Types of forging and its features [1].
\r\n\t- Traditionally accepted topics related to global health security,
\r\n\t- The impact of human activities and climate change on “planetary health”,
\r\n\t- The impact of global demographic changes and the emergence chronic health conditions as international health security threats.
\r\n\t- A theme dedicated to the COVID-19 Pandemic,
\r\n\t- Novel considerations, including the impact of social media and more recent technological developments on international health security.
\r\n\tThe goal of this book cycle is to provide a comprehensive compendium that will be able to stand on its own as an authoritative source of information on international health security.
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A member of multiple editorial boards and co-author of over 550 publications.",coeditorOneBiosketch:"An Associate Professor of Surgery & Integrative Medicine at Northeast Ohio Medical University and Cardiothoracic Surgeon at the Summa Health Care System. A prolific writer and presenter, with multiple books, hundreds of peer-reviewed articles, and innumerable presentations around the world.",coeditorTwoBiosketch:"A CEO of the INDUSEM Health and Medicine Collaborative, Global Executive Director. of the American College of Academic International Medicine (ACAIM) and head of the World Academic Council of Emergency Medicine.",coeditorThreeBiosketch:"A Director of Research in the Department of Emergency Medicine at Nazareth Hospital in Philadelphia, USA, and co-chief editor of the International Journal of Critical Illness and Injury Science. A recipient of numerous local, regional, and national awards.",coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki",profilePictureURL:"https://mts.intechopen.com/storage/users/181694/images/system/181694.jpeg",biography:"Stanislaw P. Stawicki, MD, MBA, FACS, FAIM, is Chair of the Department of Research of Innovation, St. Luke's University Health Network, Bethlehem, Pennsylvania, and Professor of Surgery at Temple University School of Medicine. Dr. Stawicki has edited numerous books and book series on the topics of clinical research, medical education, medical leadership, patient safety, health security, and various other subjects. He is a member of multiple editorial boards and has co-authored more than 650 publications. 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For many of those problems, standard techniques, as signal processing technique, shape recognition, system control theory, artificial intelligence etc., have shown as inadequate. Neural networks are a way to solve these problems in a way they are solved in human brain. Same as the human brain, neural networks are able to learn from given data, and afterwards, when they meet the same or similar data they may give the same or approximate result.
\n\t\t\tThere are several types of transfer functions: sigmoid, logistic sigmoid, linear, semilinear, threshold, Gauss\' function. Figure 1 shows the graph for one of most used transfer functions:
\n\t\t\tLogistic sigmoid function.
Multilayer neural network with signal propagation forward is very often used architecture (Bourlard, H at all, 2002). In it, signals are propagating only forward, and neurons are organized in layers. Most important properties of multilayered networks with propagation forward are given in these two theorems:
\n\t\t\tMultilayered network with single hidden layer may uniformly approximate any real continual function with arbitrary precision at the final real axis.
Multilayered network with two hidden layers may uniformly approximate any real continuous function at the final real axis.
Input layer receives data from environment. Hidden layer receives data from previous layer (in this case, outputs from input layer) and gives output depending from sum of input weights. For more complex problems, sometimes it is necessary to have more than one hidden layer. Output layer computes neural network outputs from sum of weights and transfer function.
\n\t\t\tH.263 is an international standard for video stream compression, widely used in telecommunication systems (ITU, 1995). There are several additions by ITU-T recommendation h.263, aimed at broadening of supported picture formats and video stream compression quality (ITU, 1996).
\n\t\t\tEnhancement of h.263 standard, presented in this paper, is related to application of artificial neural network (ANN) instead of standard DCT code, for sequences full of quick motion details.
\n\t\t\tIn section 2, a short description of h.263 standard is given. Section 3 describes training code for neural network used. Section 4 describes a way in which ANN is applied as an addition to existing h.263 standard. In section 5, results of experiments showing effects of this approach at quality and compression level of test sequence are presented.
\n\t\tCompression of a video signal is the key component in modern telecommunication services, as videotelephony and video conferences, in modern digital TV systems with normal and high resolution, and in numerous multimedia services. The reason is that – without compression – digital video signal consists of huge amount of data. Another problem in multimedia systems is a speed of reading and transferring data from compact disc to computer memory, and in fastest systems, it is up to 4 Mb/s. Having in mind that coding of a video signal is a topic of research for more than two decades, a large number of algorithms had been developed, implemented and tested on existing communication channels. In order to enable connection of equipment form different manufacturers, several international companies defined standards for compression and transfer of video signal. Best known are H.261 and H.263 for transfer of videoconferences and videophony, as well as MPEG standards (MPEG-1, MPEG-2 and MPEG-4) intended for standardization of multimedia systems and digital television (Schäfer, R., T. Sikora, 1995 ). Three-dimensional (3D) compression of a video signal is a generalization of two-dimensional video signal compression principle. Most frequent way to realize 3D compression of a video signal is the 3D transformation encoding based on DCT. For application of this method, video signal is divided in blocks with dimensions MNP, where M, N and P, respectively, are the horizontal, vertical and time dimensions of a block (Boncelet C. 2005). On every block 3D DCT is applied, and obtained DCT coefficients are being quantumized. As in 2D DCT, only coefficients with very small index values have significant values (Roese, J.A., at all 1997). In H.261 standard, two picture formats are defined (Markoski, B. & Đ. Babić, 2007). Therefore, for transmission of both formats of video signal by ISDN channels, it is necessary to achieve considerable level of compression (typically about 100 times). Since QCIF format is mostly intended for videophony applications, where mostly only a face of the other person is visible, frame frequency is usually decreased to 10 frames/s. H.261 standard defines algorithms for eliminating redundancy, quantumization algorithms, structure of coders and decoders, as well as data structure (Rijkse, K, 1995). It is interesting that standard does not demand using a certain algorithm for movement estimation, but it is important only to determine and transmit block movement vectors. A mechanism of regulation of bit-stream is also not demanded, but it is determined by choosing the way of processing and a way of deciding whether a block is being transmitted or not. In practice, implementation known as Referent model 8 (COST211bis/SIM89/37, 1989) is used most frequently, and it was used in standards testing.
\n\t\t\tH.263 standard is intended for standardization of picture transmission by standard telephone commutated lines wit bit-stream under 64 Kb/s, which was not covered by any standard (Rijkse, K., 1995, Girod, B, at all, 1996). It was produced by modifications of existing H.261 standard. Due to very tight deadlines in preparing the standard, original text of standard (Rijkse, K., 1995), defines only most necessary improvements of H.261 standard, but a possibility for further improvements is left open.
\n\t\t\tThe basic difference between H.261 and H.263 standards is in target bit-stream (A.Amer, E. Duboius, 2005). H.261 was supposed to be used for picture transmission over 64 Kb/s, while H.263 was supposed to be used under 64 Kb/s, most often in 22 Kb/s. In order to realize this goal, four small improvements were done to algorithms prescribed by H.261 standard. Although no one of those, per se, contributes much to total performances, all four together improve performances considerably (LeGall, D.J 1992).
