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Relevant bibliographies by topics / Welding – Automation

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Academic literature on the topic 'Welding – Automation'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Welding – Automation"

1

YAMAMOTO, Hideyuki. "Change of Welding Automation." JOURNAL OF THE JAPAN WELDING SOCIETY 77, no. 6 (2008): 547–48. http://dx.doi.org/10.2207/jjws.77.547.

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Beck, Chris. "Welding People and Automation." Manufacturing Management 2021, no. 7 (July 2021): 22–23. http://dx.doi.org/10.12968/s2514-9768(22)90472-7.

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Sheet metal volume manufacture and fabrication specialists, Contracts Engineering, entered 2020 with an increasing demand for its services. In order to boost productivity and meet this rise in orders, it made its first foray into the world of welding automation

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Lendel, I. V., V. A. Lebedev, S. Yu Maksimov, and G. V. Zhuk. "Automation of welding processes with use of mechanical welding equipment." Paton Welding Journal 2017, no. 6 (June 28, 2017): 86–91. http://dx.doi.org/10.15407/tpwj2017.06.16.

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Meng, Qing Guo, Ying Yin, and Xiao Jun Guo. "Aluminium Alloy Welding the Weld Stress Deformation and Control." Applied Mechanics and Materials 577 (July 2014): 86–89. http://dx.doi.org/10.4028/www.scientific.net/amm.577.86.

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Welding technology, as one of the process of manufacturing material in the permanent link, are already widely used in various kinds of aerospace ground launch equipment. Modern manufacturing industry is in rapid development phase, the welding automation is inevitable trend of production, which not only can greatly improve the efficiency of welding, but also what more important is to ensure the welding quality, thus to improve the operating environment. Modern welding is developing in the direction of mechanization, automation, intelligence, robot welding has become an important symbol of welding automation technology modernization. This paper focuses on regularity and influence of welding deformation, the welding deformation and deformation control, control and eliminate problems for research.

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Okubo, M. "Automation of steel bridge welding." Welding International 2, no. 10 (January 1988): 907–12. http://dx.doi.org/10.1080/09507118809447576.

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Rooks, Brian W. "Building welding expertise into automation." Industrial Robot: An International Journal 13, no. 1 (January 1986): 26–28. http://dx.doi.org/10.1108/eb004938.

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Aldalur, Eider, Alfredo Suárez, David Curiel, Fernando Veiga, and Pedro Villanueva. "Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints." Metals 13, no. 9 (August 29, 2023): 1532. http://dx.doi.org/10.3390/met13091532.

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The automation of welding processes requires the use of automated systems and equipment, in many cases industrial robotic systems, to carry out welding processes that previously required human intervention. Automation in the industry offers numerous advantages, such as increased efficiency and productivity, cost reduction, improved product quality, increased flexibility and safety, and greater adaptability of companies to market changes. The field of welding automation is currently undergoing a period of profound change due to a combination of technological, regulatory, and economic factors worldwide. Nowadays, the most relevant aspect of the welding industry is meeting customer requirements by satisfying their needs. To achieve this, the automation of the welding process through sensors and control algorithms ensures the quality of the parts and prevents errors, such as porosity, unfused areas, deformations, and excessive heat. This paper proposes an intelligent and adaptive system based on the measurement of welding joints using laser scanning and the subsequent analysis of the obtained point cloud to adapt welding trajectories. This study focuses on the optimization of T-joints under specific welding conditions and is intended as an initial implementation of the algorithm, thus establishing a basis to be worked on further for a broader welding application.

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Noruk, J., and J. P. Boillot. "Six Sigma Methodology Used To Improve Ship Welding." Journal of Ship Production 24, no. 02 (May 1, 2008): 65–71. http://dx.doi.org/10.5957/jsp.2008.24.2.65.

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Nowhere in manufacturing is the old axiom of "Do the job right the first time" more true than in the welding operations in a shipyard. The welding process needs to be optimized to achieve high productivity while maintaining quality levels required by stringent welding standards. However, even with the most fine-tuned process, the welder or welding machine operator can be "done in" by upstream operations where the material is prepared for welding. This paper discusses how new welding automation equipment can be used before, during, and after the weld is made to reduce overall variability in the process. Reduced variability results in higher levels of productivity and quality. Intelligent sensors have been combined with different levels of automation including mechanized, machine, and robots to facilitate flexible and portable automation capable of multitasking processing.

