Comparison between the most used methods to measure the variables of welding processes, considering the cost and accuracy of the method.
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Omar Tapia-Silva and Sergio Cerdeira-Estrada",authors:[{id:"19571",title:"Dr.",name:"Felipe Omar",middleName:null,surname:"Tapia Silva",fullName:"Felipe Omar Tapia Silva",slug:"felipe-omar-tapia-silva"},{id:"19577",title:"Dr.",name:"Alejandra",middleName:"A.",surname:"López-Caloca",fullName:"Alejandra López-Caloca",slug:"alejandra-lopez-caloca"},{id:"107500",title:"MSc.",name:"Ameris",middleName:null,surname:"Contreras-Silva",fullName:"Ameris Contreras-Silva",slug:"ameris-contreras-silva"},{id:"150351",title:"Dr.",name:"Sergio",middleName:null,surname:"Cerdeira-Estrada",fullName:"Sergio Cerdeira-Estrada",slug:"sergio-cerdeira-estrada"}]},{id:"37543",title:"Predictability of Water Sources Using Snow Maps Extracted from the Modis Imagery in Central Alborz, Iran",slug:"predictability-of-water-sources-using-snow-maps-extracted-from-the-modis-imagery-in-central-albo",totalDownloads:1490,totalCrossrefCites:0,signatures:"Seyed Kazem Alavipanah, Somayeh Talebi and Farshad Amiraslani",authors:[{id:"113521",title:"Dr",name:null,middleName:null,surname:"Alavi Panah",fullName:"Alavi Panah",slug:"alavi-panah"}]},{id:"37544",title:"Remote Sensing of Cryosphere",slug:"remote-sensing-of-cryosphere",totalDownloads:2997,totalCrossrefCites:3,signatures:"Shrinidhi Ambinakudige and Kabindra Joshi",authors:[{id:"103394",title:"Dr.",name:"Shrinidhi",middleName:null,surname:"Ambinakudige",fullName:"Shrinidhi Ambinakudige",slug:"shrinidhi-ambinakudige"},{id:"110648",title:"Mr.",name:"Kabindra",middleName:null,surname:"Joshi",fullName:"Kabindra Joshi",slug:"kabindra-joshi"}]},{id:"37545",title:"Remote Sensing Application in the Maritime Search and Rescue",slug:"remote-sensing-application-in-the-maritime-search-and-rescue",totalDownloads:2021,totalCrossrefCites:2,signatures:"Jing Peng and Chaojian Shi",authors:[{id:"110809",title:"Dr.",name:"Jing",middleName:null,surname:"Peng",fullName:"Jing Peng",slug:"jing-peng"},{id:"113201",title:"Prof.",name:"Chaojian",middleName:null,surname:"Shi",fullName:"Chaojian Shi",slug:"chaojian-shi"}]},{id:"37546",title:"Object-Based Image Analysis of VHR Satellite Imagery for Population Estimation in Informal Settlement Kibera-Nairobi, Kenya",slug:"object-based-image-analysis-of-vhr-satellite-imagery-for-population-estimation-in-informal-settl",totalDownloads:2884,totalCrossrefCites:9,signatures:"Tatjana Veljanovski, Urša Kanjir, Peter Pehani, Krištof Oštir and Primož Kovačič",authors:[{id:"114617",title:"Dr.",name:"Tatjana",middleName:null,surname:"Veljanovski",fullName:"Tatjana Veljanovski",slug:"tatjana-veljanovski"},{id:"136824",title:"BSc.",name:"Urša",middleName:null,surname:"Kanjir",fullName:"Urša Kanjir",slug:"ursa-kanjir"},{id:"136826",title:"Prof.",name:"Krištof",middleName:null,surname:"Oštir",fullName:"Krištof Oštir",slug:"kristof-ostir"}]},{id:"37547",title:"Remote Sensing Applications in Archaeological Research",slug:"remote-sensing-applications-in-archaeology",totalDownloads:2495,totalCrossrefCites:1,signatures:"Dimitrios D. Alexakis, Athos Agapiou, Diofantos G. Hadjimitsis and Apostolos Sarris",authors:[{id:"101025",title:"Prof.",name:"Diofantos",middleName:"G.",surname:"Hadjimitsis",fullName:"Diofantos Hadjimitsis",slug:"diofantos-hadjimitsis"},{id:"113775",title:"Dr.",name:"Dimitrios",middleName:null,surname:"Alexakis",fullName:"Dimitrios Alexakis",slug:"dimitrios-alexakis"},{id:"113778",title:"Dr.",name:"Athos",middleName:null,surname:"Agapiou",fullName:"Athos Agapiou",slug:"athos-agapiou"},{id:"113781",title:"Dr.",name:"Apostolos",middleName:null,surname:"Sarris",fullName:"Apostolos Sarris",slug:"apostolos-sarris"}]},{id:"37548",title:"The Mapping of the Urban Growth of Kinshasa (DRC) Through High Resolution Remote Sensing Between 1995 and 2005",slug:"the-mapping-of-the-urban-growth-of-kinshasa",totalDownloads:2500,totalCrossrefCites:2,signatures:"Kayembe wa Kayembe Matthieu, Mathieu De Maeyer and Eléonore Wolff",authors:[{id:"117224",title:"Dr",name:null,middleName:null,surname:"Wa Kayembe",fullName:"Wa Kayembe",slug:"wa-kayembe"}]},{id:"37549",title:"Remote Sensing for Medical and Health Care Applications",slug:"remote-sensing-for-medical-and-healthcare-applications",totalDownloads:4427,totalCrossrefCites:5,signatures:"Satoshi Suzuki and Takemi Matsui",authors:[{id:"110480",title:"Prof.",name:"Satoshi",middleName:null,surname:"Suzuki",fullName:"Satoshi Suzuki",slug:"satoshi-suzuki"}]},{id:"37550",title:"Demonstration of Hyperspectral Image Exploitation for Military Applications",slug:"demonstration-of-hyperspectral-image-exploitation-for-military-applications",totalDownloads:4517,totalCrossrefCites:1,signatures:"Jean-Pierre Ardouin, Josée Lévesque, Vincent Roy, Yves Van Chestein and Anthony Faust",authors:[{id:"113852",title:"Mr.",name:"Jean-Pierre",middleName:null,surname:"Ardouin",fullName:"Jean-Pierre Ardouin",slug:"jean-pierre-ardouin"}]}]},relatedBooks:[{type:"book",id:"1340",title:"Remote Sensing",subtitle:"Advanced Techniques and Platforms",isOpenForSubmission:!1,hash:"336dcc589420ec91a5fdc29720442313",slug:"remote-sensing-advanced-techniques-and-platforms",bookSignature:"Boris Escalante-Ramirez",coverURL:"https://cdn.intechopen.com/books/images_new/1340.jpg",editedByType:"Edited by",editors:[{id:"111500",title:"Dr.",name:"Boris",surname:"Escalante",slug:"boris-escalante",fullName:"Boris Escalante"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"37510",title:"Characterizing Forest Structure by Means of Remote Sensing: A Review",slug:"characterization-of-forest-structure-by-means-of-remote-sensing-a-review",signatures:"Hooman Latifi",authors:[{id:"103157",title:"Dr.",name:"Hooman",middleName:null,surname:"Latifi",fullName:"Hooman Latifi",slug:"hooman-latifi"}]},{id:"37511",title:"Fusion of Optical and Thermal Imagery and LiDAR Data for Application to 3-D Urban Environment and Structure Monitoring",slug:"fusion-of-optical-and-thermal-imagery-and-lidar-data-for-application-to-3-d-urban-environment-an",signatures:"Anna Brook, Marijke Vandewal and