\n\t\t\tH.263 recommendation is defined by International telecommunications society - telecommunication standard section (ITU-T, 1996). This recommendation standardizes a video stream compression process, defining syntax of compressed data format Compression is necessary in order to translate a conventional video stream into a shape available to computer applications under present limitations. H.263 uses compression code basically similar to JPEG (Joint Photographic Experts Group) and to MPEG (Motion Picture Experts Group codes) (ITU-T, 1995). Video stream is being compressed by a transformation sequence of every single picture.
\n\t\t\tH.263 video stream is organized in several layers, as shown in Figure 2. The highest layer, picture layer, defines basic properties of the video stream as picture size and coding system. Next layer is a group of blocks layer, enabling unique interpretation of spatially close blocks. Two lowest layers are macroblock layer and block layer, representing code interpretation of a picture. Every picture within video sequence is coded in one of three possible ways of coding: intra (I), inter (P) or bidirection (PB) coding. I-pictures are coded similar as in JPEG standard. P-pictures are envisaged on the basis of previously coded picture, and PB-pictures are envisaged on the basis of blocks from previous and next picture. Coding of every picture consists from its partition into macroblocks and special coding for every one of those. Every macroblock presents a 16x16-pixel zone and is a basic unit for motion compensation. Macroblock consists from 6 blocks: 4 luminent and 2 chrominent blocks. These blocks (8x8 pixels) are basic units for DCT (Discrete cosine transform).
\n\t\t\tMotion compensation is being done in order to remove time sameness between adjacent pictures in a video sequence. In this way, instead of complete picture, only information on detected changes and a way of their movement (move vectors) are transmitted. To avoid error accumulation, together with move vectors an error signal is coded, which is a difference between reconstructed and actual picture. The DCT transformation is being done to thus obtained error of move estimation. DCT transformation is being done on 8x8-pixel blocks, resulting in 64 transformation coefficients. The block energy is, after transformation, concentrated in few coefficients, corresponding to low-frequency part of range. Therefore, quantization of these coefficients is possible with relatively small error. Most of DCT coefficients are equalized with zero, which lowers information quantity needed for picture reconstruction.
\n\t\t\tStructure of H.263 video stream.
At the end of coding process, obtained information is statistically coded (Huffman and run-length coding) and written in format defined by h.263 syntax of video stream.
\n\t\tThe discipline we know today as neural networks originated as a result of fusing several quite different ways of research: signal processing, neurobiology and physics (Haykin S, 1994). Neural networks are a typical example of an interdisciplinary discipline (L. Faulsett 1995). On the one hand, this is an attempt to understand workings of a human brain, and on the other to apply the newly acquired knowledge in processing complex information (Lippmann, R. P. 1987). There are other progressive, non-algorithmic systems, as learning algorithms, genetic algorithms, adaptive memory, associative memory, fuzzy logic. General opinion is that neural networks are presently most mature and most applicable technology (Barsterretxea, att all, 2002).
\n\t\t\tConventional computers work on logic basis, deterministically, sequentially or wit a very low level of parallelism. Software written for such computers must be almost perfect in order to work appropriately. This requires long and costly designing and testing process.
\n\t\t\tNeural networks belong to parallel asynchronous distributed processing category. The network is tolerant on damages or falling out of function for a relatively low number of neurons. The network is also tolerant to presence of noise in input signal. Every memory element is delocalized - situated in network as a whole and it is impossible to identify in which part it is stored. Classic addressing is nonexistent, since memory is approached using contents, and not the address (S.P. \n\t\t\t\t\tTeeuwsen, at all. 2003\n\t\t\t\t).
\n\t\t\tBasic component of neural network is a neuron, as shown in figure 3:
\n\t\t\tBasic component of neural network.
Dendrites are inputs into neuron. Natural neurons have even hundreds of inputs. Point where dendrites are touching the neuron is called a synapse. Synapse is characterized by effectiveness, called synaptic weight. Neuron output is formed in a following way: signals on dendrites are multiplied by corresponding synaptic weights, results are added and if they exceed threshold level on the result is applied a transfer function of neuron, which is marked f on a figure. Only limitation of transfer function is that it must be limited and non-decreasing. Neuron output is routed to axon, which by its branches transfers result to dendrites. In this way, output from one layer of network is transferred to the next one.
\n\t\t\tIn neural networks, three types of transfer functions are presently being used:
\n\t\t\tjumping
logical with threshold
sigmoid
All three types are shown in figure 4:
\n\t\t\tThree types of transfer functions.
The neural network has unique multiprocessing architecture and without much modification, it surpasses one or even two processors of von Neumann architecture characterized by serial of sequential information processing (S.P. \n\t\t\t\t\tTeeuwsen at all, 2003\n\t\t\t\t). It has ability to explain every functional dependence and to expose a nature of such dependence with no need to external incentives, demands for building a model or its change. In short, neural network may be considered as a black box capable of predicting output pattern or a signal after recognizing given input pattern. Once trained, it may recognize similarities when a new input signal is given, which results in predicted output signal. There are two categories of neural networks: artificial and biological ones. Artificial neural networks are in structure, function and in information processing similar to biological ones. In computer sciences, neural network is an intertwined network of elements that processes data. One of more important characteristics of neural networks is their capability to learn from limited set of examples. The neural network is a system comprised of several simple processors (units, neurons), and every one of them gas its local memory where it stores processed data. These units are connected by communication channels (connections). Data exchanged by these channels are usually numerical ones. Units are processing only their local data and inputs obtained directly through connection. Limitations of local operators may be removed during training. A large number of neural networks created as models of biological neural networks. Historically speaking, inspiration for development of neural networks was in desire to construct an artificial system capable of refined, maybe even "intelligent" computations in a way similar to that in human brain. Potentially, neural networks are offering us a possibility to understand functioning of human brain. Artificial neural networks are a collection of mathematical models that simulate some of observed capabilities in biological neural systems and has similarities to adaptable biological learning. They are made of large number of interconnected neurons (processing elements) which are, similarly to biological neurons, connected by their connections comprising of permeability (weight) coefficients, whose role is similar to synapses. Most of neural networks have some kind of rule for "training", which adjusts coefficients of inter-neural connections based on input data (Cao J, at all 2003). Large potential of neural networks lays in possibility of parallel data processing, to compute components independent from each other. Neural networks are systems made of several simple elements (neurons) that process data parallely.
\n\t\t\tThere are numerous problems in science and engineering that demand extracting useful information from certain content. For many of those problems, standard techniques as signal processing, shape recognition, system control, artificial intelligence and so on, are not adequate. Neural networks are an attempt to solve these problems in a similar way as in human brain. Like human brain, neural networks are able to learn from given data; later, when they encounter the same or similar data, they are able to give correct or approximate result.
