FUZZY REGRESSION MODEL TO PREDICT THE BEAD GEOMETRY IN THE ROBOTIC WELDING PROCESS
来源期刊：Acta Metallurgica Sinica2007年第6期
论文作者：H.H. Kim I.S. Kim B.S. Sung Y.H. Cha Y. Xue
Key words：robotic arc welding; bead geometry; fuzzy regression model; welding quality;
Abstract: Recently, there has been a rapid development in computer technology, which has in turn led todevelop the fully robotic welding system using artificial intelligence (AI) technology. However, therobotic welding system has not been achieved due to difficulties of the mathematical model andsensor technologies. The possibilities of the fuzzy regression method to predict the bead geometry,such as bead width, bead height, bead penetration and bead area in the robotic GMA (gas metalarc) welding process is presented. The approach, a well-known method to deal with the problemswith a high degree of fuzziness, is used to build the relationship between four process variablesand the four quality characteristics, respectively. Using these models, the proper prediction of theprocess variables for obtaining the optimal bead geometry can be determined.
H.H. Kim1，I.S. Kim1，B.S. Sung1，Y.H. Cha2，Y. Xue3
(1.Department of Material Engineering, Chosun University 375, Seosul-Dong, Dong-Gu,Gwangju 501-759, Korea;
2.Department of Mechanical Engineering, Chosun University 375, Seosul-Dong, Dong-Gu,Gwangju 501-759, Korea;
3.Department of Mechanical Engineering, Mokpo National University 61, Dorim-ri,Chungkye-Myun, Muan-Gun, Jeonnam 534-729, Korea)
Abstract:Recently, there has been a rapid development in computer technology, which has in turn led todevelop the fully robotic welding system using artificial intelligence (AI) technology. However, therobotic welding system has not been achieved due to difficulties of the mathematical model andsensor technologies. The possibilities of the fuzzy regression method to predict the bead geometry,such as bead width, bead height, bead penetration and bead area in the robotic GMA (gas metalarc) welding process is presented. The approach, a well-known method to deal with the problemswith a high degree of fuzziness, is used to build the relationship between four process variablesand the four quality characteristics, respectively. Using these models, the proper prediction of theprocess variables for obtaining the optimal bead geometry can be determined.
Key words:robotic arc welding; bead geometry; fuzzy regression model; welding quality;
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