Eyal Ben-Dor",authors:[{id:"53064",title:"Prof.",name:"Eyal",middleName:null,surname:"Ben-Dor",fullName:"Eyal Ben-Dor",slug:"eyal-ben-dor"},{id:"106974",title:"Dr.",name:"Anna",middleName:null,surname:"Brook",fullName:"Anna Brook",slug:"anna-brook"},{id:"136909",title:"Dr.",name:"Marijke",middleName:null,surname:"Vandewal",fullName:"Marijke Vandewal",slug:"marijke-vandewal"}]},{id:"37512",title:"Statistical Properties of Surface Slopes via Remote Sensing",slug:"statistical-properties-of-surface-slopes-via-remote-sensing",signatures:"Josué Álvarez-Borrego and Beatriz Martín-Atienza",authors:[{id:"103367",title:"Prof.",name:"Josué",middleName:null,surname:"Álvarez-Borrego",fullName:"Josué Álvarez-Borrego",slug:"josue-alvarez-borrego"},{id:"103372",title:"Dr.",name:"Beatriz",middleName:null,surname:"Martin-Atienza",fullName:"Beatriz Martin-Atienza",slug:"beatriz-martin-atienza"}]},{id:"37513",title:"Classification of Pre-Filtered Multichannel Remote Sensing Images",slug:"classification-of-pre-filtered-multichanel-rs-images",signatures:"Vladimir Lukin, Nikolay Ponomarenko, Dmitriy Fevralev, Benoit Vozel, Kacem Chehdi and Andriy Kurekin",authors:[{id:"58658",title:"Prof.",name:"Vladimir",middleName:null,surname:"Lukin",fullName:"Vladimir Lukin",slug:"vladimir-lukin"},{id:"58660",title:"Dr.",name:"Benoit",middleName:null,surname:"Vozel",fullName:"Benoit Vozel",slug:"benoit-vozel"}]},{id:"37514",title:"Estimation of the Separable MGMRF Parameters for Thematic Classification",slug:"estimation-of-the-separable-multivariate-gauss-markov-random-field-parameters-for-thematic-class",signatures:"Rolando D. Navarro, Jr., Joselito C. Magadia and Enrico C. Paringit",authors:[{id:"111808",title:"Dr.",name:"Rolando",middleName:"Danganan",surname:"Navarro Jr.",fullName:"Rolando Navarro Jr.",slug:"rolando-navarro-jr."},{id:"113784",title:"Dr.",name:"Joselito",middleName:null,surname:"Magadia",fullName:"Joselito Magadia",slug:"joselito-magadia"},{id:"113786",title:"Dr.",name:"Enrico",middleName:null,surname:"Parinigit",fullName:"Enrico Parinigit",slug:"enrico-parinigit"}]},{id:"37515",title:"Low Rate High Frequency Data Transmission from Very Remote Sensors",slug:"low-rate-hf-data-transmission-from-very-remote-sensors",signatures:"Pau Bergada, RosaMa Alsina-Pages, Carles Vilella and Joan Ramon Regué",authors:[{id:"106907",title:"MSc.",name:"Pau",middleName:null,surname:"Bergada",fullName:"Pau Bergada",slug:"pau-bergada"},{id:"107304",title:"Ms.",name:"Rosa Ma",middleName:null,surname:"Alsina-Pages",fullName:"Rosa Ma Alsina-Pages",slug:"rosa-ma-alsina-pages"},{id:"136204",title:"Dr.",name:"Joan Ramon",middleName:null,surname:"Regué",fullName:"Joan Ramon Regué",slug:"joan-ramon-regue"},{id:"136205",title:"Dr.",name:"Carles",middleName:null,surname:"Vilella",fullName:"Carles Vilella",slug:"carles-vilella"}]},{id:"37551",title:"A Contribution to the Reduction of Radiometric Miscalibration of Pushbroom Sensors",slug:"a-contribution-to-the-reduction-of-radiometric-miscalibration-of-pushbroom-sensors",signatures:"Christian Rogaß, Daniel Spengler, Mathias Bochow, Karl Segl, Angela Lausch, Daniel Doktor, Sigrid Roessner, Robert Behling, Hans-Ulrich Wetzel, Katia Urata, Andreas Hueni and Hermann Kaufmann",authors:[{id:"112006",title:"Dr.",name:"Christian",middleName:null,surname:"Rogass",fullName:"Christian Rogass",slug:"christian-rogass"},{id:"136899",title:"MSc.",name:"Katia",middleName:null,surname:"Urata",fullName:"Katia Urata",slug:"katia-urata"},{id:"136900",title:"Dr.",name:"Andreas",middleName:null,surname:"Hueni",fullName:"Andreas Hueni",slug:"andreas-hueni"},{id:"153729",title:"MSc.",name:"Daniel",middleName:null,surname:"Spengler",fullName:"Daniel Spengler",slug:"daniel-spengler"},{id:"153730",title:"Dr.",name:"Mathias",middleName:null,surname:"Bochow",fullName:"Mathias Bochow",slug:"mathias-bochow"},{id:"153731",title:"Dr.",name:"Karl",middleName:null,surname:"Segl",fullName:"Karl Segl",slug:"karl-segl"},{id:"153733",title:"Dr.",name:"Angela",middleName:null,surname:"Lausch",fullName:"Angela Lausch",slug:"angela-lausch"},{id:"153734",title:"Dr.",name:"Daniel",middleName:null,surname:"Doktor",fullName:"Daniel Doktor",slug:"daniel-doktor"}]},{id:"37516",title:"Differential Absorption Microwave Radar Measurements for Remote Sensing of Barometric Pressure",slug:"differential-absorption-microwave-radar-measurements-for-remote-sensing-of-barometric-pressure",signatures:"Roland Lawrence, Bin Lin, Steve Harrah and Qilong Min",authors:[{id:"114808",title:"Dr.",name:"Roland",middleName:null,surname:"Lawrence",fullName:"Roland Lawrence",slug:"roland-lawrence"},{id:"115434",title:"Dr.",name:"Bing",middleName:null,surname:"Lin",fullName:"Bing Lin",slug:"bing-lin"},{id:"115435",title:"Mr.",name:"Steven",middleName:null,surname:"Harrah",fullName:"Steven Harrah",slug:"steven-harrah"}]},{id:"37517",title:"Energy Efficient Data Acquistion in Wireless Sensor Network",slug:"data-acquistion-in-wireless-sensor-networks",signatures:"Ken C.K. Lee, Mao Ye and Wang-Chien Lee",authors:[{id:"116026",title:"Dr.",name:"Ken",middleName:null,surname:"Lee",fullName:"Ken Lee",slug:"ken-lee"},{id:"116027",title:"Mr.",name:"Mao",middleName:null,surname:"Ye",fullName:"Mao Ye",slug:"mao-ye"},{id:"116028",title:"Dr.",name:"Wang-Chien",middleName:null,surname:"Lee",fullName:"Wang-Chien Lee",slug:"wang-chien-lee"}]},{id:"37518",title:"Three-Dimensional Lineament Visualization Using Fuzzy B-Spline Algorithm from Multispectral Satellite Data",slug:"three-dimensional-lineament-visualization-using-fuzzy-b-spline-algorithm-from-multispectral-sate",signatures:"Maged Marghany",authors:[{id:"96666",title:"Prof.",name:"Maged",middleName:null,surname:"Marghany",fullName:"Maged Marghany",slug:"maged-marghany"}]},{id:"37519",title:"COMS, the New Eyes in the Sky for Geostationary Remote Sensing",slug:"coms-the-new-eyes-in-the-sky-for-geostationary-remote-sensing",signatures:"Han-Dol Kim, Gm-Sil Kang, Do-Kyung Lee, Kyoung-Wook Jin, Seok-Bae Seo, Hyun-Jong Oh, Joo-Hyung Ryu, Herve Lambert, Ivan Laine, Philippe Meyer, Pierre Coste and Jean-Louis Duquesne",authors:[{id:"114809",title:"Dr.",name:"Han-Dol",middleName:null,surname:"Kim",fullName:"Han-Dol