\n\t\t\tArtificial neuron, based on sum input and transfer function, computes output values. The following figure shows an artificial neuron:
\n\t\t\tArtificial neuron.
The neural network model consists of:
\n\t\t\tneural transfer function
network topology, i.e. a way of interconnecting between neurons,
learning laws
According to topology, networks are differing by a number of neural layers. Usually each layer receives inputs from previous one, and sends its outputs to the next layer. The first neural layer is called input layer, the last one is output layer and other layers are called hidden layers. Due to a way of interconnecting between neurons, networks may be divided to recursive and non-recursive ones. In recursive neural networks, higher layers return information to lower ones, while in non-recursive ones there is a signal flow only from lower to higher layers.
\n\t\t\tNeural networks learn from examples. Certainly there must be many examples, often even tens of thousands. Essence of a learning process is that it causes corrections in synaptic weights. When new input data cause no more changes in these coefficients, it is considered that a network is trained to solve a problem. Training may be done in several ways: controlled training, training by grading and self-organization.
\n\t\t\tNo matter which learning algorithm is used, processes are in essence very similar, consisting from following steps:
\n\t\t\tA set of input data is presented to a network.
Network processes information and remembers result (this is a step forward).
The error value is calculated by subtracting obtained result from the expected one.
For every node a new synaptic weight is calculated (this is a step back).
Synaptic weights are changed, or old ones are left and new ones are remembered.
On network inputs, a new set of input data is brought to network inputs and steps 1-5 are repeated. When all examples are processed, synaptic weights values are updated and if an error is under some expected value the network is considered trained.
We will consider two training modes: controlled training and self-organization training.
\n\t\t\tThe back-propagation algorithm is the most popular algorithm for controlled training. The basic idea is as follows: random pair of input and output results is chosen. Input set of signals is sent to the network by bringing one signal at each input neuron. These signals are propagating further through the network, in hidden layers, and after some time a results show on output. How has this happened?
\n\t\t\tFor every neuron an input value is calculated, in a way we previously explained; signals are multiplied by synaptic weights of corresponding dendrites, they are added and a neuron\'s transfer function is being applied to obtained value. The signal is propagated further through the network in a same way, until it reaches output dendrites. Then a transformation is done once again and output values are obtained. The next step is to compare signals obtained on output axon branches to expected values for given test example. Error value is calculated for every output branch. If all errors are equal to zero, there is no need for further training – network is able to perform expected task. However, in most cases error will be different from zero. Then a modification of synaptic weights of certain nodes is called for.
\n\t\t\tSelf-organized training is a process where a network finds statistical regularities in a set of input data and automatically develops different behavior regimes depending on input. For this type of learning, the Kohonen algorithm is used most often.
\n\t\t\tThe network has only two neural layers: input and output one. Output layer is also called a competitive layer (reason will be explained later). Every input neuron is connected to every neuron in output layer. Neurons in output layer are organized in two-dimensional matrix (Zurada, J. M.1992).
\n\t\t\tMultilayer neural network with signal propagation forward is one of often used architectures. Within it, signals are propagating only ahead, and neurons are organized in layers. Most important properties of multilayer networks with signal propagation forward are given as following theorems:
\n\t\t\tMultilayer network with a single hidden layer may uniformly approximate any real continual function on the finite real axis, with arbitrary precision.
Multilayer network with two hidden layers may uniformly approximate any real continual function of several arguments, with arbitrary precision.
Input layer receives data from environment. Hidden layer receives outputs of a previous layer (in this case, outputs of input layer) and, depending on sum of input weights, gives output. For more complex problems, sometimes is necessary more than one hidden layer. Output layer computes, on the basis of weight sum and transfer function, outputs from neural network.
\n\t\t\tThe following figure shows a neural network with one hidden layer.
\n\t\t\tNeural network with one hidden layer and with signal propagation forward.
In this work, we used Kohonen neural network, which is a self-organizing map of properties, belonging to a class of artificial neural networks with unsupervised training (Kukolj D., Petrov M., 2000). This type of neural network may be observed as topologically organized neural map with strong associations to some parts of biological central nervous system. The notion of topological map understands neurons that are spatially organized in maps that guard, in a certain way, the topology of input space. Kohonen neural network is intended for following tasks:
\n\t\t\tQuantumization of input space
Reduction of output space dimension
Preservation of topology present within structure of input space.
Kohonen neural network is able to classify input samples-vectors, without need to recognize signals for error. Therefore, it belongs to group of artificial neural networks with unsupervised learning. In actual use of Kohonen network in algorithm for obstacle avoidance, network is not trained but enhancement neurons are given values calculated in advance. Regarding clusterization, if a network may not classify input vector to any output cluster, than it gives data regarding how much the input vector is similar to every of clusters defined in advance. Therefore, this paper uses Fuzzy Kohonen neural clusterization network (FKCN).
\n\t\t\tEnhancement of h.263 code properties is attained by generating a prototype codebook, characterized by highly changeable differences in picture blocks. Generating codebook is attained by training of self-organizing neural network (Haykin, 1994; Lippmann, 1987; Zurada, 1992). After realization of original training concept (Kukolj and Petrov, 2000), a single-layer neural network is formed. Every node of output ANN layers represents a prototype within codebook. Coordinates of every and node within network is represented by difficulty synaptic coefficients w\n\t\t\t\t\ti\n\t\t\t\t. After initialization, the code proceeds in two iterative phases.
\n\t\t\tFirst, closest node for every sample is found, using Euclidean distance, and node coordinates are computed as arithmetic means of coordinates for samples clustered around every node. The node balancing procedure is continued by confirmation of following condition:
\n\t\t\twhere T\n\t\t\t\t\tASE\n\t\t\t\t is equal to a certain part of present value of average square error (ASE). Variables w\n\t\t\t\t\ti\n\t\t\t\t and w\n\t\t\t\t\ti\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\'\n\t\t\t\t are synaptic vectors of node and in present and previous code iteration. If above condition is not met, this step is repeating, otherwise the procedure is proceeding further.
\n\t\t\tIn a second step, so-called dead nodes are considered, i.e. nodes that have no assigned samples. If there are no dead nodes, T\n\t\t\t\t\tASE\n\t\t\t\t has very low positive value. If dead nodes are existing, value q for pre-defined number of nodes (q<<K), with maximum ASE value, is found. Then dead node is moved near one randomly chosen node from q nodes with maximum ASE values. Now new coordinates of the node are as follows:
\n\t\t\twhere w\n\t\t\t\t\tmax\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tq\n\t\t\t\t is location of chosen node between q nodes with highest ASE, w\n\t\t\t\t\ti\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tnew\n\t\t\t\t is new node location, and = 1, 2,...,n\n\t\t\t\tT are small random numbers. The process of deriving new coordinates for dead nodes (2) is repeated for all of those nodes. If maximal number of iteration is achieved, or if in previous and present iteration number of dead nodes is equal to zero, code ends. Otherwise it returns to first stage.