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Patil, Rahul S. "Productivity Improvement using Automation in Conveyor Roller Welding." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 146–59. http://dx.doi.org/10.22214/ijraset.2021.34898.

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In the industrial workplace, automation enhances productivity and quality while reducing errors and waste, boosting safety, and providing greater flexibility to the manufacturing process. Industrial automation, in the end, results in enhanced safety, dependability, and profitability. Increased productivity is achieved by automating the welding machine for conveyer rollers. Many departments are included in this project like design, fabrication, testing, etc. The movement of welding torch with respect to workpiece can be automated with the help of pneumatics along with use PLC control system. Accuracy is the key factor on which the rate of success of the project can be measured. Hence, tried to standardized the project as much as possible by using standard data and equipment. Automation systems are increasingly displacing humans in the workplace. One of the advantages is that the human workforce will have more time to focus on more innovative projects as a result of the transition. All the required parameters are to be considered while designing the machine components. According to design, fabrication is done with the guidance of experts from the company as well as project guide.

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Lee, Hyung Won, Jiyoung Yu, Gwang-Gook Kim, Young-Min Kim, Insung Hwang, Seung Hwan Lee, and Dong-Yoon Kim. "Convolutional Neural Network Model for the Prediction of Back-Bead Occurrence in GMA Root Pass Welding of V-groove Butt Joint." Journal of Welding and Joining 39, no. 5 (October 30, 2021): 463–70. http://dx.doi.org/10.5781/jwj.2021.39.5.1.

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Gas metal arc (GMA) welding is widely used in the machinery industry. The quality of a welded joint is affected by the penetration of root pass welding in the V-groove joint. Automation using GMA welding is continuously required, and root pass welding automation is required to automate the entire welding process. In particular, the development of a prediction model that can ensure full penetration back-bead is required for the automation of root pass welding. In this study, a convolutional neural network (CNN) model was applied to predict the occurrence of back-bead in V-groove butt joint GMA root pass welding. The bead profile was measured using a laser vision sensor system and it was used as the input data for the prediction model, and the bead occurrence was used as the output data for the model. A total of 12,873 bead profiles were extracted and pre-processed through cutting, resizing, and thresholding. The CNN model consists of nine layers, and performs three convolution and two pooling operations. The accuracy of the prediction model was 99.5%, and through this study, it was demonstrated that the quality of root-pass welding can be controlled by using convolutional neural network and it can contribute to automation.

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Dissertations / Theses on the topic "Welding – Automation"

1

Tan, Colin C. M. "Automation of some aspects of TIG welding." Thesis, University of Liverpool, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235509.

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Holway, Bruce William. "The development of a computer integrated welding workcell." Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/16827.

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McCormick, James Leo. "An optical profile sensor for robotic weld seam tracking." Thesis, Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/16852.

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Sicard, Pierre. "Adaptive welding and seam tracking using laser vision." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63837.

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Cavanough, Gary L. "Automated weld quality monitoring." Thesis, Queensland University of Technology, 1994.

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Byrne, N. J. "Automation of MIG (Metal Inert Gas) welding equipment using microprocessors." Thesis, University of Liverpool, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380072.

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Bicknell, Andrew Keith. "Sensors for the top face monitoring of weld pools." Thesis, University of Liverpool, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316687.

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Scotti, A. "Process modelling to establish control algorithms for automated GMAW." Thesis, Cranfield University, 1991. http://dspace.lib.cranfield.ac.uk/handle/1826/10518.

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The feasibility of fully automatic GMAW processes may rely on the development of sophisticated equipment to emulate the manual welding torch oscillation pattern or on the development of high level methods of control to prevent the appearance of defects, especially the lack of sidewall fusion. An intermediate solution is to optimise the weaving parameters of a conventional pattern oscillator in such a way as to minimise the level of rejection. A prototype of a computerised system to work with Pulsed-GMAW equipment, in the vertical-up position, was proposed to produce a minimal level of rejection for welds in plates up to 25 mm thick. The system basically consists of optimised mode control algorithms, based on theoretical and experimental models of weld pool behaviour. Three tasks are performed by the system; the selection of parameters for an optimum working point, an off-line simulation of the operation and real-time error monitoring of the process. Statistical experimental modelling was applied in order to build most of the optimised models, because of the large number of variables to be treated and their complex inter-correlation. The welding variables were correlated with single responses. Partial and Correlation Analysis techniques were used to discover the relationship between the variables and the responses. Regression Analysis was then applied as a means of obtaining the 'weight' of the most significant variables. Finally, since some variables were found to be collinear, a corrective technique for biased variables was employed. Acceptance criteria for bead shapes were proposed and assessed. The effect of the oscillation parameters and other welding variables on the bead formation was analyzed and an operational 'envelope' for the parameters determined. A theoretical approach to predict the occurrence of poorly shaped beads, due to the lack of metal bridge between the joint walls, was successfully developed and applied in parallel with the statistical experimental methods. Equations for optimising the bead shape and for determining the operational envelope contours were subsequently generated and evaluated. An extension of the system to an actual adaptive control scheme was discussed and sensors and signals to be used were evaluated. Finally, a process instability phenomenon in long test plates was identified and investigated. This instability may prevent the use of GMA W in some conditions in the vertical-up position.