Kim",slug:"han-dol-kim"},{id:"116949",title:"Dr.",name:"Gm-Sil",middleName:null,surname:"Kang",fullName:"Gm-Sil Kang",slug:"gm-sil-kang"},{id:"116950",title:"Mr.",name:"Pierre",middleName:null,surname:"Coste",fullName:"Pierre Coste",slug:"pierre-coste"},{id:"150218",title:"Mr.",name:"Do-Kyung",middleName:null,surname:"Lee",fullName:"Do-Kyung Lee",slug:"do-kyung-lee"},{id:"150219",title:"Dr.",name:"Kyoung-Wook",middleName:null,surname:"Jin",fullName:"Kyoung-Wook Jin",slug:"kyoung-wook-jin"},{id:"150221",title:"Mr.",name:"Seok-Bae",middleName:null,surname:"Seo",fullName:"Seok-Bae Seo",slug:"seok-bae-seo"},{id:"150222",title:"Mr.",name:"Herve",middleName:null,surname:"Lambert",fullName:"Herve Lambert",slug:"herve-lambert"},{id:"150223",title:"Mr.",name:"Ivan",middleName:null,surname:"Laine",fullName:"Ivan Laine",slug:"ivan-laine"},{id:"150224",title:"Mr.",name:"Philippe",middleName:null,surname:"Meyer",fullName:"Philippe Meyer",slug:"philippe-meyer"},{id:"150225",title:"Mr.",name:"Jean-Louis",middleName:null,surname:"Duquesne",fullName:"Jean-Louis Duquesne",slug:"jean-louis-duquesne"},{id:"150227",title:"Mr.",name:"Hyun-Jong",middleName:null,surname:"Oh",fullName:"Hyun-Jong Oh",slug:"hyun-jong-oh"},{id:"150228",title:"Dr.",name:"Joo-Hyung",middleName:null,surname:"Ryu",fullName:"Joo-Hyung Ryu",slug:"joo-hyung-ryu"}]},{id:"37520",title:"Hyperspectral Remote Sensing - Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas",slug:"hyperspectral-remote-sensing-using-low-flying-aircraft-and-small-vessels-in-coastal-littoral-are",signatures:"Charles R. Bostater, Jr., Gaelle Coppin and Florian Levaux",authors:[{id:"115690",title:"Dr.",name:"Charles",middleName:null,surname:"Bostater",fullName:"Charles Bostater",slug:"charles-bostater"}]},{id:"37521",title:"CSIR - NLC Mobile LIDAR for Atmospheric Remote Sensing",slug:"csir-nlc-south-africa-mobile-lidar-for-atmosphere-remote-sensing",signatures:"Sivakumar Venkataraman",authors:[{id:"103080",title:"Prof.",name:"Sivakumar",middleName:null,surname:"Venkatarman",fullName:"Sivakumar Venkatarman",slug:"sivakumar-venkatarman"}]},{id:"37522",title:"Active Remote Sensing: Lidar SNR Improvements",slug:"active-remote-sensing-lidar-snr-improvements",signatures:"Yasser Hassebo",authors:[{id:"111431",title:"Dr.",name:"Yasser",middleName:null,surname:"Hassebo",fullName:"Yasser Hassebo",slug:"yasser-hassebo"}]},{id:"37523",title:"Smart Station for Data Reception of the Earth Remote Sensing",slug:"smart-station-for-data-reception-of-the-earth-remote-sensing",signatures:"Mykhaylo Palamar",authors:[{id:"108533",title:"Dr.",name:"Mykhaylo",middleName:"Ivanovich",surname:"Palamar",fullName:"Mykhaylo Palamar",slug:"mykhaylo-palamar"}]},{id:"37524",title:"Atmospheric Propagation of Terahertz Radiation",slug:"atmospheric-propagation-of-terahertz-radiation",signatures:"Jianquan Yao, Ran Wang, Haixia Cui and Jingli Wang",authors:[{id:"106272",title:"Dr.",name:"Ran",middleName:null,surname:"Wang",fullName:"Ran Wang",slug:"ran-wang"},{id:"112459",title:"Prof.",name:"Jianquan",middleName:null,surname:"Yao",fullName:"Jianquan Yao",slug:"jianquan-yao"},{id:"112460",title:"Dr.",name:"Haixia",middleName:null,surname:"Cui",fullName:"Haixia Cui",slug:"haixia-cui"},{id:"112462",title:"Dr.",name:"Jingli",middleName:null,surname:"Wang",fullName:"Jingli Wang",slug:"jingli-wang"}]},{id:"37525",title:"Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters",slug:"road-feature-extraction-from-high-resolution-aerial-images-based-on-image-classification-and-gab",signatures:"Hang Jin, Marc Miska, Edward Chung, Maoxun Li and Yanming Feng",authors:[{id:"115888",title:"Dr.",name:"Hang",middleName:null,surname:"Jin",fullName:"Hang Jin",slug:"hang-jin"},{id:"137590",title:"Dr.",name:"Marc",middleName:null,surname:"Miska",fullName:"Marc Miska",slug:"marc-miska"},{id:"137591",title:"Prof.",name:"Edward",middleName:null,surname:"Chung",fullName:"Edward Chung",slug:"edward-chung"},{id:"137592",title:"Dr.",name:"Maoxun",middleName:null,surname:"Li",fullName:"Maoxun Li",slug:"maoxun-li"},{id:"137593",title:"Prof.",name:"Yanming",middleName:null,surname:"Feng",fullName:"Yanming Feng",slug:"yanming-feng"}]},{id:"37526",title:"Hardware Implementation of a Real-Time Image Data Compression for Satellite Remote Sensing",slug:"hardware-implementation-of-a-real-time-image-data-compression-for-satellite-remote-sensing",signatures:"Albert Lin",authors:[{id:"106741",title:"Mr.",name:"Albert",middleName:null,surname:"Lin",fullName:"Albert Lin",slug:"albert-lin"}]},{id:"37527",title:"Progress Research on Wireless Communication Systems for Underground Mine Sensors",slug:"progress-research-on-wireless-communication-systems-for-underground-mine-sensors",signatures:"Larbi Talbi, Ismail Ben Mabrouk and Mourad Nedil",authors:[{id:"113351",title:"Prof.",name:"Larbi",middleName:null,surname:"Talbi",fullName:"Larbi Talbi",slug:"larbi-talbi"}]},{id:"37528",title:"Cold Gas Propulsion System - An Ideal Choice for Remote Sensing Small Satellites",slug:"cold-gas-propulsion-system-an-ideal-choice-for-remote-sensing-small-satellites",signatures:"Assad Anis",authors:[{id:"111486",title:"Prof.",name:"Assad",middleName:null,surname:"Anis",fullName:"Assad Anis",slug:"assad-anis"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"71958",title:"Online Measurements in Welding Processes",doi:"10.5772/intechopen.91771",slug:"online-measurements-in-welding-processes",body:'The welding process is used by many manufacture companies in a wider range of applications. Many studies have been carried out to improve the quality and to reduce the cost of welded components. Part of the overheads is employed in the final inspection, which begins with a visual inspection, followed by destructive and nondestructive testing techniques. In addition to cost raises, a final inspection is conducted when the part is finished only. When a defect occurs during welding, it can be reflected in the physical phenomena involved: magnetic field, electric field, temperature, sound pressure, radiation emission, and others. Thus, if a sensor monitors one of these phenomena, it is possible to build a system to monitor weld parameters and quality.
