Optimal parameter design via soft computing

• Main Authors: HsuHwa Chang, 張旭華
• Other Authors: ChaoTon Su
• Format: Others
• Language: en_US
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/14748352446157806765

博士 === 國立交通大學 === 工業工程與管理系 === 88 === The fast change of environment makes manufacturers have to promptly develop new products and provide high quality products to meet customers’ requirements so as to keep the competitive edges. Parameter design is critical for manufacturers to simultaneously achieve both the time-to-market reduction and the quality enhancement of the products and processes. However, the parameter design optimization problems are difficult owing to that nonlinear relationships exist in the system and interactions may occur among parameters. Engineers conventionally apply the Taguchi method to optimize parameter design; however, the Taguchi method has some limitations in practice. This dissertation attempts to release those limitations by using three techniques of soft computing (SC), i.e., neural network (NN), genetic algorithm (GA), and simulated annealing (SA). To achieve optimal parameter design, three SC-based procedures are proposed herein: (1) Neural networks based procedure (named NNs-based), (2) combined NN with GA procedure (named NN-GA), and (3) combined NN with SA procedure (named NN-SA). The proposed procedures in this dissertation provide the relatively simple and efficient methods, and are able to release the limitations of the statistical methods in solving the practical problems. Five numerical examples adopted from literature are resolved to illustrate the proposed procedures’ effectiveness. The computational results show that the proposed procedures in this dissertation excel the Taguchi method.