= 6452 ± 353 A/m for a polycrystalline YBa2Cu3O7-x sample at 77 K.
In Table 1\n\t\t\t\tHc1 values from different authors are shown for comparison. The values are those obtained for the Hc1 parallel to c-axis in monocrystalline samples. Our value for a polycrystalline sample is of the same order of magnitude than the lowest monocrystalline values.
Figure 14.
Meissner Efficacy versus maximum Jsurf for different positions. The values obtained for X=5.0, 10.0, 15.0 mm radial positions are similar to those obtained for the X=0.0 mm values.
Now, if we use a value of λL=4500 Å, carried out from the literature (Geflbaux & Tazawa, 1998; Mayer & Schuster, 1993) we have a lower critical current density value of Jc1 = (1.43 ± 0.08) ×107A/cm2. By using Eq. 2 we calculate Hc1\n\t\t\t\t= 3226 ± 176 A/m.
C. Bömer et al (2007) (monocr.)
Umewaza et al (2007) (monocr.)
Kaiser et al (1991) (monocr.)
Wu et al (1990) (monocr.)
Mechanical method (polycr.)
Results for Hc1 (A/m)
2900 ± 250 6000 ± 2300
4500 ± 450
3580 11000
4950 15518
3226 ± 176
Table 1.
Comparison of the values found in different articles with that measured in this paper. The values and relative errors have been obtained directly from graphs, at 77 K. Available values for H (a,b) and H ‖ (c) in monocrystals are shown. H ‖ (c) is always greater than H (a,b)
The uncertainty in the determination of Jc1 surf may be reduced by increasing the number of series of measurements (or paths). Therefore, this is a method intrinsically more precise than other common methods.
In fact, the values far from the Meissner state contribute to improve the accuracy of the Jc1 surf determination. The determination of the slopes of straight lines has a propagation of errors more convenient than that in the case of the measurement of a change in the slope of the tangents to a curve. Other methods, therefore, would require high precision measurements to obtain a reasonable error for Hc1.
6. Example of application - permanent magnet over a superconducting torus
We calculate the torque exerted between a superconducting torus and a permanent magnet by using this model. We find that there is a flip effect on the stablest direction of the magnet depending on its position. This could be easily used as a digital detector for proximity.
We consider a full superconducting torus and a cylindrical permanent NdFeB magnet over the superconductor axis (Z axis). In figure 15 we can observe the geometrical configuration of both components. Every calculation is referenced with respect of a Cartesian coordinate system placed in the center of mass of the torus which Z axis is coincident with the axis of the torus.
Figure 15.
Permanent magnet over a toroidal superconductor set-up. The dimensions are: LPM - length of the cylindrical permanent magnet, ØPM – diameter of the cylindrical permanent magnet, RINT – Inner radius of the torus, ØSECTION – Diameter of the circular section of the torus. z is the vertical coordinate of the center of the magnet and θ is the angle between the axis of the magnet and the vertical Z axis.
The superconducting torus has an internal radius RINT = 6 mm and a diameter of the section ØSECTION = 10 mm. The cylindrical permanent magnet has a length LPM = 5 mm and a diameter ØPM = 5 mm. When calculating the magnetic field generated by the magnet we define its magnetic properties as: Coercive magnetic field HCOERCIVITY = 875 kA/m and remanent magnetic flux density BREMANENT = 1.18 T. We assume that the direction of magnetization of the permanent magnet coincides with its axis of revolution.
The variables θ and z are the coordinates we modify in order to analyze the mechanical behavior of the magnet over the superconductor. z is the distance along the Z axis between the center of mass of the torus and the one of the cylindrical permanent magnet. θ is the angle between the axis of the magnet and the vertical Z axis.
The equilibrium angle (θeq) as a function of z can be determined as follows. For a certain z we calculate the Y component of the torque (My) exerted on the magnet by the superconductor as a function of θ and we find the equilibrium angle as the value for which My(θeq)=0. The sign of the slope dMy/dθ at that point determines the stability or instability of the equilibrium point.
Figure 16.
My applied to the permanent magnet by the superconductor as a function of θ for z= 0, 3, 6, 9, 12 and 15 mm.
In figure 16 the torque (My) exerted on the magnet by the superconductor as a function of θ is shown for z = 0, 3, 6, 9, 12 and 15 mm. The maximum values for the torque exerted to the permanent magnet appear at θ = 45º and θ = 135º for every z. The remarkable fact is that the sign suddenly changes when moving from z = 3 mm to z = 6 mm. The equilibrium points are always at θ = 0º and θ = 90º, but θ = 0º is a stable equilibrium point for z = 0 mm and z = 3 mm, while it is unstable for the rest of the positions. On the other hand θ = 90º is unstable for z = 0 mm and z = 3 mm, but it is stable for the rest of the positions. That means that if you approach a magnet along the Z axis and it is able to rotate, it will be perpendicular to the Z axis while it is at z ≥ 6 mm, but it will suddenly rotate to be parallel to the Z axis when you pass from z = 6 to z ≤ 3.
In figure 17 the variation of the torque at θ = 45º as a function of z. The torque changes its sign between z =3 mm and z =4 mm.
Finally, figure 18 shows the stable equilibrium angle as a function of z. It is evident that, at a certain position between z =+ 3 and z =+ 4 mm we found that the stable equilibrium angle switches from a vertical orientation of the magnet to an horizontal one describing the flip effect claimed in this work.
Therefore, it can be concluded that if you approach a magnet along the Z axis and it is able to rotate, it will be perpendicular to the Z axis while it is at a certain distance (z ≥ 4 mm in our example) and it will change to be parallel to the Z axis for closer positions (z ≤ 3 mm in our example). As the equilibrium angle does not depend on the magnetic moment, the magnet can be much smaller. As a flip in the orientation of a permanent magnet can be easily instrumented, this effect can be easily used as a binary detector for proximity.
Figure 17.
Torque My exerted on the magnet for θ = 45º as a function of z.
Figure 18.
Stable equilibrium angle (θeq) as a function of z.
7. Conclusion
Magnet-superconductor forces both in Meissner and mixed states can be calculated with the accuracy required to engineer useful levitating devices.
The implementation of a local differential expression in a finite elements program opens new perspectives to the use of magnet-superconductor devices for engineering. This can be used to calculate forces whatever the size, shape and geometry of the system, for both permanent magnets and electromagnets.
Accuracy and convergence, in addition to the experimental verification for different cases have been tested. There is a good agreement between experimental results and calculation, even with very low-cost computing resources involved.
