Improving Quality & Manufacturability by Design For Six Sigma

摘要：

In this paper, a new Design for Six Sigma(DFSS) integrating Artificial Neural Network approach is presented for improving quality and manufacturability simultaneously. FoDowed the neural network based on optimal DFSS method is employed as a tool in cutting parameters optimization. The results showed the roller burnishing formation can be controlled by adjusted cutting parameters with DFSS. The experiments proved the roller burnishing size with quality assurance method improved as much as 71 to 79 ％ comparing with conventional cutting condition. As a matter of fact, the parameters optimization by DFSS method is offering an effective tool to control the roller burnishing size in machining.

关键词： quality manufacturability dfss artificial neural network

作者: J.J.Wu Y.Z.Wang W.S.Cai J.J.Wu J.J.Shao

作者单位: Department of Mechanical &Electrical Engineering Jiangxi University of Science & Technology Ganzhou, Department of Economics and Managerial Science Tongji University Shanghai, P.R.China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Advanced Management Science(2010年IEEE高级管理科学国际会 IEEE ICAMS 2010)

会议地点: 成都

会议语种:英文

页码: 262-266

在线出版日期: 2010-07-09（万方平台首次上网日期，不代表论文的发表时间）