陈和平 教授,

德克萨斯州立大学，美国

题目

    Optimal Welding Parameter Learning for Robotic Arc Welding Process

摘要

    Robotic arc welding has been implemented in many applications for manufacturing automation. Once a robotic welding system is set up, one of the challenges is to tune the welding process parameters. Some methods have been proposed to explore optimal welding parameters, however they are suffering some practical limitations. Because the welding process parameters determine the weld quality, it is important to find optimal set of parameters to achieve best weld quality. In this talk, a welding process parameter optimization method is presented to optimize weld quality for robotic arcwelding process. A non-parametric model is constructed using Gaussian Process Regression iteratively while Bayesian Optimization Algorithm is adopted to exploit potential optimal candidate. To evaluate the weld quality, a tensile machine is utilized to measure the tensile strength of the weld joint. Experimental results show that an optimal set of welding process parameters can be determined using ten trials. The number of experiments to explore optimal welding process parameter is greatly decreased compared with the existing methods. Hence the proposed method is an efficient and effective tool to optimize weld quality for complex welding process.

简介

    陈和平博士1989年从哈尔滨工业大学获得学士学位， 1999年从新加坡南洋理工大学（新加坡）获得硕士学位，2004年从美国密西根州立大学获得博士学位。     陈博士现在是德克萨斯州立大学终身教授和机器人实验室主任。他曾在ABB研发中心作了6年研究员。他有12年工业界经历， 发表了150余篇论文，并有近20个专利或专利申请。他获得10多个奖项，包括国际顶级期刊优秀论文奖和密西根州立大学优秀毕业生奖。他是IEEE的资深会员，和工业界及学术界有广泛的合作。他的研究范围包括机器人焊接、工业机器人及自动化、机器学习/深度学习、移动操作、晶圆传输机器人、机器视觉、控制系统、纳米制造及纳米机器人。