\n\t\tThe basic way of removing spatial sameness during coding in h.263 code is using of transformation (DCT) coding (Kukolj at all, 2006). Instead of being transferred in original shape after DTC coding, data are presented as the coefficient matrix. Advantage of this transformation is that obtained coefficients could be quantized, which increases the number of coefficients with zero value. This enables removal of excess bits using entropy coding on the bit repeating basis (run-length).
\n\t\t\tThis approach is efficient in cases when a block is poor in details, so the energy is localized in a few first coefficients of DCT transformation. But, when a picture is rich in details, the energy is equally distributed to other coefficients as well, so after quantization we do not obtain consecutive zero coefficients. In these cases, coding of those blocks uses much more bits, since bit-repetition coding could not be efficiently used. Basic way of compression factor control in this case is increase of quantization step, which brings to loss of small details in reconstructed block (block is blurred) with highly expressed block-effect on reconstructed picture (Cloete, Zurada, 2000).
\n\t\t\tEnclosed improvement of h.263 code is based on detection of these blocks and their replacement by corresponding ANN node. Basic criterion for critical blocks detection is the length of generated bits, using the standard h.263 code.
\n\t\t\tAs training set for ANN we used a set of blocks, which are, during the standard h.263 process, represented with more than 10 bits. Boundary level of code length, N=10 bits, have been chosen with purpose to obtain codebook with 2N=1024 prototypes.
\n\t\t\tIn order to obtain training set, video sequences from "Matrix" movie were used, as well as standard CIF test video sequences "Mobile and calendar" (Hagan, et al 2002). A training set from about 100,000 samples was obtained for ANN training. As a training result, training set was transformed into 1024 codebook prototypes with least average square error regarding the training set.
\n\t\t\tThe modified code is identical with standard way of h.263 compression of video stream until the stage of move vector compensation. Every block is coded by the standard method (using DCT transformation and coding on the basis of bit repeating), and than decision on application of ANN instead of standard approach is made. Two conditions must be fulfilled in order to use the network.
\n\t\t\t\n\t\t\t\t\t\tCondition of code length: whether standard approach gives the code longer of 10 bits as the representation of observed block. This is the primary condition, providing that ANN is used only in cases when standard code does not give satisfying compression level.
\n\t\t\t\t\t\tCondition of activation threshold: whether average square error, obtained using neural network, is within boundaries:
where:
\n\t\t\tASEINN - average square error obtained using ANN;
\n\t\t\tASEDCT - average square error obtained using the standard method
\n\t\t\tk - activation threshold for the network (1.0 - 1.8).
\n\t\t\tOn the basis of these conditions, choice between standard coding method and ANN application is being made.
\n\t\t\tChanges in h.263 stream format.
Format of coded video stream is taken from h.263 syntax (ITU-T, 1996). Data organization in levels has been kept, as well as a way of representation for block moves vector. A modification of syntax of block level was done, introducing additional field (1 bit length) in header of block level (Fig. 3), in order to note which coding method was used in certain blocks.
\n\t\tTesting of the described modified h.263 code was done on dynamic video sequence from the "Matrix" movie (525 pictures, 640x304 points). Basic measured parameters were the size of coded video stream and error within coding process. Error is expressed as peak signal to noise ratio (PSNR):
\n\t\t\twhere ASE\n\t\t\t\t\tl\n\t\t\t\t is average square error of reconstructed picture in comparison to the original one.
\n\t\t\tDuring the testing, quantization step used in standard DCT coding process and activation threshold of neural network (expressed as coefficient k in formula (4)) were varied as parameters.
\n\t\t\tThe standard h.263 was used as a reference for comparison of obtained results.
\n\t\t\tTwo series of tests were done. In first group of tests, quantization step has been varied, while activation threshold was constant (k=1.0). In second group of tests, activation threshold has been varied, with constant value for quantization step (1.0).
\n\t\t\t\n\t\t\t\tFigure 8 shows the size of obtained coded stream for both methods. It could be seen that compression level obtained using ANN is higher than one obtained using standard h.263 code. For higher quantum values, comparable sizes of stream are obtained, since in this case condition of code length for ANN use was not met, so the coding is being done almost without ANN.
\n\t\t\t\n\t\t\t\tFigure 9. shows the size of error within coded video stream for both methods. It could be seen that, for same values of used quantum, ANN has insignificantly higher error than the standard h.263 approach.
\n\t\t\tDependence of stream size from quantum.
Dependence of PSNR from quantum.
\n\t\t\t\tFigures 10. and 11. show results obtained by varying activation threshold of neural network between 1.0 and 1.8. Due to clearness, results are shown for the first 60 pictures from the test sequence. Sudden peaks correspond to changes of camera angle (frame).
\n\t\t\tDependence of compression from the ANN activation threshold.
Dependence of PSNR from the ANN activation threshold.
Obtained results show that with increase of neural network activation threshold, compression level decreases and quality of video stream increases. Further increase of activation threshold (above k=1.8), effect of ANN on coding becomes minor.
\n\t\tThe paper deals with h.263 recommendation for the video stream compression. Basic purpose of the modification is stream compression enhancement with insignificant losses in picture quality. Enhancement of the video stream compression is achieved by artificial neural network. Conditions for its use are described as condition of code length and condition of activation threshold. These conditions were tested for every block within picture, so the coding of the block was done by standard approach or by use of neural network. Results of testing have shown that by this method the higher compression was achieved with insignificantly higher error in comparison to the standard h.263 code.
\n\t\tTitanium was discovered in 1791, but it came into effective application only in the 1950s. After 115 years, i.e., in the year 1906, M. A Hunter at General Electric Company prepared pure titanium for the first time [1]. Since 1950s, titanium holds a prime position in aerospace, biomedical, automotive, and chemical processing industries due to unique features listed below:
Low density (60% of steel or super alloy’s density),
Higher tensile strength (Higher than ferritic stainless steel and comparable to martensitic stainless steel and Fe- base superalloys)
Higher operating temperature (Up to 595°C for commercially available alloys and >595°C for titanium aluminides)
Excellent corrosion resistance (Higher than stainless steel and biocompatible)
Forgeability
Castability (Mostly by investment casting)
Despite being the fourth-most abundant structural metal available in the earth crust, its commercial exploitation has been low compared to steel and aluminium due to high cost of production.
