Title :
Notice of Retraction
Improving quality amp; manufacturability by Design for Six Sigma
Author :
Wu, Jin Jei ; Wang, Y.Z. ; Cai, W.S. ; Shao, J.J.
Author_Institution :
Dept. of Mech. & Electr. Eng., Jiangxi Univ. of Sci. & Technol., Ganzhou, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, a new Design for Six Sigma(DFSS) integrating Artificial Neural Network approach is presented for improving quality and manufacturability simultaneously. Followed 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.
Keywords :
burnishing; cutting; design for quality; machining; neurocontrollers; optimisation; quality assurance; six sigma (quality); artificial neural network approach; cutting parameters optimization; design for six sigma; machining; manufacturability; optimal DFSS method; quality assurance method; roller burnishing formation; Artificial neural networks; Burnishing; Gallium nitride; Magnetic analysis; Artificial Neural Network; DFSS; Manufacturability; Quality;
Conference_Titel :
Advanced Management Science (ICAMS), 2010 IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6931-4
DOI :
10.1109/ICAMS.2010.5553241
