Title :
Improve burnishing formation yield applying Design for Six Sigma
Author :
Wu, Jianjun ; Wang, Yizhen ; Zhang, Qizhong ; Huang, Pengpeng
Author_Institution :
Dept. of Mech. & Electr. Eng., Jiangxi Univ. of Sci. & Technol., Ganzhou, China
Abstract :
In order to overcome the drawback of traditional optimize only focus on part parameters and low efficient, this paper explores a new Design for Six Sigma (DFSS) integrating Artificial Neural Network approach in optimizing burnishing formation process quality and yield. The experiments show that DFSS-Neural based on LCC method is an effective tool to improve the roller burnishing yield in machining.
Keywords :
burnishing; design for quality; life cycle costing; machining; neural nets; production engineering computing; six sigma (quality); LCC method; artificial neural network; burnishing formation process quality; burnishing formation yield; design for six sigma; life cycle costing; machining; roller burnishing yield; Burnishing; Manufacturing; Materials; Mathematical model; Milling; Presses; Six sigma; Artificial Neural Network; DFSS; Yield;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
DOI :
10.1109/IEEM.2011.6118027