\nPure Titanium has an hcp crystal structure. Due to the allotropic nature of titanium, the room temperature hcp crystal structure (alpha phase) will be transformed to bcc (beta phase) structure on heating to a particular temperature called beta transus temperature (882.5°C). Alloying elements of titanium are classified on the basis of their influence on the transus temperature. For example, if the transus temperature is increased on the addition of the certain elements, then they are called as alpha stabilisers (Al, O, N, and C); similarly there are some other elements which bring the transus temperature down and they are termed as beta stabilisers (V, Mo, Ta and Nb). The elements Sn and Zr have little or no effect on transus temperature and are termed as neutral elements.
\nBeta alloys form the metastable beta phase upon quenching rather than undergoing martensitic transformation. A schematic representation of the beta isomorphous phase diagram is shown in the Figure 1. Beta alloys can also be classified as those which have alloy which has enough beta stabilisers to avoid the martensitic start (Ms) pass through upon quenching. Beta alloys are further classified into metastable and stable beta alloys based on the content of beta stabilisers. Commercially available beta alloys are metastable beta alloys and stable beta alloys are not commercially available [2]. The metastable beta phase can precipitate the fine alpha phase upon ageing/thermal treatment. Hence, beta alloys are hardenable and can attain a higher strength level than alpha + beta alloys and higher specific strength compared to many other alloys [3].
\nBeta isomorphous phase diagram.
Corrosion resistance of beta alloys is also found to be better than that of alpha + beta alloys. Higher hydrogen tolerance makes beta alloys to perform better in the Hydrogen-rich environments [2]. Increased fracture toughness for a given strength level and amenability to room temperature forming and shaping are superior attributes compared to alpha + beta alloys [1]. Ti-13V-11Cr-3Al (B120VCA) was the first beta alloy produced/developed and used in the SR-71 (Surveillance aircraft) as a sheet product.
\nBeta alloys’ inherent characteristics such as pronounced ductility owing to the crystal structure (bcc), heat treatability, and superior cold rollability make them an effective alternative to alpha + beta alloys [4]. Furthermore, beta alloys have lower beta transus temperature than the alpha + beta alloys [5]. Hence, beta alloys are considered to be the economical choice in perspective of processing compared to the alpha + beta alloys. For example, despite the higher formulation cost, Ti-15V-3Al-3Cr-3Sn alloy’s thinner gauges (<2 mm thick) cost one-tenth of those of Ti-6Al-4V [3].
\nThe initial step is the fabrication of ingot from sponge for conversion to mill products. The melting practices to produce beta titanium alloy ingots can be broadly categorised into Vaccum Arc Remelting (VAR) and Cold Hearth Melting.
\nThe conventional method used for the melting of beta titanium alloys is the Vacuum Arc Remelting (VAR) in a consumable arc furnace. In VAR, the furnace is initially evacuated for required vacuum and a dc arc is struck between the two electrodes. Here a consumable electrode (material to be melted) is employed as the cathode and starting materials such as titanium-based metal chips or machine turnings act as the anode. The consumable electrode can be fabricated from either of the two strategies.
From the compacted sponge and/or scrap
From plasma/electron beam hearth melting
Among these methods, the first method of predensification by compacting using a hydraulic press is widely used to fabricate electrodes. Compacted electrodes with nominal alloy composition are made by the pressing of blended clean and uniform-sized titanium sponge and alloying elements devoid of any harmful inclusions. These compacts (called as briquettes) are then assembled with bulk scrap to form the first melt electrode (called as a stick) by appropriate welding methods.
\nFinally, these fabricated electrodes are placed inside a vacuum furnace. When the electric arc is established, associated heat generation will result in the dripping of molten metal down to the water-cooled copper crucible to form the ingot. Initially, a layer of solid titanium or skull will be formed on the surface of cooled copper crucible which will hold the subsequently falling molten metal. In order to ensure chemical homogeneity, the ingots will be inverted and remelting will be performed. Ingots produced during first stage melting are again used as consumable electrodes during double or triple remelting. In addition to this, electrical coils are provided in most of the VAR furnaces to generate an electromagnetic field capable of stirring the molten metal thereby further enhancing the homogeneity. Cold hearth melting is another developing technique which uses either plasma arc (PAM) or electron beam (EBM) melting furnace.
\nProper monitoring should be ensured to control the solidification of beta titanium based ingots. Specifically, beta eutectoid compositions containing Fe, Mn, Cr, Ni and Cu are associated with depressed freezing temperatures [2]. This allows for solidification over a significant temperature range, consequently leading to solute segregation during solidification of the ingot. Such type of segregation results in regions with lower beta transus and results in a microstructure distinctive from the surrounding material. These solute segregated regions are clearly visible in beta titanium alloys subjected to heat treatment below/near to beta transus and are termed as beta flecks. Beta flecks, which range from a scale of few hundred micrometres to a few millimetres, can act as crack nucleation sites leading to fatigue failure. Beta flecks are mostly developed in large diameter ingots. However, beta isomorphous alloys containing Nb, Mo and V are not associated with these depressed solidification temperatures and are less prone to solute segregation.
\nLower values of tensile ductility and low cycle fatigue life of near-β Ti alloy Ti–10V–2Fe–3Al was found to be due to the presence of beta flecks [6]. Under tensile loading, crack nucleation occurred at beta fleck grain boundaries leading to intergranular and quasi-cleavage fracture. In the case of fatigue loading, the inhomogeneous strains developed due to the presence of beta flecks accelerated the crack nucleation and early crack propagation.
\nFor an expensive material such as titanium, casting is the perfect choice in attaining a (near) net shape in the fabrication of components with complex geometry without incurring much wastage. A significant weight (35%) saving can be achieved by employing the titanium casting instead of stainless steel casting in B-777 aircraft [7]. In general, rammed graphite mould and investment casting were utilised in titanium casting. Investment casting is preferred to obtain thin sections and better surface finish [8]. Ti-5Al-5V-5Mo-3Cr castings followed by HIP (Hot Isostatic Pressing) possess a superior strength compared to hipped Ti-6Al-4V castings with almost same ductility [9]. To extend brake life of fighter aircraft (F-18 EF)Ti-15V-3Al-3Cr-3Sn castings were used instead of Ti-6Al-4V castings due to the higher specific strength of the former [10].
\nTo exploit the ductile nature of the beta phase (bcc crystal structure), even for alpha and alpha + beta alloys, ingot break down forging is done above the beta transus temperature. In general, to avoid thermal stress cracking, titanium alloys are subjected to preheating before high-temperature forging.
\nForging is performed to produce billets and bars of titanium with the optimum combination of strength and ductility [11]. Forging is performed using hydraulic presses. Both straight-forging and upset forging are performed in case of Ti alloys. For greater deformation and larger size, upset-forging is preferred [1]. Higher reactivity of the titanium demands the inert / vacuum processing to prevent surface contamination during high-temperature processing [1]. Drawing operation of titanium is prone to galling and seizing. Hence, proper lubricants have to be employed to avoid those effects [1]. Compared to all other Ti alloys, beta alloys can withstand high pressure before cracking. Ti- 13V-11Cr-3Al can withstand up to 690 MPa without cracking. In contrast, Ti-6Al-4V can withstand 585 MPa before cracking [1].