Moreover, the expression can be used to determine the point when the mixed state arises in a superconductor piece.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/16235.pdf",chapterXML:"https://mts.intechopen.com/source/xml/16235.xml",downloadPdfUrl:"/chapter/pdf-download/16235",previewPdfUrl:"/chapter/pdf-preview/16235",totalDownloads:3110,totalViews:160,totalCrossrefCites:0,totalDimensionsCites:4,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:62,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"October 15th 2010",dateReviewed:"February 11th 2011",datePrePublished:null,datePublished:"July 18th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/16235",risUrl:"/chapter/ris/16235",book:{id:"159",slug:"superconductivity-theory-and-applications"},signatures:"Jose Luis Perez-Diaz and Efren Diez-Jimenez",authors:[{id:"26353",title:"Prof.",name:"Jose Luis",middleName:null,surname:"Perez-Diaz",fullName:"Jose Luis Perez-Diaz",slug:"jose-luis-perez-diaz",email:"jlperez@ing.uc3m.es",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"128204",title:"Prof.",name:"Efren",middleName:null,surname:"Diez-Jimenez",fullName:"Efren Diez-Jimenez",slug:"efren-diez-jimenez",email:"ediez@ing.uc3m.es",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Carlos III University of Madrid",institutionURL:null,country:{name:"Spain"}}}],sections:[{id:"sec_1",title:"1. Introduction ",level:"1"},{id:"sec_2",title:"2. Magneto-mechanics of a superconductor in Meissner state",level:"1"},{id:"sec_2_2",title:"2.1. Force calculation",level:"2"},{id:"sec_3_2",title:"2.2. Torque calculation",level:"2"},{id:"sec_5",title:"3. Finite elements implementation",level:"1"},{id:"sec_5_2",title:"3.1. Results provide by the post-processing",level:"2"},{id:"sec_7",title:"4. Experimental verification",level:"1"},{id:"sec_7_2",title:"4.1. Force measurement",level:"2"},{id:"sec_8_2",title:"4.2. Equilibrium angle measurement",level:"2"},{id:"sec_10",title:"5. Limits of application",level:"1"},{id:"sec_11",title:"6. Example of application - permanent magnet over a superconducting torus",level:"1"},{id:"sec_12",title:"7. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'AlarioM. A.VicentJ. L.\n\t\t\t\t\t1991\n\t\t\t\t\tSuperconductividad, 1st ed. (EUDEMA, Madrid, 1991).'},{id:"B2",body:'ArkadievV. (1947). A floating magnet. 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Dpt. Ingeniería Mecánica – Universidad Carlos III de Madrid, Spain
Dpt. Ingeniería Mecánica – Universidad Carlos III de Madrid, Spain
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1. Introduction
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During a product development process, information becomes available to, and is requested by, many partners, design teams and organisations. Information about properties and performance of components and subsystems is the basis of decisions made in the development process. This information has many sources ranging from mathematical models, simulations, testing of physical models and prototypes and customer use data. It has many destinations, in the primary design phase and then through the product life in operations of maintenance, refit and redesign. Product Lifecycle Management (PLM) is primarily concerned to create product models to cover the full range of processes, operations and activities required to support a product through its lifecycle. A critical and current issue is the extent that these product models provide the basis for generating corresponding process models particularly dynamically so that process models continuously reflect the current state of the product models [1]. One aim is to enhance through improvements in workflow for planning product development processes, the significant gains that PLM systems have delivered over a period of 25 years in reducing both the duration and costs of product development [2]. Research by the authors [3, 4] has concentrated on the processes of testing and their ubiquity through product development. Critical testing processes such as field testing ([2], for example) are identified in these workflows, which deliver product development. However, the way that these testing processes form a critical part of all the processes from start to finish of the product lifecycle, whether as inputs, as drivers for iteration, for establishing alignment to regulations or for confirmation of completion of a satisfactory design, has received limited consideration in the literature. Tests are long and expensive activities and most product development activities and tasks depend on the results of test, whether, physical test, simulations or field data gathered during customer operations. This chapter examines methods to integrate testing more closely with other product development processes as well as to improve the planning of the processes of testing so that testing activities are scheduled optimally. Further, the chapter examines how the results of tests can be applied to assist other product development processes. Critically, it analyses how preliminary test results can be of significant assistance to these other processes, speeding their completion.
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Previous research by the authors has addressed two particular issues. First, combining information from both physical and virtual testing (simulation) can bring forward in time the availability of a workable product model suitable for the next design stage [3]. This helps planning a design process in an iterative cycle of proposal, test and redesign through developing a method to analyse the overlap between steps in this cycle and optimise this overlap to reduce overall development time. In particular, the long duration of some physical tests, which are necessary to ensure performance and conformance to regulations and standards, are a bottleneck in product development. Starting downstream design activities dependent on these tests before the tests are completed can ease this bottleneck. Essentially, the proposed method applies information from two distinct product models, simulation and physical test to change the process model, allowing significant overlap between activities. The method relies on observing the degree of convergence between simulation and test data.
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The second piece of research [4, 5] examines more closely how testing activities can be explicitly integrated into the product development process for complex engineering products. This research highlights the mismatch between several models of product development which tend to relegate testing to be an activity late in the design process or primarily concerned with quality issues. In fact, examination of practice shows that testing is integrated throughout. The misconception in product development process models has possibly arisen because the long duration of physical tests means that the results of testing are not available until later stages, although the activity itself necessarily starts early in the process. This research therefore points to a significant reappraisal of appropriate process models resulting from how data is available in product models. Both strands of previous research have focused on testing for design, rather than wider product development through lifecycle. However, they provide useful insights into the relationship between the product models of PLM [1, 2] and the process models [6] in product development.
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This chapter applies the results of this research to integrate testing and design more widely in the product lifecycle. Section 2 introduces some background and literature of PLM with particular reference to testing. Testing is considered from a general perspective as activities which analyse properties and performance of designs. A short review of existing research on the relationship between testing and PLM in Section 3 covers the mixes of testing activities at various stages of the product lifecycle based on some industry observations. Section 4 extends the proposition, first proposed by Tahera et al. [3] for testing and design, and reviews a three-way mix of testing types comprising simulation, use data (from embedded product monitoring) and physical testing. Further, wider implications of these methods are drawn in Section 5, especially in how PLM systems coordinate product models generated through design, testing and product monitoring activities. Section 6 discusses the tentative nature of these findings, the requirements for further research and the potential benefits for PLM systems. In particular, the refinements in process models recognise testing activities explicitly and their close integration with other processes in product development. Changes in process models drive changes to product models and PLM. This research does not cover the latter stages [as referred as End of life (EOL)] of product lifecycle.
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2. PLM data and descriptions
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Two observations are relevant when considering testing in product lifecycle. First, testing is a continuing activity, whether physical or virtual, throughout lifecycle. Testing data sets up maintenance schedules and product use data assists in updating these schedules. Periodic refit and redesign may emerge from testing new materials, components and subsystems to track upgrades and changes to customer requirements.
