By Chen S.-B., Wu J.
''Welding handicraft is likely one of the so much primordial and conventional technics, usually by means of manpower and human reports. Weld caliber and potency are, hence, straitly constrained by means of the welder's ability. within the sleek production, computerized and robot welding is changing into an inevitable development. besides the fact that, it's tough for automated and robot welding to arrive prime quality end result of the complexity, uncertainty and disturbance in the course of welding approach, particularly for arc welding dynamics. the knowledge acquirement and real-time keep watch over of arc weld pool dynamical approach in the course of automated or robot welding continuously are confusing difficulties to either technologists in weld box and scientists in automation.'' ''This booklet offers a few software researches on intelligentized technique in arc welding method, equivalent to computer imaginative and prescient, picture processing, fuzzy logical, neural networks, tough set, clever keep an eye on and different synthetic intelligence tools for sensing, modeling and clever regulate of arc welding dynamical process.'' The stories within the e-book point out that the designed imaginative and prescient sensing and regulate structures may be able to partly emulate a talented welder's clever behaviors: looking at, estimating, decision-making and working, and convey a superb power and promising prospect of synthetic clever applied sciences within the welding production.
Read or Download Lecture Notes in Electrical Engineering. Vol.29. Intelligentized Methodology for Arc Welding Dynamical Processes: Visual Information Acquiring, Knowledge Modeling and Intelligent Control PDF
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Additional resources for Lecture Notes in Electrical Engineering. Vol.29. Intelligentized Methodology for Arc Welding Dynamical Processes: Visual Information Acquiring, Knowledge Modeling and Intelligent Control
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Wang. Visual information acquisition and adaptive control of weld pool dynamics of Aluminum alloy during pulsed TIG welding. PhD dissertation, Shanghai Jiao Tong University, 2003 69. J. B. Chen, T. Lin. Visual sensing and image processing in aluminum alloy welding, Lecture Notes in Control and Information Sciences, LNCIS 362, pp. 275–280, 2007(ISTP) 70. B. Chen. “Visual Information Acquirement and Real-time Control Methodologies for Weld Pool Dynamics during Pulsed GTAW,” Tthe invited paper of the THERMEC’2006, Journal Materials Science Forum vols.
The conference on Materials, plenary report) 53. M. Zhang, L. Wu, B. H. Chen. Determining joint penetration in GTAW with vision sensing of weld face geometry. Welding Journal, 1993, 72(10):463–469 54. M. Zhang, R. Kovacevic, L. Wu. Sensitivity of front-face weld geometry in representing the full penetration. Proc. Instn. Mech. Engrs, Part B: Journal of Engineering Manufacture. 1992, 206:191–197 55. M. Zhang, R. Kovacevic, L. Wu. Dynamic analysis and identification of gas tungsten arc welding process for weld penetration control.
Approaches to utilizing neural networks in process control were discussed. The need for modeling transient as well as static characteristics of physical systems for closed-loop control was pointed out, and an approach to achieving this was presented. References [189] used ANN to build dynamic model and inverse dynamic model of welding process. Inverse dynamic model was to relate backside weld pool width to welding current to predict output controlling variable; dynamic model was to Fig. 5 Intelligent Control Strategies for Arc Welding Process 21 Fig.