\nThe microstructure of the ingots of beta alloys varies from small equiaxed grains (at the surface) to elongated columnar grains and large equiaxed grains at the bulk/centre of the ingot [4]. Beta Ti alloys are more suitable for low temperature working without being vulnerable to rupture or cracking compared to other Ti alloys [1] and this effect is attributed to the availability of enough slip systems to accommodate the deformations.
\nSecondary forging refers to the forging process employed to obtain the final shape/components. The temperature required for this kind of forging is lower than that for ingot breakdown forging. Unlike alpha and alpha + beta alloys, beta alloys show a significant increase in strength at high strain rates [1]. Hence, higher pressures are to be applied for forging of beta alloys; the pressure required to induce crack during forging is higher for beta alloys compared to alpha and alpha + beta alloys [1]. Beta titanium alloys have a broader range of forging temperature compared to alpha/alpha + beta alloys.
\nDue to the lower beta transus temperature, beta alloys have lower hot working temperature compared to alpha and alpha + beta alloys, For example, Ti–10V–2Fe–3Al has a secondary working temperature range between 700–870°C [12]. Types of forging and features are given in the Table 1.
\nS.No. | \nForging type | \nFeatures | \n
---|---|---|
1 | \nOpen-Die Forging | \n\n
| \n
2 | \nClosed-Die Forging | \n\n
| \n
3 | \nHot-die forging | \n\n
| \n
4 | \nIsothermal Forging | \n\n
| \n
5 | \nPrecision forging | \n\n
| \n
Types of forging and its features [1].
Unlike other alloys, rolling of titanium requires higher working pressure and extreme control in temperature. Cylindrical rollers are used to produce the strips, sheet and plate. In contrast, grooved rollers are employed in producing the rounds and other structural shapes. In sheet and plate rolling process, cross rolling is done to reduce the anisotropy in mechanical properties. Texture strengthening is less pronounced in the beta alloys compared to alpha alloys [1]. The lower rate of strain hardening of the beta alloy makes it more acquiescent to cold working.
\nIn Ti-3.5Al-5Mo-6V-3Cr-2Sn-0.5Fe alloy, rolling and ageing in the sub-transus (alpha + beta field) temperature yielded a better combination of the strength and ductility compared to working in the beta field [13]. Sheet beta Ti alloys are amenable to cold rolling. Cold rolling has a strong effect upon mechanical properties. For example, Rosenberg [14] reported the effect of cold rolling on tensile strength, yield strength and ductility of Ti-15-3 alloy:
UTS (Rolled alloy) = UTS (un-rolled) + 0.75 × Percentage of reduction (%)
YS (Rolled alloy) = YS (un-rolled) + 0.65 × Percentage of reduction (%)
Ductility (Rolled alloy) = EL (un-rolled) − 0.65 × Percentage of reduction (%)
Two high roll mill and three high roll mill are commonly used for rolling titanium and its alloys.
\nMaterial processing performed with the aid of both mechanical force and thermal/ heat treatment can be termed as thermomechanical processing. The primary objective of this processing is to obtain a component in functional design with pre-determined microstructure and corresponding mechanical properties. Thermomechanical processing of beta Ti alloys can be done both above transus temperature (Super-transus processing) and below the transus temperature (Sub-transus processing). Super-transus processing with hot deformation is optimised to obtain fine recrystallised beta grains. Sub-transus processing is optimised to obtain fine beta grains with controlled alpha phase morphology [12]. Size, volume fraction, morphology, and the spatial distribution of the alpha precipitates formed during the thermomechanical processing have a vital influence over the mechanical properties of the end product.
\nIn Ti-15V-3Al-3Cr-3Sn alloy, Boyer et al. [15], showed the usefulness of thermomechanical treatment for attaining a wide range of tensile strength (from 1070 to 1610 MPa.)
\nHeat treatment is the basic metallurgical process through which optimization of hardness, tensile strength, fatigue strength and fracture toughness can be achieved. All the metastable beta alloys are heat treatable to attain higher strength than alpha + beta alloys.
\nDuplex ageing treatment yielded a superior combination of mechanical properties with no precipitation free zone and finer alpha precipitation compared to single ageing in Ti-15V-3Al-3Cr-3Sn-3Zr [16] and Ti-3Al-8V-6Cr-4Mo-4Zr [17]. The rate of heating to ageing temperature was found to have a substantial effect on the evolution of microstructure and mechanical properties [18]. Choice of solution treatment temperature is important. For example, for Ti-1Al-8V-5Fe (Ti185), solution treatment near beta transus temperature leads to a highest tensile and yield strength [19].
\nSolution treatment followed by ageing in metastable beta alloys will lead to a microstructure consisting of soft alpha in the beta grain boundaries. Hence, this softer alpha phase may lead to the decline in the HCF behaviour [20] and tensile ductility by augmenting the intergranular fracture [17]. For example, Sauer and Luetjering [21] have also reported the adverse effect of alpha phase layers along the beta grain boundaries on the tensile and fatigue behaviour of Ti-5Al-2Sn-4Zr-4Mo-2Cr-1Fe (β CEZ).
\nModifying the surface is an effective and economical way to enhance the tribological and fatigue properties of the material. Thermo-chemical and mechanical surface modification techniques are common in beta alloys.
\nIn order to enhance the surface hardness, wear resistance and near-surface strength, thermo-chemical surface processing techniques such as nitriding and carburising are employed. Among various thermo-chemical surface processing techniques, nitriding is extensively used. In this process, the nitrogen is fused into the titanium base alloy. Among the various technologies used for Nitriding, i.e., gas nitriding, laser nitriding, plasma nitriding, Ion nitriding and gas Nitriding are used widely [22]. Titanium nitrides will be formed on the surface as a result of the nitriding and these nitrides increase the surface hardness drastically and improve the tribological properties at the expense of the ductility of the material. Increased hardness due to TiN formation was made use in flap tracks of Military airplanes [23]. However, nitriding has a negative influence on the tensile strength and fatigue strength of the material.
\nMechanical surface modifications such as shot peening, ball burnishing and laser peening are developed to enhance the fatigue behaviour of the target material by inducing the residual compressive stress and work hardening effect in near surface region. Both crack nucleation and crack propagation during fatigue loading were found to be affected by the surface modification treatment. However, surface roughness will be significantly increased at the end of the mechanical surface modification such as shot peening and this may lead to early crack initiation.