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Tam, Joseph. "Methods of Characterizing Gas-Metal Arc Welding Acoustics for Process Automation." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/859.

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Recent developments in material joining, specifically arc-welding, have increased in scope and extended into the aerospace, nuclear, and underwater industries where complex geometry and hazardous environments necessitate fully automated systems. Even traditional applications of arc welding such as off-highway and automotive manufacturing have increased their demand in quality, accuracy, and volume to stay competitive. These requirements often exceed both skill and endurance capacities of human welders. As a result, improvements in process parameter feedback and sensing are necessary to successfully achieve a closed-loop control of such processes.

One such feedback parameter in gas-metal arc welding (GMAW) is acoustic emissions. Although there have been relatively few studies performed in this area, it is agreed amongst professional welders that the sound from an arc is critical to their ability to control the process. Investigations that have been performed however, have been met with mixed success due to extraneous background noises or inadequate evaluation of the signal spectral content. However, if it were possible to identify the salient or characterizing aspects of the signal, these drawbacks may be overcome.

The goal of this thesis is to develop methods which characterize the arc-acoustic signal such that a relationship can be drawn between welding parameters and acoustic spectral characteristics. Three methods were attempted including: Taguchi experiments to reveal trends between weld process parameters and the acoustic signal; psycho-acoustic experiments that investigate expert welder reliance on arc-sounds, and implementation of an artificial neural network (ANN) for mapping arc-acoustic spectral characteristics to process parameters.

Together, these investigations revealed strong correlation between welding voltage and arc-acoustics. The psycho-acoustic experiments confirm the suspicion of welder reliance on arc-acoustics as well as potential spectral candidates necessary to spray-transfer control during GMA welding. ANN performance shows promise in the approach and confirmation of the ANN?s ability to learn. Further experimentation and data gathering to enrich the learning data-base will be necessary to apply artificial intelligence such as artificial neural networks to such a stochastic and non-linear relationship between arc-sound and GMA parameters.

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Miller, Matthew Scott. "Development of a non-contact data acquisition system for robotic welding process monitoring." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/16071.

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Books on the topic "Welding – Automation"

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Tarn, Tzyh-Jong, Shan-Ben Chen, and Changjiu Zhou, eds. Robotic Welding, Intelligence and Automation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73374-4.

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Tarn, Tzyh-Jong, Shan-Ben Chen, and Gu Fang, eds. Robotic Welding, Intelligence and Automation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19959-2.

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Tarn, Tzyh-Jong, Changjiu Zhou, and Shan-Ben Chen, eds. Robotic Welding, Intelligence and Automation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b84345.

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Tarn, Tzyh-Jong, Shan-Ben Chen, and Xiao-Qi Chen, eds. Robotic Welding, Intelligence and Automation. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18997-0.

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1937-, Tarn Tzyh-Jong, Chen S. -B, and Zhou Changjiu 1963-, eds. Robotic welding, intelligence and automation. Berlin: Springer, 2007.

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D, Lane Jack, ed. Robotic welding. Kempston, Bedford, UK: IFS (Publications), 1987.

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Berge, James M. Automating the welding process: Successful implementation of automated welding systems. New York: Industrial Press, 1994.

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Welding, International Institute of, ed. Automation and robotisation in welding and allied processes: Proceedings of the international conference held at Strasbourg, France, 2-3 September 1985 under the auspices of the International Institute of Welding = Automisation et robotisation en soudage et techniques connexes : communicationsprésentées à la conférence internationale tenue à Strasbourg, France les 2 et 3 Septembre 1985 sous les auspices de l'Institute International de la Soudure. Oxford: Published on behalf of the International Instituteof Welding by Pergamon, 1985.

