文献类型：期刊文献

英文题名：Intelligent modelling of back-side weld bead geometry using weld pool surface characteristic parameters

作者：Wang, XueWu[1];Li, RuiRui[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2014

卷号：25

期号：6

起止页码：1301

外文期刊名：JOURNAL OF INTELLIGENT MANUFACTURING

收录：;EI(收录号：20143600033698);WOS:【SCI-EXPANDED(收录号:WOS:000344482300008)】；

基金：This work is partially funded by the National Science Foundation under grant CMMI-0927707 and China Oevrsea Scholarship. The assistance from PhD students Mr. WeiJie Zhang and Mr. YuKang Liu in experiments and guidance from Professor YuMing Zhang at the University of Kentucky Welding Research Laboratory are greatly appreciated.

语种：英文

外文关键词：GTAW; Characterization; Intelligent model; PCA-BPNN; GBAMPSO

摘要：In manual welding process, skilled welders can ensure the weld quality through compensating for deviation observed from the weld pool surface. In this paper a three dimensional vision sensing system was used to mimic the human vision system to observe the three-dimensional weld pool surface in pipe GTAW process. Novel characteristic parameters containing information about the penetration state specified by its back-side weld pool width and height were proposed based on the reconstructed three dimensional weld pool surfaces. Then, variation in characteristic parameters and their relationships with the back-side parameters were studied through experiments under different welding conditions. Direct measurement of penetration is not preferred in a manufacturing site, soft-sensing method was thus proposed as an alternative to obtain it in real time due to established soft-sensing model and auxiliary variables which can be sensed in real time. In order to obtain the penetration status in real time conveniently, back-propagation neural network, principle component analysis based back-propagation neural network and global best adaptive mutation particle swarm optimization based back-propagation neural network models were established to estimate the penetration based on the proposed characteristic parameters. It was found that the top-side characteristic parameters proposed can reflect the back-side weld pool parameters accurately and the models are capable of predicting the penetration status in real time by observing the three-dimensional weld pool surface.