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Second, testing supplies information which becomes part of a product description. PLM systems handle several product descriptions [7, 8] and a major challenge is maintaining consistency and integrity of multiple descriptions In the simple case this might mean ensuring that changes to a design in one description, perhaps CAD geometry, are propagated accurately to descriptions for manufacture and assembly such as BoMs and tolerancing schemes. Results of testing update these multiple descriptions in PLM systems. As observed above, testing takes place continuously through product development and product use. However, the schedules for physical testing activities have long duration.
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Product performance data is gathered over a range of use conditions and longitudinally over time. Data of two types is relevant in testing. Special tests can be set to investigate particular characteristics such as thermal dynamics of an engine which formed one of the areas of previous research [3]. Other data is gathered from product monitoring in the field. Increasingly the latter data, which may include component wear, degradation in performance or replacement of components, for determining preventative maintenance or redesign of failing components, is well established for complex products such as aerospace [9]. However, quick and effective use of this testing data depends on levels of confidence when only partial data is available. A similar situation to testing in the initial stages [3] occurs throughout lifecycle, where reliable decisions on maintenance, retrofit and redesign when taken early can reduce operational product cost to customers as well as more speedily remove potential causes of failure.
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The broader challenge for PLM systems with their multiple decisions of different aspects of a product is two-fold. Figure 1 indicates these two broad challenges as updating performance and product descriptions iteratively. The first is to ensure that testing data updates performance and operational user descriptions consistently. The second occurs, when testing or use monitoring data prompts component or subsystem redesign. The underlying configurational product descriptions such as (Bill of materials) BoMs for manufacture and assembly will change accordingly, and updating these new product descriptions consistently is critical. With the focus of this chapter on the interplay of simulations, physical testing and monitoring data, it is noted that simulations depend on design descriptions and that inconsistent descriptions will reduce the accuracy of simulations leading to slower alignment between the results of simulations and the results from physical test. PLM has a design focused view in which the processes of product development effectively ‘call for’ testing and monitoring to validate and verify a design proposal. A critical circumstance in product lifecycle is the incorporation of new technologies in components and systems. Testing is often mandatory to meet regulations before new components can be fitted and operated, especially for complex products in the automotive and aerospace sectors which have a long service life. Conversely it is suggested here that a testing and use data view of PLM can drive design. It is argued that testing and design are equal partners in the product development process and promoting closer integration through PLM will give competitive advantage. This chapter explores how product development teams can reduce redesign iteration cycle time at several points in the product lifecycle. Incremental reductions in product cost and improvements in customer service at each cycle accumulate as the number of cycles increases yielding a significant benefits over the whole lifecycle.
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Figure 1.
Iterative updating of product and performance descriptions.
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Several pieces of research have examined how early availability of data from testing can reduce overall design duration [10, 11, 12]. However, these methods generally take an abstract perspective looking at optimising a given set of design and testing activities. However, they do not really question the assumed relationship between design (generally interventions in product such as design, maintenance or retrofit) and testing (generally derived data from analysis, simulation, physical test, or product use monitoring). Previous research [3] has examined the relationship between design and testing in their narrow senses and concluded that rebalancing leads to a more feasible and realistic model of process. Data from testing (in the wider sense throughout lifecycle) is expensive and time consuming to provide. How and when it is used is a critical part of process models. Conversely these descriptions of performance and functionality derived from testing require support from PLM alongside descriptions of product components, configurations and architectures derived from design activities [13].
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Dynamic process models have been identified as critical to delivering the benefits of PLM systems [1, 14]. Methods to construct evolving process models from PLM product models use Design Structure Matrices and workflow networks. This research theme presents a new framework for PLM systems so that they can support these evolving process models. Conversely, process models which highlight the balance (and integration) of design and testing (in their wider senses outlined above) assist in the construction of product models in PLM systems.
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Figure 2 presents a generic sequential process model [1] of the stages of a product’s lifecycle from identifying market needs to recycling. This also represents the overall information flows in product lifecycle.
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Figure 2.
Development process model (taken from [1]).
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Another view of information flow within product lifecycle is presented in Figure 3 adopted from [15], which consists of three main phases: beginning of life (BOL) includes idea generation, product development and production, middle of life (MOL), includes use, service and maintenance and end of life (EOL) comprises of reuse, recycle and disposal.
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Figure 3.
Levels of information flow achieved between the different product lifecycle phases [15].
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At every stage of the product lifecycle, information such as design specification, Computer Aided Design (CAD) drawings, Computer aided Engineering (CAE), physical test results and technical documents are generated [16]. These pieces of information are captured, stored, managed, and transferred between different people and application system during product lifecycle management. In general, PLM includes the planning, execution, control, and documentation of all processes in the product lifecycle [17]. Information flow from the BOL phase to other phases is managed through several information systems, such CAD tools, product data management (PDM) and knowledge management (KM). However, the information flow from/to MOL and EOL is not well supported or managed through current tools and information systems, therefore the critical information from these phases about product use data often do not adequately feedback to the BOL phase [17]. This may cause the decision- making process in the product lifecycle to be inaccurate and incomplete.
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Different descriptions make up a product model in PLM. These are created during stages of the product lifecycle to facilitate the next stage of the process. The descriptions of product, design and performance are particularly relevant for PLM management. The definitions for these descriptions vary and for this research the following definitions are adopted.
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Product description is the explicit result of a design process, which is the information for defining the product [18] usually in the form of drawings and CAD models. Design information is of two types. First, background information, such as the design requirements, design methods and design standards and second foreground information on the details of the product. The latter is the product description. Design description covers the information from which the product can be manufactured [19]. Performance description is the realistic system-level performance description. This can be based on several different physical models. For example, heterogeneous models covering mechanical, fluid and electronic dimensions are needed to describe the performance of complex products [20].
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3. Testing across product lifecycle: an industry example
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This section outlines testing and associated activities at various stages of the product lifecycle as observed in a major international company which designs and manufactures automotive diesel engines. First, the company-based product lifecycle management process model is described. Next, types and sequencing of testing in the product development process of the company is examined. The scope of testing is then broadened to include other aspects of PLM, especially maintenance and new generation product design.
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It is a UK based diesel engine design and manufacturing company, that offers a wide range of diesel and gas engines and power packages from 8.2 kW to 1886 kW and has the capacity to produce up to 800,000 units per year. There are product families with different power ranges to meet the requirements from different markets. Products also vary in families depending on the number of cylinders, aspiration and control mechanism. Figure 4 shows the series of engines in the company’s product range. Eighteen semi-structured interviews were carried out at the company premises from February 2011 to February 2014. Eight engineers including a senior engineer, a development engineer, a business manager, a verification and validation manager and a validation team leader were interviewed. The case studies involved a series of interviews ranging from 40 to 180 minutes in duration.
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Figure 4.
Company’s product range (taken from [4]).
