The study of intelligent optimal design for Hydro-mechanical deep drawing of stainless steel

• Main Authors: Tsung-Han Wu, 吳宗翰
• Other Authors: Jing-Ping Wang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/39189191283232404652

碩士 === 國立臺灣海洋大學 === 輪機工程系 === 97 === Hydro-deep drawing processes were studied on the optimization considerations of Neural Genetic Hybrid algorithm. In this research, the problems of drawing length and the finished product accuracy were discussion. To make an optimal choice of drawing parameters, the finite element method with the package DEFORM-3D were used. Consequently, an optimization die obtained form the Taguchi Method were designed and manufactured. The data bases used artificial neural network and genetic algorithm to prediction the optimization experimental parameters. It was shown that the results obtained by this method were good coincidence with experiments. In addition, it was worthy to note that the height of drawing length were directly proportional to the hydraulic pressure on back of specimen and contrary to the height of earring and wrinkling.