FUZZY REGRESSION MODEL TO PREDICT THE BEAD GEOMETRY IN THE ROBOTIC WELDING PROCESS

作     者：B.S. Sung I.S. Kim Y. Xue H.H. Kim Y.H. Cha 

作者机构：Department of Material Engineering Chosun University 375 Seosul-Dong Dong-GuGwangju 501-759 Korea Department of Mechanical Engineering Mokpo National University 61 Dorim-riChungkye-Myun Muan-Gun Jconnam 534-729 Korea Department of Mechanical Engineering Chosun University 375 Seosul-Dong Dong-GuGwangju 501-759 Korea 

出 版 物：《Acta Metallurgica Sinica(English Letters)》 (金属学报（英文版）)

年 卷 期：2007年第20卷第6期

页      面：391-397页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：robotic arc welding bead geometry fuzzy regression modelwelding quality 

摘      要：Recently, there has been a rapid development in computer technology, which has in turn led to develop the fully robotic welding system using artificial intelligence （AI） technology. However, the robotic welding system has not been achieved due to difficulties of the mathematical model and sensor 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 metal arc） welding process is presented. The approach, a well-known method to deal with the problems with a high degree of fuzziness, is used to build the relationship between four process variables and the four quality characteristics, respectively. Using these models, the proper prediction of the process variables for obtaining the optimal bead geometry can be determined.