For years, much has been done to predict problems in welding to make it a stable process capable of making union parts with minimal human interference. Despite various sensors used in welding processes, there is still no effective option able to identify, directly, the weld bead characteristics obtained during the process. This is a limiting factor in the process control because weld bead characteristics can only be determined after the completion of welding through testing (destructive or not) when no control action can be taken.
In the last decade, measurements of multiple sensors are used to estimate some geometric parameters of the weld bead, such as the weld penetration, reinforcement, and width, and to classify the process stability. These estimations can be made online1 and can be used to control the weld bead geometry formation. More recently, research has shown the possibility of estimating some microstructural characteristics and thermally affected zone dimensions, as shown in [1]. These estimations can be used to limit the control actions so as not to affect the desired characteristics of the weld bead. These techniques do not eliminate destructive testing but greatly reduce its use and prevent the defect formation or loss of characteristics.
In the welding processes, many variables can be used to control the geometry and quality of the final product. For this, other variables need to be measured or estimated. Some variables can be measured or modified online, directly in the welding power source, such as welding voltage and welding electric current intensity. Others can be measured using noncontact sensing methods, such as width and reinforcement of weld bead, drop volume, and electrical stick-out. But several variables, necessary to close the control loops, are difficult to measure online. This difficulty is due to the extreme conditions in the arc zone, because the electric arc is a powerful radiation emitter, in a long range of the frequency spectrum, including high temperature and visible light, generating steam and droplets of molten metal, coming from the electrode and the base metal. For these latter cases are necessary estimation methods based on models of welding processes that may include the power source and the robotic system used to move the torch or piece.
For the automation and control of complex manufacturing systems, a great deal of progress came up in the last decade, for precision and online documentation (bases for the quality control). With the advent of electrically driven mechanical manipulators and later the whole, relatively new, multidisciplinary mechatronics engineering, the need for information acquisition has increased. The acquisition is, in many cases, distributed through the system, with strong interaction between the robot and its environment. The design objective is to attain flexible and lean production. The requirement of real-time processing of data from multi-sensor systems with robustness, in an industrial environment, shows the need for new concepts on system integration.
Technology advancements seek to meet the demands for quality and performance through product improvements and cost reductions. An important area of research is the optimization of applications related to welding and the resultant cost reduction. The use of nondestructive tests and defect repair are slow processes. To avoid this, online monitoring and control of the welding process can favor the correction and reduction of defects before the solidification of the melted/fused metal, reducing the production time and cost [2].
Developments in microelectronics have led to rapid advances in welding power sources. With the use of fast microelectronic circuits, the speed of welding process control and welding parameter adjustment has been increased tremendously, and dynamic control over the arc and molten metal transfer have become possible. Research and development carried out by manufacturers of welding power sources focus on rapid optimal control of the welding parameters during welding. Modern welding sources are equipped with special control functions of arc and molten metal transfer, focusing on two basic areas: The first area of focus is on welding thin metal sheets (0.53 mm), and the second is on high-productivity thick metal sheet welding (over 5 mm) [3].
With continuing advancements in digital and sensor technology, new methods with relatively high accuracy and quick response time for the identification of perturbations during the welding process have become possible. Arc position, part placement variations, surface contaminations, and joint penetration are key variables that must be controlled to ensure satisfactory weld production [4]. The techniques related to welding process optimization are based on experimental methodologies. These techniques are strongly related to experimental tests and seek to establish relations between the welding parameters and welding bead geometry.
Researches related to adaptive systems for welding seek the improvement of welding bead geometry with direct (if based on monitoring sensors) or indirect monitoring techniques. The indirect monitoring systems are the more used, looking to link elements such as welding pool vibrations, superficial temperature distribution, and acoustic emissions to size, geometry, or welding pool depth [5]. The most used approaches in welding control are infrared monitoring, acoustic monitoring, welding pool vibrations, and welding pool depression monitoring as shown in [6]. The literature analysis made in [7] shows a similar conclusion, but adds vision techniques with the same level of use with infrared measurement.
The majority of modeling methods used in the past were based on the physics and chemical characteristics of the process components. This method needs great knowledge about welding processes, and, because of its complexity, these magnitudes are difficult to obtain and keep constant throughout the process, causing inaccuracies in the models. With the development of information processing capabilities, black box modeling methods were successfully used, as such the statistics and probabilities, but needs very complex models because of the nonlinearity of the welding process and the correlation between variables. Nowadays, the black box methods based on intelligent artificial algorithms are predominant as shown in [7], where 29% of models analyzed were obtained using intelligent artificial algorithms, 18% used image processing (which can include intelligent artificial algorithms such as deep learning methods), 12% used statistical methods, and only 2% used physics and chemical characteristics. Also, recently, big data and data mining algorithms are to be introduced in industrial applications.
This chapter focuses on the online measurement of main magnitudes in welding processes to close the control loop and allow the welding power source and robot parameters to be adjusted. These actions make it easier to meet the project requirements, reduce the cost of welding production, and increase productivity by reducing the number of parts rejected in final quality inspection. The next sections discuss the sensing and analysis techniques used to measure or estimate important variables in welding processes, with emphasis on the conventional gas metal arc welding (GMAW) process. A summary of its evolution is also shown, and some examples of algorithms to measure data processing are discussed.