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1939-, Weston John, and Welding Institute, eds. Automated welding systems in manufacturing: Fourth international conference, Gateshead, North East UK, 17-19 November 1991. Cambridge, England: Abington Pub., 1992.

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10

A, Piotrowski John, Randolph William T, and Robotics International of SME. Marketing Services Division., eds. Robotic welding: A guide to selection and application. Dearborn, Mich: Welding Division, Robotics International of SME, Publications Development Dept., Marketing Division, 1987.

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Book chapters on the topic "Welding – Automation"

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Pashkevich, Anatol. "Welding Automation." In Springer Handbook of Automation, 1027–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78831-7_59.

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Pashkevich, Anatol. "Welding Automation." In Springer Handbook of Automation, 935–47. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-96729-1_43.

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Connor, Leonard P. "Automation and Control." In Welding Handbook, 311–48. London: Macmillan Education UK, 1987. http://dx.doi.org/10.1007/978-1-349-10624-0_10.

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Bolz, Roger W. "11B Industry Applications: Automotive Assembly Respot Welding." In Manufacturing Automation Management, 182–83. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2541-3_39.

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Chin, Bryan A., and Nels H. Madsen. "Automatic Welding: Infrared Sensors for Process Control." In Computer-Based Automation, 411–29. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4684-7559-3_19.

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Chen, S. B., T. Qiu, T. Lin, L. Wu, J. S. Tian, W. X. Lv, and Y. Zhang. "Intelligent Technologies for Robotic Welding." In Robotic Welding, Intelligence and Automation, 123–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44415-2_8.

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Schubert, Emil. "Process Stability of Automated Gas Metal Arc Welding of Aluminium." In Robotic Welding, Intelligence and Automation, 1–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44415-2_1.

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Du, Dong, Bo Sui, Y. F. He, Qiang Chen, and Hua Zhang. "Visual Measurement of Path Accuracy for Laser Welding Robots." In Robotic Welding, Intelligence and Automation, 152–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44415-2_10.

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Ip, Y. L., A. B. Rad, and Y. K. Wong. "Map Building and Localization for Autonomous Mobile Robots." In Robotic Welding, Intelligence and Automation, 161–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44415-2_11.

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Maravall, D. "Control and Stabilization of the Inverted Pendulum via Vertical Forces." In Robotic Welding, Intelligence and Automation, 190–211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44415-2_12.

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Conference papers on the topic "Welding – Automation"

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Frolov, A. V. "Automation the Welding Trajectory Control." In 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2020. http://dx.doi.org/10.1109/fareastcon50210.2020.9271607.

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Melakhsou, Abdallah Amine, and Mireille Batton-Hubert. "On welding defect detection and causalities between welding signals." In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). IEEE, 2021. http://dx.doi.org/10.1109/case49439.2021.9551659.

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Barcelos Gonçalves, Judson, Iago Marques Nunes, Luiz Rafael Resende da Silva, Douglas Ruy S S Araujo, Giuliano Souza, and Antônio Carlos Barbosa Zancanella. "AUTOMATION SYSTEM DEVELOPMENT FOR MULTIPROCESS WELDING MECHANISM." In 24th ABCM International Congress of Mechanical Engineering. ABCM, 2017. http://dx.doi.org/10.26678/abcm.cobem2017.cob17-0252.

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S, Janaki Raman, Ramkumar Venkatasamy, Indumathi S. K, Manoj Kumaran, and Christen Samuel. "Welding Machine Automation for Pneumatic Vessel Fabrication." In 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2022. http://dx.doi.org/10.1109/icesc54411.2022.9885424.

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Leonardo, Bruno Quaresma, Cristiano Rafael Steffens, Sidnei Carlos da Silva Filho, Jusoan Lang Mor, Valquiria Huttner, Eduardo Do Amaral Leivas, Vagner Santos da Rosa, and Silvia Silva Da Costa Botelho. "Vision-based system for welding groove measurements for robotic welding applications." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487785.

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Erden, Mustafa Suphi. "Manual Welding with Robotic Assistance Compared to Conventional Manual Welding." In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). IEEE, 2018. http://dx.doi.org/10.1109/coase.2018.8560489.

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Liang, Zhimin, Hongming Gao, Liang Nie, and Lin Wu. "3D Reconstruction for Telerobotic Welding." In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4303589.

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Yi Jinggang, Jiang Haiyong, Xing Yazhou, Liu Jiangtao, and Ma Yuejin. "Obstacle-avoiding automatic welding machine based on dual-welding-torch Synchronous linkage." In 2010 International Conference on Mechanic Automation and Control Engineering (MACE). IEEE, 2010. http://dx.doi.org/10.1109/mace.2010.5535579.