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Figure 5 presents the view of the diesel engine company on their product lifecycle management process model. The top layer of the model shows key stages of the product lifecycle from the business strategy to the disposal of the product. The beginning, middle and end of life (BOL, MOL, EOL) classification, introduced in Section 2, is shown on the bottom layer. The middle layers show key activities that are undertaken during the stages of product development. Although, this chapter will only discuss the product design and development activities but it is important to highlight that development of the company’s product support starts in parallel with product design and development. Further, to aid support, the product is monitored by the company during its operation and up to its disposal.
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Figure 5.
Product lifecycle management process model.
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3.1. PLM process model
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The company’s product development process mainly comprises two wide-ranging processes; the New Technology Introduction (NTI) process and the New Product Introduction (NPI) process. The general research and development exercise occurs through New Technology Induction (NTI) process in their research and Development (R&D) department before the NPI process starts. Emission-related legislation is a key driver in technology development for this company. New technology, for instance, an after-treatment system that will reduce engine emission, would be developed in the NTI stage, and this system would be integrated with the engine through NPI process. This chapter focuses on the NPI process as most directly aligned with generic product development and PLM. However, it is noted that the background processes of NTI critical in product lifecycle especially as new technologies come on stream during life, enabling redesign and retrofitting of new components and subsystems.
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As shown in Figure 6, this NPI process in the company has seven stages starting from the identification of market needs to the review of a product’s performance in the field, i.e. “Requirements” to the “Review of Market Performance” (see Tahera [4] for further details of the company processes). Each stage leads to a formal gate review. Based on prescribed criteria, a product must pass through review at the gateways (GW1, GW2,…GW7) before the product development project proceeds to the next stage.
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Figure 6.
Stage-gate process for new product introduction in a company study.
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Testing and the key activities of design, computer aided design and engineering (CAE), and procurement of prototypes are considered in this study. The latter is a major activity since these need specialist design and manufacturing expertise, often involving new manufacturing processes, materials and technologies. A more detailed flow diagram of these stages is presented in Figure 6 to show the integration of the key activities.
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As the diesel engine is a mature product and design changes happen incrementally, engineers in the company start with an existing analysis of the previous generation of products. For a new product introduction (NPI) programme, product objectives are checked against a current product issues (CPI) database. The CPI database provides information about failure modes and effects of current products, which will need special attention for next generation products. This process is carried out by lead team members who are the technical specialists, component owners, design owners and the verification and validation managers.
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The NPI process starts in the requirement gathering phase, and should be finished before Gateway 1 (GW1), i.e. before the concept demonstration phase, however spread across the SD phase. Initially, the design alternatives are included in the analysis, because selection of a design is made based on the risk with that particular design and the associated time and cost of its validation program. All design options are considered during this phase. These help to analyse the trade-offs that can be made across different design options. If this analysis identifies high risks in design decisions, CAE analysis and design changes are undertaken until the risk is reduced to an acceptable level to proceed with the project. These CAE analyses typically fall into three main areas: structural analysis, mechanism or dynamic analysis and thermo-fluid-flow dynamics. They result in the determination of parameters like material properties, geometric idealisation, and physics, which help to define the scope of the design activity. When the overall risks are assessed, design verification and validation actions are decided and planned to mitigate risk. Verification and validation activities can range from design changes, further CAE analysis to testing.
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Ideally, most of the development related testing should start after the requirements have been identified i.e. after the Gateway 1 (GW1) and continue till the product is validated, i.e. until Gateway 5 (GW5), after which the engine is released to production. However, as depicted in Figure 6, these testing activities can spread further across subsequent stages of the process. At each stage, functional tests include the performance and emission (P&E) tests and mechanical tests for durability and reliability. Performance testing measures engines properties. For example, power and fuel consumption of an engine may be measured given a regulated fuel and air intake into the engine cylinder under steady state conditions of constant speed and load. While ensuring the performance, engines need to satisfy legislative conditions, for instance, the chemical constitution of the exhaust gases. The durability and reliability tests are conducted in peak harshness and tougher condition for a reasonably short period of time, called accelerated tests, forcing components or engine to fail/pass. For example, a gross thermal test procedure specifies the test cycle for determining the thermal fatigue resistance of core engine components. Typically, performance and emission related tests are performed before the mechanical durability and reliability testing.
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Testing occurs at different levels of the product. Component level testing happens primarily at suppliers of components, although the case study company also carries out testing to investigate areas of design concern. Engine level testing involves standalone engines on a test bed. Machine level testing involves engines mounted in a machine or vehicle to reproduce expected conditions of use. Figure 7 indicates how engine level and machine level testing are mainly conducted in parallel in the three consecutive stages, for different purposes in the product development and PLM. The stages are characterised by the type of testing activity. Stage 2 has Concept/System Demonstration (SD), stage 3 has Design Verification (DV), stage 4 has Product Validation (PV) and stages 5 and 6 focus on Certification.
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Figure 7.
Flow diagram of testing and associated activities in diesel engine design and manufacture (adapted from [4]).
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Concept/system demonstration (SD) testing is primarily to demonstrate ‘performance capability’. It shows that the technology can deliver the required performance. Alternative concepts are analysed and evaluated at this phase. A combination of old and new parts are built into an engine called a MULE. This MULE engine is tested to verify the performance of new parts.
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Design verification (DV) is primarily to develop optimal performance and validate hardware at the optimised performance. The aim is to ensure that design outputs meet the given requirements under different use conditions. At this stage, testing focuses on the verification of a chosen design, through detailed analysis and testing of stress, strength, heat transfer and thermodynamics etc. This stage validates the hardware prior to commitment to expensive production tooling.
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Product validation (PV) checks the effect of production variability on performance and any remaining hardware variation. This phase performs hardware testing which is limited to late design changes and emissions conformance testing. In this phase, detailed testing for reliability and durability occurs and the intended product is validated. The mandatory tests required for compliance usually occur during PV phases.
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Testing for certification happens in stages 5 and 6 before product is released to customers. Global emission regulations for diesel engine manufacturers provide requirements for the testing and evaluation of new components and new engine designs. It is an imperative for certification that the company follows the standard regulations during product development in terms of how a product needs to build and tested during validation for certification. For instance, to meet the in-use compliance, the company needs to demonstrate that the engine will meet specific levels of particulate emissions that will be detected and measured at the end of the useful life of the product.
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The case study company considers that testing of their product continues into use. As one senior engineer in the company remarked “in fact, real tests start when products are in use”. Their engines are equipped with a remote monitoring system that allows them to capture and collect field data. They have special user groups and they have established close relationship with consumers who help to collect more reliable data. The data consists of equipment characteristics identification data, usually the unique numbers, operating data (engine on/off and physical variables), event data (failure and maintenance history) and environmental and condition data. These field data are useful for several reasons:
to monitor: how a product is used by a specific customer groups to identify any inappropriate and misuse,
to monitor the current health of the product to plan and design their aftermarket service, i.e. repairs and maintenance services.
to feedback to the beginning of the lifecycle as the product health monitoring data are the key input for designing the next generation of the product. This enables a reliable specification for the design phase and the development of a product description.