\nSince 1970s, shot peening is being employed in enhancing the mechanical behaviour of Ti alloys in aerospace industries [24]. Schematic representation of shot peening is shown in the Figure 2. Shot peening of beta alloys, i.e. Ti-10V-2Fe-3Al and Ti-3Al-8V-6Cr-4Mo-4Zr yielded a marginal increase in the fatigue life compared to electro polished sample [25]. In LCB beta alloy, in order to compensate the residual compressive stress induced in the surface after peening, substantial tensile residual stress formed in the subsurface region and this deteriorated the fatigue behaviour compared to polished sample [26]. It is important to control the shot peening conditions to get the desired enhancement in fatigue life.
\nSchematic representation of shot peening.
Unlike shot peening and laser peening, roller burnishing reduces the surface roughness by stressing the surface with a roller ball with optimised pressure. Schematic representation of the roller burnishing is shown in the Figure 3. Roller burnishing of Ti-10V-2Fe-3Al beta alloy induced deeper and higher magnitude residual stress compared to shot peening. In roller burnishing of LCB beta alloy, higher the rolling pressure, deeper was the site for fatigue crack nucleation [27]. In Beta C (Ti-3Al-8V-6Cr-4Mo-4Zr) alloy, deep rolling ended up with deeper residual stress distribution compared to shot peening, but the magnitude of the residual stress remained high for the shot peened sample. A marginal increase in fatigue life was achieved through deep rolling of Beta C alloy [28].
\nSchematic representation of the ball burnishing.
Compared to shot peening, laser peening has unique features like the capability of inducing deeper and stable residual stress with extreme control in operation. Conventionally, laser peening is performed using Nd: Glass lasers after applying the coating, i.e. black paint on the target surface. To make this process simple, economical and more portable, LPwC (Laser peening without Coating) was developed in 1995 [29]. LPwC has proven to be an effective technique by inducing a relatively high compressive residual stress. For example, a residual stress of approx. −825 MPa was induced at a depth of ~75 μm from the surface in LCB (Ti-6.8Mo-4.5Fe-1.6Al) beta alloy [30].
\nIn the case of implant materials, the interaction between the biological environment and the implanted materials occurs on the biomaterial surface. Clinical success of implant materials is greatly dependent on various surface characteristics viz. chemical inertness, texture, corrosion resistance and surface energy [31] . In the case of orthopaedic implants, the surface should possess more bone forming ability and for blood contacting devices, it should not initiate any blood clot formation. Hence surface modification of biomedical grade beta titanium alloys is very significant. Oxide layer formation will occur spontaneously on the surface of titanium on exposure to air. This TiO2 film possesses a thickness of about 1.5 to 10 nm at room temperature. Chemical stability and structural characteristics of this oxide film greatly influence the biocompatibility of titanium implant materials. Some of the potential methods to enhance the properties of native TiO2 film are anodisation, sol–gel methods, acidic and alkaline treatments [32]. In addition to these, specific surface topographies and roughness induced by mechanical surface modifications (sandblasting, grit blasting, peening) have improved the clinical success of implant materials. An overview of the various surface modification techniques employed for biomedical beta titanium alloys is schematically shown in Figure 4.
\nOverview of surface modification of beta titanium alloys for biomedical application [33].
In dental applications, Laser Nitriding has proved to be an effective process in enhancing the surface hardness, the coefficient of friction and corrosion resistance of the Ti-20Nb-13Zr and wear and corrosion resistance of Ti-13Nb-13Zr biomedical-beta alloys [34, 35]. Plasma nitrided beta 21S (Ti-15Mo-3Nb-3Al-0.2Si) alloy showed higher hardness but inferior corrosion resistance compared to the untreated alloy [36]. In line with the Nitriding, carburising of Ti-13Nb-13Zr (a biomedical beta alloy used for artificial joints) improved the surface hardness and wear resistance through the formation of the titanium carbide [37].
\nAs mentioned in the introduction (Section 1), a major limiting factor for the titanium application is its high production cost. In addition to the high raw material cost, the forging, machining contribute majorly to the production cost. This limitation instigated the industries to work towards processing methods through which the near net shape (NNS) could be obtained. Despite the higher cost involved, Powder metallurgy of titanium is capable of yielding almost same or better mechanical properties compared to wrought and cast components along with accurate net shape capability. This merit is mainly attributed to the absence of texture, segregation and nonuniformity in the grain size encountered in conventional processing.
\nEven for the components made through powder metallurgy route, solution treatment followed by ageing (STA) leads to an enhancement in mechanical properties such as tensile strength and yield strength compared to the as-sintered condition [38]. Ti-10V-2Fe-3Al and Ti-11.5Mo-6Zr-4.5Sn alloys have been produced through powder metallurgy route. However, 90% of the powder metallurgy is focussed on the alpha + beta alloy Ti-6Al-4V.
\nGuo et al. [39] reported a remarkable increase in the mechanical properties of Ti-10V-2Fe-3Al powder alloy compared to the wrought and cast products through isothermal forging of the sintered alloy. Jiao et al. [40] studied the model of alpha phase spatial distribution in laser additive manufactured Ti-10V-2Fe-3Al. The influence of nano-scale alpha precipitates on tensile properties of age hardened laser additive manufactured Ti-5Al-5Mo-5V-1Cr-1Fe (Ti-55,511) alloy was studied by He et al. [41] and the authors reported that precipitated nanoscale alpha precipitates have led to a decline in ductility.
\nA recent forecast released by Airbus Industries [42], confirms the promising development of air transport requiring 37,400 aircraft at a value of 5.8 trillion US dollars business in the next 20 years. However, reducing the fuel consumption to control the emission of CO2 and NOx is the driving factor for the aerospace industries and this could be possible by reducing the overall weight [43]. Similarly, in space application weight of the payload is more crucial than civil/cargo aviation. Ti-6Al-4V is a workhorse for the aerospace industry for several decades and 65% of total titanium production in the United States belongs to Ti-6Al-4V alloy [3].Even though the alpha +beta alloys dominated the scene, beta alloys with their unique characteristics such as excellent hardenability, heat treatability to high strength levels and a high degree of sheet formability, are becoming increasingly important for the aerospace sector. Beta alloys and their aerospace application are listed in the Table 2.
\nS. No. | \nAlloy | \nApplication/components | \n
---|---|---|
1 | \nTi-15V-3Al-3Cr-3Sn | \nLanding gear, springs, sheet, plate and airframe castings, environmental control system ducting | \n
2 | \nTi-6V-6Mo-5.7Fe-2.7Al | \nFasteners | \n
3 | \nTi-13V-11Cr-3Al | \nAirframe, landing gear and springs | \n
4 | \nTi-3Al-8V-6Cr-4Mo-4Zr (β-C) | \nSprings and fasteners | \n
5 | \nTi-11.5Mo-6Zr-4.5Sn | \nRivbolts—Boeing 747 | \n
6 | \nTi-5Al-5Mo-5V-3Cr | \nAircraft landing gear, Fuselage components and high lift devices | \n
7 | \nTi-10V-2Fe-3Al | \n\n
| \n
8 | \nBeta 21s | \n\n
| \n
9 | \nTi-35V-15Cr (Alloy C) | \nCompressor and exhaust nozzle components | \n
Titanium is the ultimate choice for biomedical applications as they outperform conventionally used biomedical alloys such as 316L stainless steel and cobalt-chromium alloys [47]. The formation of a nanometre thick oxide layer on titanium when exposed to any environment imparts high corrosion resistance and superior biocompatibility [48]. All classes of titanium α, α + β, near β and β alloys are widely used for biomedical applications.