Also, a novel modeling method, based on a sensor fusion algorithm, is shown. This method uses dynamic information of welding processes to improve the model response, rather than relying on static models used in research found in the literature. The method uses arc welding measurements and thermographic information to estimate the weld bead penetration. The algorithm obtains the thermographic features and supplies information about the amount and spatial distribution of the energy in the workpiece, minimizing the errors when multiple inflection points are found because it does not use the second derivative for the calculation of thermographic width. Besides, volume calculations are performed using the actual thermographic curve instead of the ideal Gaussian curve used in most research as an approximation value and using more complex equations. This approach uses only addition operations, simplifies calculations, and improves model accuracy, allowing its implementation in an embedded device. The actual application area is automatic control, arc welding, and sensor fusion research.
Automatic welding is affected by disturbances in the production parameters and welding conditions. Variations in components’ position and dimensions, weld joint misalignment, oil in the surface and other improprieties in materials, instability in welding wire speed, and shielding gas flow are examples of disturbances. However, by adding measuring systems, the control system may have a better performance when disturbances are found. For this purpose, the disturbances and affected variables must be measured or estimated online, to counteract the effects of the disturbances through the control actions. These variables can be different in each process type or change your significance in the process. Some variables can be measured directly from the welding power source or using indirect measuring techniques, and others need to be estimated from measured variables. This last group includes the weld bead depth or penetration.
In welding processes, the variables can be classified depending on whether they can be measured or modified and if these actions can be done online or offline. Responding to this classification, the variables in welding processes can be divided into five basic groups [8]:
Fixed, which cannot be modified by the operator but defined in the process design
Adjustable online, which can be modified during the process
Adjustable offline, which can be modified only before starting the process
Quantifiable online, which is measurable during the process
Quantifiable offline, which is measurable only after the process ended
An example of variables and groups for the constant voltage GMAW orbital process is shown in Figure 1, and some measurement techniques are described in the following sections.
Variables and classification groups to conventional constant voltage gas metal arc welding (GMAW) process (adapted from [8]).
The most common measurement variables in welding are related to the power supplied to the process by the unit of material length or area, and most are defined or measured by the power source. In conventional arc welding processes, these variables are electric voltage, intensity of electric current (also called simply amperage or welding current), and wire feed speed in processes with material addition. The parameters to control the waveform of voltage or electric current in the advanced arc welding process are important also. Other variables can be necessary to define and know, as gas flow, pre-gas time and post-gas time, source impedance, and time or position when the arc is open and closed (arc status). In laser welding processes, laser power, pulse rate, focal distance, and spot size are important too.
The modern welding power sources have one or various microcontrollers or microprocessors to control source operations, data acquisition, and communications. The communication interface usually implements a serial protocol or digital and analogical inputs and outputs to obtain or send information from an external supervisory control system (computer, programmable logic controller, and robotic system, among others). This interface can be used to synchronize work between power sources and robotic systems, other machines, control levels, or factory management. The welding variables, as voltage, electric current, and wire feed speed, can be measured and modified using this interface. Some commonly used standard network protocols are RS-232, RS-485, Modbus (RTU, TCP, or UDP), CAN Bus, DeviceNet, Field Bus, and Ethernet. Other companies define their protocol such as the Arclink developed by Lincoln Electric and SpeedNet by Fronius.
The communication protocol can be “open” or “proprietary.” In the first case, the user can communicate his control system directly with the power source using the communication protocol description. In the second case, he needs to buy and use proprietary software or hardware. The implementation of protocols used in modern power sources is serial2, and information is obtained or sent in digital format.
The old welding power sources can have an interface with digital and analogical inputs and outputs, to let the monitoring and control of the source. These power sources need a dedicated data acquisition and control system to convert the analog values in digital information and vice versa. The user, using this system, can monitor and control the power source operation partially or fully. To design or select the system, the sampling time, resolution and range of analogical inputs and outputs, and range and type of digital inputs and outputs, based on the power source characteristics and application requirements, should be considered.
Some manufacturers offer hardware interfaces to translate the information from serial protocol to digital and analogical inputs and outputs. If you do not have conditions to use a serial protocol, these interfaces can help to get and send information to power sources. These systems can be more slow, inaccurate, and inefficient than serial protocol, because of the sequential conversions from analogical to digital (on the source), from digital to analogical (on the interface), and from analogical to digital again (on the acquisition system). The ROB 5000 of Fronius, shown in Figure 2, is an example [9].
Fronius interface to translate the information between serial protocol and data acquisition system with digital and analogical inputs and outputs.
Other variables that must be measured to control robotic welding processes are those that characterize the relative movement and position between the piece and torch. These variables can be obtained from the position control system of the robot, but many times an initial or absolute reference is necessary.
The relative movement between the piece and torch is defined by the welding speed. Sometimes, the torch is coupled to the robot manipulator and moves while the piece is fixed. Other systems fixed the torch and move the piece. More complex robotic systems move the torch and piece. In all cases, it is important to consider the relative speed between the torch and the piece to obtain the welding speed. This speed can be calculated using the torch and the piece speed obtained from the robotic system. On plane welding, the speed can be expressed in longitude per time unit, such as
In orbital welding, the orbital angle is used to indicate the torch position in the polar coordinate system with the origin on the pipe axis. This measurement is very important because the welding conditions can be different for each orbital position. Its value must be obtained from the robotic system too. In this case, it is possible to express the welding speed using orbital angle per time unit, such as
The forehand angle or attack angle is the torch angle side view relative to the base metal surface, which is the angle in direction of torch travel. The work angle is defined by the torch angle end view relative to the base metal surface as shown in Figure 3. These angles can be fixed before starting the welding and staying constant or can be controlled by the robotic system. In the second case, the angles can be calculated or obtained from the robotic system also.
Welding speed, torch angles, and orbital angle (adapted from [10]).
The contact tip-to-work distance (CTWD) can be obtained from the robot control system related to torch or piece position, or it can be measured with a laser distance sensor. Other variables, such as electrical stick-out or electrode extension and arc length (see Figure 4) need more complex procedures to obtain a measurement because they depend on the fusion rate. All these variables are expressed in longitude unit, such as
Contact tip-to-work distance and electrical stick-out (or electrode extension) differences (adapted from [11]).
The first method to obtain the CTWD is more economical but has low accuracy. The zero references of the robotic system are calibrated with the workpiece position, but the surface variations and thickness of the material can affect the accuracy of the value. In arc welding processes, small variations in CTWD can affect the electric resistance of the arc, and consequently, the welding current and the heat input can be significantly affected. For example, reduction of the arc length causes the increase of heat input (and more current because it reduces the equivalent resistance of arc) which makes the wire electrode melt more quickly and thereby restore the original arc length.
A laser sensor can be more accurate, but the measurement point needs to be selected correctly. This sensor measures the distance to a point on the surface of the base metal of the piece, and it has three basic principles of operation: time of flight, phase comparison, or triangulation method.
In the time of flight measurement principle, shown in Figure 5, a laser diode produces short pulses which are projected onto the target. The light reflected from the target is recorded by the sensor element. The time of flight of the light pulse to the target and back determines the measured distance. The integrated electronics in the sensor compute the distance using the time of flight. Sensors using this principle are not sensitive to external light.
Time of flight measurement principle (adapted from [12]).