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To help deliver these benefits the company creates two descriptions for monitoring and new product development in addition to the PLM descriptions mentioned in Section 2 for product, design and performance:
Current product governance—during product in operation/ field data to help new product development,
Product support development—during product development to help product monitoring.
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3.2. Current product governance: field data and new products
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In the diesel engine company, the ‘voice of the customer’ (VOC) is captured in many ways: directly, through discussions, interviews and workshops with customers, and indirectly through analysing customer specifications, warranty data, and field reports etc. and through dealers and distributor channels. Quality Functional Deployment (QFD) is applied to identify critical technical requirements of the design which will need verification and validation by testing.
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The company uses Failure Modes and Effects Analysis (FMEA) to evaluate a potential design for possible failures and to prevent them by proactively changing the design rather than reacting to adverse events after failures have occurred. This emphasis on prevention may reduce risk of failure in field. FMEA is particularly useful in evaluating a New Product Introduction programme prior to implementation as well as in assessing the impact of a proposed change to an existing design. More details about FMEA and steps of FMEA analysis can be found in [21]. FMEA is one of the most widespread methods used in determining priorities for technical risks in the PD process especially during the testing phase [22].
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To identify the potential effects, the company reviews documents, including historical data, warranty documents, field service data, and customers’ complaints. The company rates the severity of the effects of a failure mode. Any failure occurring in the field is considered as a high risk. Issues identified in use significantly drive next generation product development and testing procedures. The company continuously monitors and captures a product’s performance and durability when engines are used in a field. For a new product development, the company uses information from the ‘use in the field’ to assess how the product is performing and from the ‘use of the customer’ (how customers are using the product) to judge when a potential failure is likely to occur.
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Field data is particularly valuable as it consists of information about failures and repair actions that have been taken place under real operating conditions. This enables the acquisition of statistically significant reliability and repair data [23]. Issues in recording field incidents are addressed by Smith [23] particularly how reliance on people means that recording is subject to errors, omissions and misinterpretation.
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3.3. Product support development: design for maintainability
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Maintainability is characterised as the ease of retaining or restoring a product in effective use conditions by using specific procedures and resources [24]. It is an important factor in the economic success of an engineering system. “Design for maintainability requires an evaluation of the accessibility and reparability of the inherent systems and their related equipment in the event of failure, as well as of integrated systems shutdown during planned maintenance” [25]. Maintainability procedures and techniques not only avoid and fix failures they also consider how a system might fail. Three types of maintenance can be distinguished: breakdown maintenance (corrective maintenance), preventive maintenance, and predictive maintenance (condition-based maintenance).
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Condition monitoring and fault diagnosis techniques are used for predictive maintenance [26]. Product health monitoring is a research area that covers failure detection, current health assessment and remaining useful life prediction [26, 27]. According to Fu et al. [28], most failures do not occur instantaneously. There is degradation and associated symptoms before the actual failure. The main objective of the predictive maintenance is to reliably identify these degradation processes so that maintenance can be affected before the actual breakdown. Predictive maintenance is based on the product’s performance and condition monitoring data. For example, in well-established methods, vibration data is analysed to find the frequency responses to identify the type of fault present in the equipment [27].
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At the design and development design stage the main characteristics of a product are determined and product performance is evaluated. Therefore, design for maintainability should be considered during the product development. However, according to Coulibaly et al. [29], there is lack of an efficient tool for considering maintainability and serviceability at the early design. Also, there is limited research on how information from design, CAE and tests can support product maintenance.
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Kiritsis et al. [30] have commented that clear definition of the information for maintenance is required if appropriate and adequate information is to be collected. Usually, data collected during Middle of Life (MOL) phase of product is for maintenance management purposes and may not be appropriate for feeding back to the Beginning of Life (BOL) phase to redesign or improve a product. Although people involved in this process often have a clear understanding of the required information, it is not straightforward to define or determine exactly what information will be required.
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A baseline performance description would allow degradation over a period of use to be assessed. As mentioned before, advanced engineering products such as the diesel engines studied here are equipped with instruments such as sensors, meters, controllers, and computational devices and have the processing capacity to self-detect/ predict certain problems. Next section proposes a conceptual model to facilitate this process. Design and testing data from the EOL stages can be a useful reference point for comparing with monitoring data for predictive maintenance. Also, this model can help to clearly define the information required to be collected to comparison.
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4. Extending the proposition: testing data for predictive maintenance
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This section extends a method for managing the iteration of design and testing during the product development stage [3] to predictive maintenance during the product use phase. First, the previous work will be described briefly, then how the work can be extended for the purpose of the predictive maintenance will be explained.
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In an iterative design and testing process, testing results usually drive the subsequent re(design) activities. A control system analogy can be used to describe an iterative design and testing process. A control system monitors, compares and adjusts at a sequence of time points. A monitoring device makes a measurement, and reports it to the comparator, which compares it with the pre-determined desired value. A decision rule uses the result from the comparator to adjust an effector. Similarly, in a test, actual measurements of a parameter are taken and compared with pre-determined values identified in design analysis to identify if the design is satisfactory.
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During a lengthy durability test, for example in a “Deterioration Factor” test, intermediary test measurements are taken at a sequence of time points between start ts and finish tf (ts, t1, t2…tn…tf), as in Figure 7. Engineers know that the performance of an engine will change over the time and they allow an acceptable margin for each time point. This is illustrated in Figure 8 with a range of expected values specified by design and CAE prior to the test. Engineers will know how much they expect the product to deteriorate after say 200 or 500 hours of running the test. If the product deteriorates below an allowable limit, or margin, at that time, then it is deemed under-designed. If an engine performs above the margin then it is assumed to be over-designed. Therefore, if the engine produces any value under or above the expected values (including margins) then these deviations are not acceptable (see Figure 8) and indicate that redesign is required. ‘Deviation’ is the difference between the expected value of a parameter and an actual measurement of that parameter, at the time of an assessment (e.g. test).
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Figure 8.
A schematic of expected and measured value and associated deviations at different times during a test.
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Figure 9 shows a schematic, which presents a simplified case of Figure 8 in which the expected value is a single value rather than a range. In practice this might be the mean of the distribution of expected values and is represented as the upper straight line (in red). The lower line (in green) represents the measured values. A physical test starts at ts and finishes at tf. Since the design meets specification based on the best knowledge available at ts, (or rather there is no information to indicate that it does not) the red and green line meet at ts. During the testing process, test measurements are taken and the actual value of a parameter at any point is identified.
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Figure 9.
A simplified model of deviations between expected and measured values during a test.
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Deviation, at a time point, is identified as the difference between test measurements and expected value. The magnitude of the deviation is shown with a double-headed arrow in Figure 10 which depicts a case of under-design, with measured product performance gradually degrading and the deviation increasing monotonically. This considerable simplification is an assumption of the model developed here. The sloping line represents the evolution of test results over time, which tends to show increases in deviation of the design from expected performance. The deviation does not, in practice, decline linearly.