\nDespite being initially developed for aerospace applications, CP titanium and Ti-6Al-4V are still the most widely used Ti grades being used for biomedical applications. However, CP Ti is associated with lower wear resistance and Ti-6Al-4V when implanted inside the body releases Al and V ions which can lead to severe neurological disorders and allergic reactions. Moreover, the elastic modulus values of these alloys (~110 GPa) are almost four times than that of human cortical bone (20–30 GPa) which can lead to stress shielding effect. This led to the development of β-Ti alloys composed of non-toxic elements and their inherent lower elastic modulus assists in reducing the stress shielding effect when used for orthopaedic applications [3]. Alloy systems based on Ti-Nb, Ti-Mo, Ti-Ta and Ti-Zr are potential materials for biomedical applications. Some of these β-Ti alloys initially developed are Ti-15Mo-5Zr-3Al, Ti-12Mo-6Zr-2Fe (TMZF), Ti-15Mo-3Nb-0.3O (21SRx) and Ti-13Nb-13Zr possessing modulus values in the range of 70–90 GPa.
\nIn the early 1990s, medical device industry focused on developing these low modulus β-Ti alloys for orthopaedic applications. Initially, two β-Ti alloys Ti-13Nb-13Zr specified by ASTM F1713 and Ti-12Mo-6Zr-2Fe (TMZF) specified by ASTM F1813 received Food and Drug Administration approval as implant materials. Among these, TMZF alloy possesses an elastic modulus of about 74–85 GPa, with a yield strength of 1000 MPa. During the early 2000s, this metastable β-Ti alloy was used for making hip stems, which rub against a modular neck made from a cobalt-chromium based alloy. However, in 2011, the US Food and Drug Administration recalled the use of this TMZF alloy due to the unacceptable level of wear debris formation. Another β-Ti alloy 21SRx is derived from the aerospace alloy 21S from which aluminium was eliminated over biocompatibility concerns. In addition, alloys such as Ti-29Nb-13Ta-4.6Zr and Ti-35Nb-7Zr-5Ta are receiving increasing attention due to their lower elastic moduli of about 65 and 55 GPa, respectively, lower than other β-Ti alloys [50].
\nApart from orthopaedics, titanium is extensively used in the dental applications [49]. In the case of orthodontic wire material, it should possess three general characteristics viz. large spring back (ability to be deflected over longer distances without permanent deformation), lower stiffness and high formability [51]. The initially utilised materials for orthodontic wire application were gold based alloys containing copper, palladium, platinum or nickel. However, spring back values of these gold alloys were limited owing to their lower yield strength. In the 1960s gold was replaced by stainless steel and cobalt-chromium based alloy (elgiloy). These materials continue to be the standard orthodontic wire material for the past 70 years and possess higher springiness and strength with comparable corrosion resistance. During the early 1970s, nickel-titanium alloy Nitinol (Nickel Titanium Naval Ordinance Laboratory) was also used for orthodontic wires. Even though Nitinol orthodontic archwires are widely used owing to their superior superelastic properties, their use is hampered by reduced formability during the final stages of treatment. Moreover, there are serious concerns over the nickel ion release from these materials in the oral environment. It was later demonstrated that orthodontic wires made from β-Ti alloy Ti-11.3Mo-6.6Zr-4.3Sn (TMA alloy) possess enhanced spring back and formability, along with reduced stiffness. TMA alloys possess ideal elastic modulus values lower than that of stainless steels and higher than nitinol [51]. The higher surface roughness associated with these TMA wires can, however, lead to arch wire-bracket sliding friction due to the high coefficient of friction of TMA alloys. One of the most successful approaches to tackle this problem is the ion implantation process which renders the TMA wires with lower surface roughness and reduced friction coefficients. Another beta titanium alloy Ti-6Mo-4Sn was also investigated for orthodontic wire applications. By proper heat treatment procedures, this alloy exhibited an elastic modulus of 75 GPa and a tensile strength of 1650 MPa [52]. Ti-13V-11Cr-3Al, metastable Ti-3Al-8V-6Cr-4Mo-4Zr, metastable Ti-15V-3Cr-3Al-3Sn, near-beta Ti-10V-2Fe-3Al were also researched for dental archwire applications.
\nThough beta titanium alloys possess superior haemocompatibility, which is beneficial for cardiovascular devices, they are not fully exploited for cardiovascular applications. Despite higher haemocompatibility, no β-Ti alloy based stents have been commercialised which can be attributed to their lower ductility and modulus as compared to 316L stainless steel and cobalt-chromium based stent materials. Recently, research based on the development of new β-Ti alloy compositions for coronary stent applications has been getting increased attention. Initial studies on Ti-12Mo (wt %) and ternary Ti-9Mo-6W (wt %) demonstrated a ductility of about 46% and 43% respectively [53]. Apart from this, initial investigations on Ti-50Ta, Ti-45Ta-5Ir and Ti-17Ir for stent applications were performed by Brien et al. [54]. Among the three alloys, Ti-17Ir exhibited a favourable elastic modulus of 128 GPa owing to the eutectoid Ti3Ir phase precipitation; iridium content will also assist in improving the fluoroscopic visibility of the stents during interventional procedures [54].
\nBeta titanium alloys have shown much promise and extensive research and development work has been devoted to this group of alloys over the last four decades. For aerospace applications, their heat treatability, high hardenability, high strength to weight ratio and excellent hot and cold workability are major attractions. For orthopaedic applications, their corrosion resistance to biofluids, biocompatibility and low elastic modulus coming close to that of human bone are the important attractive features. Accordingly, development of cost-effective processing techniques has also assumed importance. Problems unique to beta titanium alloys such as high degree of proneness to segregation, high loads to be applied during hot working etc. have since been resolved. Powder processing and additive manufacturing of the alloys have recently received attention and hold promise. Surface modification has been an important part of the developmental efforts and has taken a prominent place, especially for biomedical applications. Coming years are bound to witness increased exploitation of this group of alloys, particularly in biomedical and aerospace applications.
\nThe authors would like to express their gratitude to the Management of Vellore Institute of Technology (VIT)—Vellore campus, Tamil Nadu, India, for allowing us to submit this manuscript.
\nThe authors declare that there is no conflict of interest regarding the publication of this article.
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