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A Study on the Prediction of Bead Geometry for Lab Joint Fillet Welds Using Sensitivity Analysis  

Jeong, Jae-Won (목포대학교 대학원 기계공학과)
Kim, Ill-Soo (목포대학교 기계선박해양공학부)
Kim, Hak-Hyoung (목포대학교 대학원 기계공학과)
Kim, In-Ju (한국생산기술연구원 전북연구센터)
Bang, Hong-In (한국폴리텍V대학 익산캠퍼스 컴퓨터응용기계과)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.17, no.6, 2008 , pp. 49-55 More about this Journal
Abstract
Arc welding process is one of the most important technologies to join metal plates. Robotic welding offers the reduced manufacturing cost sought, but its widespread use demands a means of sensing and correcting for inaccuracies in the part, the fixturing and the robot. A number of problems that need to be addressed in robotic arc welding processes include sensing, joint tracking, and lack of adequate models for process parameter prediction and quality control. Problems with parameter settings and quality control occur frequently in the GMA(Gas Metal Arc) welding process due to the large number of interactive process parameters that must be set and accurately controlled. The objectives of this paper are to realize the mapping characteristics of bead width using a sensitivity analysis and develop the neural network and multiple regression method, and finally select the most accurate model in order to control the weld quality(bead width) for fillet welding. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.
Keywords
Fillet Welding; Sensitivity Analysis; Process Parameter; Regression Analysis Method; Neural Network Model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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