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Figure 10.
Comparison of CAE and test data with field data to identify product’s performance level.
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The difference between test measurements at different times, can reveal the ‘degree of evolution’ [12], i.e. how fast the deviation is changing in approach to the final value of the deviation at tf. Details can be found in [12].
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A similar proposition can be used for predictive maintenance. The design stage identifies the expected product performance in use, i.e. a range of expected values of a parameter can be specified by design, CAE and tests during the development stages. Product’s health measurements are taken at a sequence of time points between start ts and finish tf (ts, t1, t2 …..tn….tf), as in Figure 9. Using a similar approach as explained above, the “amount of deviation” and the ‘degree of evolution’ can identified.
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Once, these two factors are identified, i.e. how fast and how much within a time interval a product is degrading can be determined, an effective maintenance plan can be made.
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5. Implications of the proposed method in PLM
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The extension to include user and service data, combined with CAE simulations and test data to the latter stages of the product lifecycle in maintenance schedules and the refit of critical components is straightforward. As noted above the user and service data of previous products is used extensively in initial new product development, especially in FMEA and QFD processes. With a product currently in service, a history of user data will be accumulating. This serves several purposes. First to help specify and tune the maintenance schedules. Statistically significant data will be available from a large population of products about the behaviour, performance profiles and probabilities of failure of critical components in the product. This data is at the core of establishing service schedules and swop outs for potentially failing components.
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Second, failing components can be identified for redesign and refitting. The use conditions and the causes of failure may be clear from the data. The cycle of product development will be repeated with simulation, physical test, often necessary for regulation conformance, and redesign. The methods of Section 4 which allowed convergence of simulation and virtual test results with those of emerging physical tests will enable a quicker time to redesign and replacement of the failing component with corresponding significant improvements in product performance and customer satisfaction.
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Third, the emergence of new technologies at high technology readiness levels means that designs which were not feasible originally, because of the risks associated with low TRL can now be incorporated into the product. The purpose is to reduce costs, both to manufacturer and to consumer of the product. This process is frequently complicated by the dependence of the product developer on the processes of a specialist supplier. The advantages of the new and now mature technologies can be assessed against the use and service data. This will determine the benefits to all parties of the new technology and thus the business and engineering pressures on timescales. With an intense pressure on speed of product improvement through new technologies, there is considerable advantage in being able to overlap test and simulation of the performance of new technology. It is noted that as these processes continue further use data is continually made available. Using targeted use data in the mix of virtual and physical testing can assist in tuning the overlap, indeed there is the opportunity to install prototype new technology components in the current product and monitor use. This may help the convergence of simulation, test and use data.
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Fourth, and consequential on the third, are the benefits of retrofitting. With a new technology embedded in a redesigned component, the opportunity may arise for variants, tailored to a range of use conditions. Which variant to retrofit and the associated programme of retrofit integrated with new maintenance schedules will depend on (i) performance characteristics of the variants from test and simulation and (ii) the specialised use data to match variant to user. This integration of test and use data, can assist the optimal choice of variants.
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Across these processes for maintenance, refit and retrofit, the aggregated benefits of combining physical test, simulation and use data can be considerable. This can result in reducing time to introduction of revised maintenance schedules, to designing and fitting new technologies, as well as reduced costs to manufacturers and users. When all taken together the benefits to product lifecycle accumulate and make the argument for PLM systems to provide consistent and up to date information flows in supporting these processes.
\n
In extending the model of overlapping test and design, using convergence between data sources, to these processes in the product lifecycle several additional descriptions arise in the PLM product model. These are driven by the necessity to manage the revised processes of product lifecycle which arise from the new data and new information flows, particularly in use and service data.
\n
New process models and new product models develop hand in hand. This section has considered how product development and support through life cycle combines test, simulation and use data. Some general issues affecting PLM product models include how to compare this field data with simulation and test, the potential effects on information flows in the process models and the application of field data from one phase of product to the development process for next generation products, where fundamental analysis of the configuration and architecture of a product is undertaken over and above retrofitting new components and new technologies to the existing products.
\n
Comparing field data with physical test is not straight forward. Usually, the case study company uses the accelerated testing methods in which tests are conducted in peak harshness and tougher condition for a reasonably short period of time. Most of the accelerated testing is to verify that the product will perform reliably during the useful life, until it starts to wear out. Physical test results might not be readily useful for comparing with the field data as the use conditions could be different, load cycle and sensor loading location could be different, for instance, CAE analysis and virtual testing can play an important role in comparing these test and field data. CAE analysis can model and control these conditions and can focus on individual parameters. The information of CAE analysis can be disaggregated into cycles, for example. Parameters can be analysed individually if required to support decision making. Analysis of these three data, i.e. CAE analysis, physical test and field data could provide useful information for predictive maintenance, as to analysis why and how a product might fail. This may also help to record/capture field data in an appropriate manner to be used by the design engineers for the next generation of the product.
\n
The potential implications for PLM systems of the integration of design, test and field data in making information available in preliminary form to be used by PLM for dependent activities. This effectively overlaps activities previously linearly sequenced and reduces times and costs for customers and suppliers. However, such integration comes with a significant overhead. Increased numbers of cycles of revisions to the PLM descriptions is entailed as some preliminary information although sufficient to start subsequent activities may not be enough to finish them especially when on-site assurance and regulatory conformation are necessary before customer use.
\n
\n
\n
6. Discussion and further research
\n
PLM systems assemble and manipulate product descriptions, maintaining a product model. These descriptions come from many sources in the product development process including design, simulation, test and field data. To some extent the timely availability of descriptions is dependent on the process model used to organise and manage tasks. This chapter has addressed this issue through examining how a change to process models through integrating activities has an impact on PLM descriptions.
\n
The main argument of this chapter is delineating further the relationship between the product models of PLM and the process models for planning product development. Karniel and Reich [1] make the case that product models of PLM, updated throughout product development, have the potential to drive the planning of adaptable and dynamic processes for product development. Along with other research (e.g. [14]), they develop methods and algorithms to derive dynamic process models from the updating product models of PLM. This view gives, in a sense, a priority to the product models of PLM. The ‘new paradigm’ of Karniel and Reich [1] provides a critical role for PLM in planning dynamic processes. Updated product models in PLM are used to update process models. Although, in many industry contexts, available information in PLM and other information systems is necessary for the management and organisation of the dynamic processes of product development, which are by nature contingent and dependent, it is not sufficient. There are imperatives and opportunities in managing processes can drive the modes and forms of information available to PLM.