In the phase comparison measuring principle, a high-frequency modulated laser light with low amplitude is transmitted to the target as shown in Figure 6. Depending on the distance of the object, it changes the phase relationship between transmitted and received signals. Sensors using this principle operate with high accuracy for measurement distances up to 150 m.
Phase comparison measuring principle (adapted from [12]).
In the triangulation method, shown in Figure 7, the laser beam is projected and reflected from a target surface to a collection lens. The lens focuses an image of the spot on a linear array camera. The camera views the measurement range from an angle at the center of the measurement range. The position of the spot image on the pixels of the camera is then processed to determine the distance to the target. The camera integrates the light falling on it, so long exposure times allow greater sensitivity to weak reflections.
Triangulation measuring principle (adapted from [13]).
The most used laser emitter distance sensors are in the wavelengths of red color (close to 658
Other research methods use the voltage and current feedback signals from the welding process, computing the minimum resistance during the short circuit period, and uses this value to estimate the CTWD after applying a correction factor for the duration of the short circuit. The effect of wire feed speed, actual CTWD, and shielding gas on the correction factor is determined experimentally [14].
The CTWD can be calculated with the same bead profile obtained from the laser profilometer. The distance between the torch contact tip and the sensor reference is fixed and known. The CTWD is the difference between the baseline value and this distance, as shown in Figure 8.
Contact tip-to-work distance measurement [8].
The quality of arc welding is commonly described by the geometry of the molten weld pool and the weld bead because the mechanical properties of the welding joint are reflected in these geometry characteristics. The geometry of the weld bead is a set of parameters defined in the design stage, and to achieve the required quality, it should be measured and controlled throughout the process. The parameters or variables which define the most important characteristics of the geometry of the weld bead (including the weld pool) are the weld bead width, the weld bead reinforcement, and weld bead depth or weld bead penetration, as shown in Figure 9.
Geometric dimensions of the weld bead: (a) cross-section, (b) top view, (c) side view, and (d) side view of a longitudinal cut [7].
The visual information of the molten weld pool is used by expert operators to control the welding process in manual welding. In automatic welding processes, this information can be used to improve the control behavior and achieve the desired quality in the welding joint. In arc welding processes, the geometry parameters are governed by many factors, such as welding current, welding voltage, wire feed speed, welding speed, and the contact tip-to-work distance. Then, for successful control of geometric variables, it is necessary to provide feedback to the control system.
For feedback implementation, it is important to know that the process’ adverse environmental conditions, in the vicinity of the electric arc and the molten pool, would damage the measuring instruments that require physical contact with the surface of the piece. These conditions make the measuring of the weld bead geometry a difficult task using conventional measuring principles. Instead, noncontact techniques have been developed and employed successfully.
For this purpose, vision sensing is a promising solution. One common method is formed by a laser beam, which draws one or more lines on the surface to be measured, the image is filtered to obtain only the wavelength emitted by the laser, and a camera or matrix image sensor captures the line created by the laser. Subsequently, using image processing algorithms and triangulation techniques, a profile of the piece with the required information is obtained.
This system, referred to as a laser scanner or profile sensor, is shown in Figure 10. In it, the optical system projects the diffusely reflected light of this laser line onto a highly sensitive sensor matrix. From this matrix image and the angle between the camera and the laser diode (
Two-dimensional laser triangulation principle [7].
Another way to obtain a three-dimensional profile is by using a laser pattern with a dot matrix or line grill, as shown in Figure 11. The two-dimensional information is processed using triangulation techniques to obtain a three-dimensional profile. In [15], the weld pool surface deformation is obtained from the projection pattern using the deformation of the lines (see Figure 11b) or the distance between points (see Figure 11c). Other implementations of this method are shown in [16, 17, 18]. In these methods, the uncertainty to recognize the real position is a problem. For this reason, a point of the pattern dot is intentionally missed to serve as a reference.
Three-dimensional profile of the weld pool surface using structured light and triangulation method: (a) diagram of measure system, (b) reflected image using line laser pattern, and (c) reflected image using dot laser matrix (adapted from [15]).
It can also use the same measuring principle to obtain a profile of the weld joint before joining the one. This allows the implementation of algorithms to define or adjust the trajectory to be followed by the torch (seam-tracking algorithms). Similarly, it is possible to estimate the amount of material required (deposition rate) for the formation of a bead with the desired dimensions [7].
The use of video cameras to measure the weld bead width and reinforcement is possible too, as is shown in [19, 20, 21], but these methods need optimal light conditions and are difficult to apply in the industrial environment.
These principles cannot be applied to penetration measurement, and this variable could be estimated from a different way. Due to the complexity of the measurement methods, we are going to dedicate another section to show some estimation methods of this magnitude.
Total penetration in welding processes is important to ensure weld quality. When the total penetration takes place, the melt weld pool crosses to the bottom side of the workpiece, as shown in Figure 12. The depth of penetration of the weld bead can be determined by nondestructive testing techniques such as ultrasound or X-ray. However, the portability and robustness of these traditional instruments are not a good option for the harsh conditions of the process and to develop an online measuring system. Because of that, many research works attempt to detect total or partial penetration with other methods, but few obtain a useful value for the control system algorithm.
Total and partial penetration in the weld bead [7].
Measurement of the backside width of the weld pool can be used to ensure total penetration, but this method is difficult because of the reduced space, limited movements, and restricted access, among other conditions, that make it impossible to place the sensors under the weld pool.
The front side of the weld pool offers information about the total penetration status. This information includes the temperature of arc and weld pool, arc voltage, arc light, arc sound, geometry parameters, oscillation frequency, and resonance frequency, among others. For obtaining this information, indirect sensing technologies are used. These measuring technologies can be classified as conventional, vision, and multi-sensor or sensor fusion technologies, as shown in Figure 13.
Indirect sensing technologies used to monitor the bead and weld pool [7].
Conventional sensing technologies monitor parameters closely related to the weld bead and the weld pool geometry. It includes ultrasound, infrared thermography, weld pool oscillation, arc sound, and X-ray, among others. Vision sensing technologies obtain these features as skilled welders do. It can be divided into two-dimensional and three-dimensional sensing. These methods are applied to obtain the geometric shape of the weld pool and weld bead with good results. Sensor fusion integrates several sensing technologies in the same monitoring system. Figure 14 shows the literature review statistics, obtained in [7], about the use of indirect monitoring technologies to estimate the weld bead geometry.
Literature review statistics about indirect monitoring technologies used to obtain measurements of the weld bead geometry [7].
Some of these measuring methods need a model to obtain the desired information from the process. To obtain a representative model, the estimators have been used successfully under specific conditions. But it is imperative to create a model that can be easily programmed and fed into the control system. The model must have satisfactory precision in the prediction of the depth of the weld bead and cover all of the positions used in the welding work. It is very useful if it also represents a wide range of thicknesses of the material, but this is not always possible.
The research about modeling the weld bead depth tries to relate this variable with the welding electric current intensity, welding voltage, wire feed speed, and welding speed. Documents analyzed in [7], show that the research is making mainly in horizontal or flat welding to obtain static models and the researchers are using artificial intelligence algorithms. The most used methods to estimate the weld bead geometry are artificial neural networks, fuzzy logic, and their combinations. This group represents 28% of the works, as shown in Figure 15. Image processing and statistical techniques such as multiple regression analysis, least squares, or factorial design are also frequently used. All these make up 58% of the total of the publications found.