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Yi, Jinggang, Yazhou Xing, and Zheyi Yi. "Double-Point Optical Fiber Detection System of Two-Welding Torch Counterguard Automation Welding Machine." In 2010 Symposium on Photonics and Optoelectronics (SOPO 2010). IEEE, 2010. http://dx.doi.org/10.1109/sopo.2010.5504409.

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Smirnov, Yuri, Anna Kalyashina, and Rimma Zaripova. "Simulation of Robotic Laser Welding Process." In 2022 International Russian Automation Conference (RusAutoCon). IEEE, 2022. http://dx.doi.org/10.1109/rusautocon54946.2022.9896310.

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Reports on the topic "Welding – Automation"

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Harris, Patel, and Vaze. PR-185-0351-R07 Welding Processes for Small to Medium Diameter Pipe - Productivity and Economic Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2006. http://dx.doi.org/10.55274/r0011066.

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The project aimed to develop innovative welding processes and technologies for single-sided pipeline girth welding. Root pass welding techniques were emphasized since they have the greatest potential to improve pipeline integrity and facilitate the use of new and existing gas metal arc welding (GMAW) fill pass techniques. Advanced automation techniques were also used to improve weld quality, process control, and robustness. The objective of Task 7 was to examine the competing productivity and economic factors involved in selecting the appropriate technology for a particular pipeline project.

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Reichert, Harris, and Vaze. PR-185-0351-R04 Welding Processes for Small to Medium Diameter Pipelines - Improved Root Pass Techniques. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2006. http://dx.doi.org/10.55274/r0011070.

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The purpose of this portion of the project was to develop improved root pass automation (Task 3) and process control systems (Task 4) for girth welding of pipeline butt joints. To achieve these goals a motion control module was developed for control of a track-mounted Serimer-Dasa welding tractor (bug). The module includes a software program that can communicate with a motion controller and control all motions of the axes.

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LaMorte, Boring, and Porter. L52244 Advanced Welding Repair and Remediation Methods for In-Service Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2007. http://dx.doi.org/10.55274/r0010379.

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This project developed a prototype multi-axis automatic welding system with adaptive control and tracking for use on in-service welding repairs on liquid and gas transmission pipelines. The system is capable of deploying either gas metal arc welding (GMAW) or flux cored arc welding (FCAW) to weld pressure-containing sleeves (Type B), to weld reinforcement sleeves (Type A), or to directly deposit a layer of weld over an area to replace metal loss due to corrosion. The welding system was field tested at TransCanada in North Bay, Ontario and was demonstrated during a workshop at Edison Welding Institute in Columbus, Ohio. In addition, preliminary in-service welding trials with GMAW and FCAW were performed on high strength pipeline steels (X80, X100 and X120) to determine the susceptibility of hydrogen cracking, under simulated in-service welding conditions.

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Wang, Yong-Yi. PR350-164500-R03 Semi-Automatic FCAW-S Welding Process and Implications to Pipeline Girth Weld Integrity. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2022. http://dx.doi.org/10.55274/r0012238.

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This report provides a comprehensive update of key considerations and drivers that will influence FCAW-S weld performance. The major focus has been toughness, specifically in understanding the causes of seemingly large variation in measured toughness. Analysis of the data indicates that improving welding practice is an effective tool for reducing the toughness variation and enhancing overall weld performance. Comparisons are made with the performance of other welding processes commonly used for pipeline girth welding, such as SMAW.

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Johnson, Fiore, and Ramirez. L52228 Mechanical Properties of 5LX-80 Pipeline Girth Welds. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 2007. http://dx.doi.org/10.55274/r0011004.

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The primary objectives of this program were to establish baseline weld metal mechanical property measurements for welding X-80 pipelines and tie-ins using commercially available cellulosic SMAW, basic SMAW, FCAW-S, and mechanized GMAW consumables. Additional objectives addressed in this project include: - Examination of the effect of welding position on toughness in welds produced using manual and semi-automatic processes. - Evaluation of the effect of repairing FCAW-S weldments with non-FCAW-S consumables and to provide repair recommendations. - Evaluation of the reported variations in weld metal toughness produced using several heats of GMAW consumables and standard internal/external narrow-groove GMAW procedures.

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Daumeyer, G. J. III. Progress report on a fully automatic Gas Tungsten Arc Welding (GTAW) system development. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/10106005.

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