By Chen S.-B., Wu J.
''Welding handicraft is likely one of the such a lot primordial and standard technics, more often than not through manpower and human reviews. Weld caliber and potency are, consequently, straitly restricted through the welder's ability. within the smooth production, automated and robot welding is changing into an inevitable pattern. in spite of the fact that, it really is tricky for automated and robot welding to arrive prime quality because of the complexity, uncertainty and disturbance in the course of welding technique, particularly for arc welding dynamics. the knowledge acquirement and real-time keep watch over of arc weld pool dynamical strategy in the course of automated or robot welding regularly are complicated difficulties to either technologists in weld box and scientists in automation.'' ''This booklet provides a few software researches on intelligentized technique in arc welding method, comparable to computer imaginative and prescient, picture processing, fuzzy logical, neural networks, tough set, clever keep watch over and different synthetic intelligence equipment for sensing, modeling and clever keep an eye on of arc welding dynamical process.'' The reports within the booklet point out that the designed imaginative and prescient sensing and regulate structures may be able to partly emulate a talented welder's clever behaviors: staring at, estimating, decision-making and working, and convey a superb capability and promising prospect of synthetic clever applied sciences within the welding production.
Read Online 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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Extra info 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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Some basic concepts relating to neural networks and how they could be used to model weld-bead geometry in terms of the equipment parameters selected to produce the weld were explained. 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  used ANN to build dynamic model and inverse dynamic model of welding process.