Literature review statistics about analysis method used to estimate the weld bead geometry [7].
In the welding process, the thermal energy is supplied through an electric current and stored in the material. Due to thermal inertia, dynamic models can be a better representation of the process. In these models, the historical values of the welding parameters are also selected as inputs. Dynamic behavior is essential to estimate the current and future state form to the past state. Despite this, [7] shows dynamic models in a minority. The main cause of the selection of the static model is the difficulty in obtaining a continuous data set of the weld bead depth. One way to obtain it is to perform a longitudinal cut and a macrographic analysis on the weld bead with an image processing algorithm as made in [22]. In the traditional cross-section cut, it is not possible to obtain enough information to make a dynamic model.
Measurements obtained by several sensors or measuring systems can be used to estimate the values of other or the same magnitudes. These techniques that combined different sensing technologies or information sources to obtain better sensing results are named sensor fusion or fusion sensory data. The sensor fusion is a multilevel process that needs a model to combine the information and describe the static or dynamic behavior of processes.
There are applications in different spheres such as aerial and ground navigation of mobile robots, systems for environmental monitoring, visual sensor networks, medicine, security, fault detection, and quality control, among others, as shown in [23]. This is a relatively young research area, but it is the third method used for the indirect monitoring of the welding process, as shown in Figure 14. In recent years, these methods have been studied to achieve effective welding and sense of the weld bead.
The sensor fusion can be classified in different ways as discussed in [24]. One of the most representative classifications for welding processes is according to the relationships of the input data, which defines how the information relates between the sensors. It can be complementary, competitive or redundant, and cooperative, as shown in Figure 16, but it is very common to find more than one way of working on the same group of sensors.
Classification according to the relationships of the input data (adapted from [24]).
Figure 16 shows four levels of the fusion process. The following list exemplifies welding process parameters:
Information level represents the input magnitudes and zones that are measured by sensors, for example, the weld bead and weld pool dimensions.
Source of information is the sensors installed on the process, for example, the video and thermographic cameras.
The fusion of information is the algorithms used to obtain the fused information, for example, an image processing and neural network algorithms.
Fused information is the final result, for example, the weld bead depth.
The complementary fusion is about fusing incomplete information that is obtained starting from different sources. This is the case in that several sensors are measuring different parts of an atmosphere or phenomenon, covering a bigger area, and allowing a more complete and more global vision of the process. For example, you can combine the weld pool thermographic information (A) obtained from an infrared camera (S1) and weld bead dimensions (B) obtained from a video camera (S2) to calculate the weld pool dimensions (a + b).
In competitive or redundant fusion, all the sensors are monitoring the same area, working redundantly and competitively. These sensors can have similar or different measurement principles. An example is the dimensional information (B) about the weld bead obtained from two video cameras (S2 and S3) used to calculate the weld bead dimensions (b).
Cooperative or coordinated fusion uses the information from independent several sensors to obtain new information, for example, the combination of the information of the weld bead (C) obtained from a vision system (S4) and pyrometer (S5) to estimate the weld bead penetration value (c).
A cooperative sensor fusion algorithm is used in [22] to obtain an estimator of the weld bead depth
The artificial neural network has 8 neurons in the input layer, 12 neurons in the hidden layer, and 1 in the output layer. The activate function is the hyperbolic tangent sigmoid transfer function. The network training should be done with experimental measurements of the parameters of input and output and by using the backpropagation algorithm. A block diagram is shown in Figure 17.
Weld bead depth estimator block diagram, based on artificial neural network, developed in [22].
The thermographic matrix, supplied by the thermographic camera, is processed with a moving average filter to obtain the thermographic peak
Features extracted from an infrared image: (a) three-dimensional curve, (b) front view of the curve, (c) intersection between the boundary plane and three-dimensional curve, (d) and the calculation of the width, area and thermographic volume.
The base plane is calculated as the average of 10% of the values on the left and right sides, as shown in Figure 18b. The boundary plane is 10% above the base plane. The sum of active pixels in the intersection plane between the thermographic surface and the boundary plane is the area. The sum of the thermographic values within the intersection plane is the thermographic volume, shown in Figure 18d. This algorithm was optimized for implementation in embedded devices.
A contribution of the method is the minimization of errors when multiple inflection points are found because it does not use the second derivative for the calculation of thermographic width. Also, volume calculations are performed using the actual thermographic curve instead of the ideal Gaussian curve used in most research as an approximation value and using more complex equations. This approach uses only addition operations, simplifies calculations, and improves model accuracy.
The weld bead depth profile to network training was obtained using an image processing algorithm on the macrographic picture of the longitudinal cut of the piece. Figure 19a shows 610 measurements of weld bead depth in a yellow line and the base metal surface in a red line. The model has a fit of 0.99844, a performance or median square error (MSE) of
Results obtained in the estimation of the weld bead depth in [22]: (a) weld bead depth longitudinal profile and (b) model response and experimental measurements. In the sample axis, each value corresponds to a position in the piece.
It can be noticed that accurate sensing results were obtained based on multi-sensor information fusion technology, due to more weld pool information and effective information fusion techniques.
An analysis made in [7] about techniques and methods used for measurement and estimation of the geometry of the weld bead found publications and patents since 1971, but it is not until the 1980s that a greater interest in this subject was observed. Recently, in the last decade, the number of publications is increased. Figure 20 shows this growing interest.
Evolution of the number of publications about measurement and estimation of the weld bead geometry (adapted from [7]).
The technological development and cost reduction of sensors and the need for welding process control in an industrial environment (robotic welding) stimulated the number of scientific works about these topics.
An example is the development of infrared sensors that since the 1970s were available but in the 1980s were given classified contracts by the US Department of Defense to Honeywell and Texas Instruments to develop uncooled infrared sensor technology. In 1992, the US Government de-classified this technology for commercial products, allowing the sale of their devices to foreign countries, but kept close the manufacturing technologies. In the next decade, several countries developed uncooled imaging systems [25] with a drastic reduction of uncooled array cost. This evolution can be observed in Figure 21 with a significant increase in scientific publications in this decade.
Evolution of techniques used to measure the weld bead geometry [7].
The estimation of geometry weld bead using acoustic signal and vision techniques shows more activity in the last 20 years because of the great development of cameras, audio systems, and digital signal processor devices with high speed and quality, little size, and low cost.
In Table 1, some commonly used methods to obtain measurements of welding processes online are compared. The comparison criteria are the cost of implementation and the accuracy of the method. Both criteria are evaluated as low, medium, or high. In the table, a blank cell indicates a method that is not used to measure a specific variable. Some methods are used in conjunction with other measurement methods to obtain or estimate the value of the variable. Various measurement methods can be combined using a sensor fusion technique.