\n
This chapter has examined one aspect of this mutual dependency between product and process models. Making changes to process models through increasing the integration of test, simulation and acquiring field data, changes the requirements for product models and associated PLM systems. This research adds to the understanding of ways that process models drive the types of PLM systems necessary to support them. It complements the extensive body of research on the how PLM systems can drive dynamic and adaptable process models for product development. Considerable further research is required both in theoretical methods and in industry cases to optimise the costly and time consuming processes of testing, simulation and field data collection as well as integrating them with PLM systems.
\n
\n\n',keywords:"product development process, testing, computer aided engineering (CAE) analysis, design for maintenance, product description",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/63080.pdf",chapterXML:"https://mts.intechopen.com/source/xml/63080.xml",downloadPdfUrl:"/chapter/pdf-download/63080",previewPdfUrl:"/chapter/pdf-preview/63080",totalDownloads:1376,totalViews:390,totalCrossrefCites:1,dateSubmitted:"March 1st 2018",dateReviewed:"July 18th 2018",datePrePublished:"November 5th 2018",datePublished:"November 21st 2018",dateFinished:"August 14th 2018",readingETA:"0",abstract:"The product lifecycle management (PLM) is the process of managing the entire lifecycle of a product from the idea generation, through the design, development and manufacturing to service and disposal of the product. Testing often is considered to be an activity to perform during the product design and development phase. However, the information about how a product is designed and tested is useful for designing the maintenance and the monitoring and maintenance data can provide useful information in developing the next generation of a new product. The main objective of this chapter is to understand how testing process in integrated into the product lifecycle. This chapter reports a case study in a UK based manufacturing company and based on that develops a framework to highlight the importance of testing. Also, proposes a conceptual model of how testing activities can be managed in the product lifecycle management process.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/63080",risUrl:"/chapter/ris/63080",signatures:"Khadija Tahera and Christopher Earl",book:{id:"7489",type:"book",title:"Product Lifecycle Management",subtitle:"Terminology and Applications",fullTitle:"Product Lifecycle Management - Terminology and Applications",slug:"product-lifecycle-management-terminology-and-applications",publishedDate:"November 21st 2018",bookSignature:"Razvan Udroiu and Paul Bere",coverURL:"https://cdn.intechopen.com/books/images_new/7489.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78984-543-3",printIsbn:"978-1-78984-542-6",pdfIsbn:"978-1-83881-829-6",isAvailableForWebshopOrdering:!0,editors:[{id:"13146",title:"Prof.",name:"Razvan",middleName:null,surname:"Udroiu",slug:"razvan-udroiu",fullName:"Razvan Udroiu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"248549",title:"Dr.",name:"Khadija",middleName:null,surname:"Tahera",fullName:"Khadija Tahera",slug:"khadija-tahera",email:"k.tahera@hud.ac.uk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"266187",title:"Prof.",name:"Christopher",middleName:null,surname:"Earl",fullName:"Christopher Earl",slug:"christopher-earl",email:"c.f.earl@open.ac.uk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"The Open University",institutionURL:null,country:{name:"United Kingdom"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. PLM data and descriptions",level:"1"},{id:"sec_3",title:"3. Testing across product lifecycle: an industry example",level:"1"},{id:"sec_3_2",title:"3.1. PLM process model",level:"2"},{id:"sec_4_2",title:"3.2. Current product governance: field data and new products",level:"2"},{id:"sec_5_2",title:"3.3. Product support development: design for maintainability",level:"2"},{id:"sec_7",title:"4. Extending the proposition: testing data for predictive maintenance",level:"1"},{id:"sec_8",title:"5. Implications of the proposed method in PLM",level:"1"},{id:"sec_9",title:"6. Discussion and further research",level:"1"}],chapterReferences:[{id:"B1",body:'Karniel A, Reich Y. Managing the Dynamics of New Product Development Processes: A New Product Lifecycle Management Paradigm. Springer Science & Business Media; 2011, Jul 28\n'},{id:"B2",body:'Stark J. Product lifecycle management. In: Product Lifecycle Management. Vol. 2. Cham: Springer; 2016:1-35\n'},{id:"B3",body:'Tahera K, Earl C, Eckert C. A method for improving overlapping of testing and design. IEEE Transactions on Engineering Management. 2017;64(2):179-192\n'},{id:"B4",body:'Tahera K. The role of testing in engineering product development processes [PhD Engineering and Innovation]. Milton Keynes: The Open University; 2014\n'},{id:"B5",body:'Tahera K, Wynn DC, Earl C, et al. Research in Engineering Design. 2018. https://doi.org/10.1007/s00163-018-0295-6\n\n'},{id:"B6",body:'Wynn DC, Clarkson PJ. Process models in design and development. Research in Engineering Design. 2018;29(2):161-202\n'},{id:"B7",body:'Srinivasan V. An integration framework for product lifecycle management. Computer-Aided Design. 2011;43(5):464-478\n'},{id:"B8",body:'Takata S et al. Maintenance: Changing role in life cycle management. CIRP Annals-Manufacturing Technology. 2004;53(2):643-655\n'},{id:"B9",body:'Daily J, Peterson J. Predictive maintenance: How big data analysis can improve maintenance. In: Supply Chain Integration Challenges in Commercial Aerospace. Cham: Springer; 2017:267-278\n'},{id:"B10",body:'Shabi J, Reich Y, Diamant R. Planning the verification, validation, and testing process: A case study demonstrating a decision support model. Journal of Engineering Design. 2017;28(3):171-204\n'},{id:"B11",body:'Engel A. Verification, Validation, and Testing of Engineered Systems. John Wiley & Sons; 2010;73\n'},{id:"B12",body:'Krishnan V, Eppinger SD, Whitney DE. A model-based framework to overlap product development activities. Management Science. 1997;43(4):437-451\n'},{id:"B13",body:'Moullec M-L et al. Toward system architecture generation and performances assessment under uncertainty using Bayesian networks. Journal of Mechanical Design. 2013;135(4):041002\n'},{id:"B14",body:'Lévárdy V, Browning TR. An adaptive process model to support product development project management. IEEE Transactions on Engineering Management. 2009;56(4):600-620\n'},{id:"B15",body:'Bufardi A, Kiritsis D, Xirouchakis P. Generation of design knowledge from product life cycle data. In: Methods and Tools for Effective Knowledge Life-Cycle-Management. Berlin, Heidelberg: Springer; 2008:375-389\n'},{id:"B16",body:'Li J, Tao F, Cheng Y, Zhao L. Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology. 2015;81(1-4):667-684\n'},{id:"B17",body:'Jun H-B, Kiritsis D, Xirouchakis P. Research issues on closed-loop PLM. Computers in Industry. 2007;58(8-9):855-868\n'},{id:"B18",body:'Kimura F, Suzuki H. Representing background information for product description to support product development process. CIRP Annals-Manufacturing Technology. 1995;44(1):113-116\n'},{id:"B19",body:'Howard TJ, Culley SJ, Dekoninck E. Describing the creative design process by the integration of engineering design and cognitive psychology literature. Design Studies. 2008;29(2):160-180\n'},{id:"B20",body:'Van der Auweraer H, Anthonis J, De Bruyne S, Leuridan J. Virtual engineering at work: The challenges for designing mechatronic products. Engineering with Computers. 2013;29(3):389-408\n'},{id:"B21",body:'Stamatis DH. Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press; 2003\n'},{id:"B22",body:'Segismundo A, Augusto Cauchick Miguel P. Failure mode and effects analysis (FMEA) in the context of risk management in new product development: A case study in an automotive company. International Journal of Quality & Reliability Management. 2008;25(9):899-912\n'},{id:"B23",body:'Smith DJ. Reliability, Maintainability and Risk: Practical Methods for Engineers. Butterworth-Heinemann; 2017\n'},{id:"B24",body:'Stapelberg RF. Availability and maintainability in engineering design. Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design. 2009:295-527\n'},{id:"B25",body:'Stapelberg RF. Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design. Springer Science & Business Media; 2009, Feb 17\n'},{id:"B26",body:'Lu B, Li Y, Wu X, Yang Z. A review of recent advances in wind turbine condition monitoring and fault diagnosis. In: Power Electronics and Machines in Wind Applications, 2009. PEMWA: IEEE; 2009:1-7\n'},{id:"B27",body:'Choudhary AK, Harding JA, Tiwari MK. Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing. 2008;20(5):501\n'},{id:"B28",body:'Fu C, Ye L, Liu Y, Yu R, Iung B, Cheng Y, Zeng Y. Predictive maintenance in intelligent-control-maintenance-management system for hydroelectric generating unit. IEEE transactions on energy conversion. 2004;19:179-186\n'},{id:"B29",body:'Coulibaly A, Houssin R, Mutel B. Maintainability and safety indicators at design stage for mechanical products. Computers in industry. 2008;59:438-449\n'},{id:"B30",body:'Kiritsis D, Bufardi A, Xirouchakis P. Research issues on product lifecycle management and information tracking using smart embedded systems. Advanced Engineering Informatics. 2003;17:189-202\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Khadija Tahera",address:"k.tahera@hud.ac.uk",affiliation:'
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Open Access publishing helps remove barriers and allows everyone to access valuable information, but article and book processing charges also exclude talented authors and editors who can’t afford to pay. The goal of our Women in Science program is to charge zero APCs, so none of our authors or editors have to pay for publication.
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All of our IntechOpen sponsors are in good company! The research in past IntechOpen books and chapters have been funded by:
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The different types of granitic rocks, their compositional characteristics, tectonic settings and magma sources are outlined. Mineralogical classification of granites includes four rock types: tonalites, granodiorites, granite (monzogranite and syenogranites) and alkali-feldspar granites. Alphabetical classification subdivided granites into: I-type, S-type, A-type and M-type granites. Moreover, formation of granitic magmas requires distinctive geodynamic settings such as: volcanic arc granite (Cordilleran); collision-related granites (leucogranites); intra-plate and ocean ridge granites. The Eastern Desert of Egypt (ED) forms the northern part of Nubian Shield. Both older and younger granites are widely exposed in the ED. Old granites (OG) comprise tonalites and granodiorites of syn- to late-orogenic granitoid assemblages. They are calcalkaline, I-type, metaluminous and display island arc tectonic setting. Younger granites (YG) on the other hand, include granites, alkali-feldspar granites and minor granodiorites. They are of I- and A-type granites and of post-orogenic to anorogenic tectonic settings. The majority of the YG are alkaline, A-type granite and of within-plate tectonic setting (WPG). The A-type granites are subdivided into: A2-type postorogenic granites and A1-type anorogenic granites. Granite magma genesis involves: (a) fractional crystallization of mafic mantle-derived magmas; (b) anatexis or assimilation of old, upper crustal rocks (c) re - melting of juvenile mafic mantle – derived rocks underplating the continental crust. Generally, older I-type granitoids were interpreted to result from melting of mafic crust and dated at approximately 760–650 Ma, whereas younger granites suggested to be formed as a result of partial melting of a juvenile Neoproterozoic mantle source. Moreover, they formed from anatectic melts of various crustal sources that emplaced between 600 and 475 Ma.",book:{id:"9879",slug:"geochemistry",title:"Geochemistry",fullTitle:"Geochemistry"},signatures:"Gaafar A. El Bahariya",authors:[{id:"267666",title:"Dr.",name:"Gaafar",middleName:null,surname:"El Bahariya",slug:"gaafar-el-bahariya",fullName:"Gaafar El Bahariya"}]},{id:"59828",title:"Evolution of Drainage in Response to Brittle - Ductile Dynamics and Surface Processes in Kachchh Rift Basin, Western India",slug:"evolution-of-drainage-in-response-to-brittle-ductile-dynamics-and-surface-processes-in-kachchh-rift-",totalDownloads:1563,totalCrossrefCites:7,totalDimensionsCites:22,abstract:"The eastern part of Kachchh Rift basin was reactivated after 2001 Bhuj earthquake of Mw 7.7 and continuous seismicity has been recorded since then. The northern part of Wagad upland also experienced moderate earthquakes Mw ≥ 5.7 in February 2006 and March 2007. 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Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:36,paginationItems:[{id:"82195",title:"Endoplasmic Reticulum: A Hub in Lipid Homeostasis",doi:"10.5772/intechopen.105450",signatures:"Raúl Ventura and María Isabel Hernández-Alvarez",slug:"endoplasmic-reticulum-a-hub-in-lipid-homeostasis",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"82409",title:"Purinergic Signaling in Covid-19 Disease",doi:"10.5772/intechopen.105008",signatures:"Hailian Shen",slug:"purinergic-signaling-in-covid-19-disease",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82374",title:"The Potential of the Purinergic System as a Therapeutic Target of Natural Compounds in Cutaneous Melanoma",doi:"10.5772/intechopen.105457",signatures:"Gilnei Bruno da Silva, Daiane Manica, Marcelo Moreno and Margarete Dulce Bagatini",slug:"the-potential-of-the-purinergic-system-as-a-therapeutic-target-of-natural-compounds-in-cutaneous-mel",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82103",title:"The Role of Endoplasmic Reticulum Stress and Its Regulation in the Progression of Neurological and Infectious Diseases",doi:"10.5772/intechopen.105543",signatures:"Mary Dover, Michael Kishek, Miranda Eddins, Naneeta Desar, Ketema Paul and Milan Fiala",slug:"the-role-of-endoplasmic-reticulum-stress-and-its-regulation-in-the-progression-of-neurological-and-i",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}}]},overviewPagePublishedBooks:{paginationCount:32,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. 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She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. 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Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry"}}},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"bookSubject",path:"/subjects/836",hash:"",query:{},params:{id:"836"},fullPath:"/subjects/836",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()