Variable | Costa/accuracyb of measuring method | ||||||||
---|---|---|---|---|---|---|---|---|---|
From welding power source | From robotic system | Laser distance sensor | Laser scanner | Structural lightc | Pyrometerd | Thermographic camerad | Acoustic signald | Weld pool oscillationc | |
Welding voltage | L/H | ||||||||
Welding current | L/H | ||||||||
Wire feed speed | L/M | ||||||||
Welding speed | L/H | ||||||||
Welding angle | L/H | ||||||||
Orbital angle | L/H | ||||||||
Contact tip-to-work distance | L/M | M/H | H/H | ||||||
Welding joint dimensions | H/H | M/H | |||||||
Weld bead width | M/M | H/H | M/H | ||||||
Weld bead reinforcement | M/M | H/H | M/H | ||||||
Weld bead penetration | M/M | H/M | L/L | M/L |
Comparison between the most used methods to measure the variables of welding processes, considering the cost and accuracy of the method.
L—Low cost; M—medium cost; and H—high cost.
L—Low accuracy; M—medium accuracy; H—high accuracy.
This method is used together with one or several video cameras.
This method is used alone or together with welding power source measurements (e.g., arc variables).
The analysis techniques most used in the last century were regression models, least mean square algorithms, Kalman filter, and other statistical methods. In the last few years, the principal techniques used are the artificial neural network, fuzzy logic, and neuro-fuzzy. The intense development of image processing algorithms was observed in the last 20 years.
The correct selection of measuring techniques and the use of sensor fusion algorithms, combined with indirect measurement techniques, can help to reduce the cost of welding production and increase productivity by the detection or prediction of many welding defects or set point deviations. These measurements allow online adjusting of the welding power source and robot parameters in a closed control loop. Online estimation of variables that cannot be measured improves control systems and reduces the number of parts rejected in the final quality inspection.
To take advantage of a modern welding power source, it is important to equip the monitoring and control system with serial communication capabilities. The modeling of estimators is a critical step to obtain an accurate measuring system, and dynamic models have a better representation of the welding process than static models due to thermal inertia of the process. Vision and thermographic measuring techniques, image processing, and neural network algorithms, despite consuming more computing resources, are the most used to estimate the weld bead geometry, and excellent results have been observed.
The research on sensor fusion algorithms is grown. Following this trend, in this work a novel modeling method that uses arc welding measurements and thermographic information to create a dynamic model to estimate the weld bead penetration is presented. This new approach obtains information about the amount and spatial distribution of the energy in the workpiece and uses only addition operations, simplifies calculations, and improves model accuracy. A satisfactory solution was shown to be applied in welding automatic control using computers or embedded devices.
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\\n\\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\\n\\nAUTHOR'S WARRANTY
\\n\\nThe Author and Co-Authors confirm and warrant that the Work does not and will not breach any applicable law or the rights of any third party and, specifically, that the Work contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy.
\\n\\nThe Author and Co-Authors confirm that: (i) the Work is their original work and is not copied wholly or substantially from any other work or material or any other source; (ii) the Work has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) Authors and any applicable Co-Authors are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) Authors and any applicable Co-Authors have not assigned, and will not during the term of this Publication Agreement purport to assign, any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\\n\\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\\n\\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\\n\\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\\n\\nTERMINATION
\\n\\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
\\n\\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\\n\\nIntechOpen’s DUTIES AND RIGHTS
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen agrees to provide publishing services which include: managing editing (editorial and publishing process coordination, Author assistance); publishing software technology; language copyediting; typesetting; online publishing; hosting and web management; and abstracting and indexing services.
\\n\\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\\n\\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\\n\\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
\\n\\nMISCELLANEOUS
\\n\\nFurther Assurance: The Author shall ensure that any relevant third party, including any Co-Author, shall execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\\n\\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\\n\\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\\n\\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\\n\\nVariation: No variation of this Publication Agreement shall have effect unless it is in writing and signed by the parties, or their duly authorized representatives.
\\n\\nSeverance: If any provision, or part-provision, of this Publication Agreement is, or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted. Any modification to, or deletion of, a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\\n\\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\\n\\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\\n\\nPolicy last updated: 2018-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'When submitting a manuscript, the Author is required to accept the Terms and Conditions set out in our Publication Agreement – Monographs/Compacts as follows:
\n\nCORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\nSubject to the following Article, the Author grants to IntechOpen, during the full term of copyright, and any extensions or renewals of that term, the following:
\n\nThe foregoing licenses shall survive the expiry or termination of this Publication Agreement for any reason.
\n\nThe Author, on his or her own behalf and on behalf of any of the Co-Authors, reserves the following rights in the Work but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Work as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\n\nThe Author, and any Co-Author, confirms that they are, and will remain, a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\n\nSubject to the license granted above, copyright in the Work and all versions of it created during IntechOpen's editing process, including all published versions, is retained by the Author and any Co-Authors.
\n\nSubject to the license granted above, the Author and Co-Authors retain patent, trademark and other intellectual property rights to the Work.
\n\nAll rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the specific approval of the Author or Co-Authors.
\n\nThe Author, on his/her own behalf and on behalf of the Co-Authors, will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Work as a consequence of IntechOpen's changes to the Work arising from the translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits as determined by IntechOpen.
\n\nAUTHOR'S DUTIES
\n\nWhen distributing or re-publishing the Work, the Author agrees to credit the Monograph/Compacts as the source of first publication, as well as IntechOpen. The Author guarantees that Co-Authors will also credit the Monograph/Compacts as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Work.
\n\nThe Author agrees to:
\n\nThe Author will be held responsible for the payment of the agreed Open Access Publishing Fee before the completion of the project (Monograph/Compacts publication).
\n\nAll payments shall be due 30 days from the date of issue of the invoice. The Author or whoever is paying on behalf of the Author and Co-Authors will bear all banking and similar charges incurred.
\n\nThe Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Work worldwide for the full term of the above licenses, and shall provide to IntechOpen, at its request, the original copies of such consents for inspection or the photocopies of such consents.
\n\nThe Author shall obtain written informed consent for publication from those who might recognize themselves or be identified by others, for example from case reports or photographs.
\n\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\n\nAUTHOR'S WARRANTY
\n\nThe Author and Co-Authors confirm and warrant that the Work does not and will not breach any applicable law or the rights of any third party and, specifically, that the Work contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy.
\n\nThe Author and Co-Authors confirm that: (i) the Work is their original work and is not copied wholly or substantially from any other work or material or any other source; (ii) the Work has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) Authors and any applicable Co-Authors are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) Authors and any applicable Co-Authors have not assigned, and will not during the term of this Publication Agreement purport to assign, any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\n\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\n\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\nTERMINATION
\n\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
\n\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\n\nIntechOpen’s DUTIES AND RIGHTS
\n\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
\n\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen agrees to provide publishing services which include: managing editing (editorial and publishing process coordination, Author assistance); publishing software technology; language copyediting; typesetting; online publishing; hosting and web management; and abstracting and indexing services.
\n\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\n\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
\n\nMISCELLANEOUS
\n\nFurther Assurance: The Author shall ensure that any relevant third party, including any Co-Author, shall execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\n\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\nVariation: No variation of this Publication Agreement shall have effect unless it is in writing and signed by the parties, or their duly authorized representatives.
\n\nSeverance: If any provision, or part-provision, of this Publication Agreement is, or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted. Any modification to, or deletion of, a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\n\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\n\nPolicy last updated: 2018-09-11
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