Applying Particle-Swarm-Optimization-Based Neural Network in Cost Estimating for Plastic Injection Molded Parts

• Main Authors: Yu-Chun Wang, 王郁淳
• Other Authors: 王河星
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/w8zq7x

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 96 === Injection plastic products are subject to frequent variability due to the changeable environment, normally, manufacturers in Taiwan are faced with: unpredictable product cost, urgent delivery and variable production planning, etc. Traditional plastic injection moldings are also confronted with likewise problems, so plastic injection molding practitioners have to quickly capture market trends so as to make effective response, strengthen customer services, and improve the traditional over concentration in market, production and product. In terms of business strategies, it is an important process to convert product research and development management into competence for timely and effective cost estimation in rapid response to the market. With a stress on labor division, plastic injection is composed of multi-stage manufacturing, take injection plastic product manufacturing process for example, firstly a product design has to be drawn by the product research and design staff, then, molds will be provided by mould manufacturers for specialized injection plants or injection equipments within the factory to carry out the plastic injection process, next, assembly and packaging will be done by assemblers, and finally semi-finished products will be inspected to provide finished products for sale. Making rapid response to customer needs in terms of new product design, material purchase, outsourcing, quality control, new product pricing seems especially important due to the complex procedures for injection plastic product. This study focuses on the integration of neutral network and particle swarm algorithm to solve the problems concerned with cost estimation for new products for the plastic injection molding industry, where particle swarm optimization was used to solve the problems concerned with parameter setting for the neural network in order find optimum network parameters. The utilization of Particle Swarm Optimization-Back-Propagation (PSO-BP) was aimed at precise estimation of production cost for plastic injection molding, improvement on overall production estimation accuracy, and offering correct and prompt information to customers. PSO-BP was used in this study to strengthen plastic cost control to enhance customer services, make quick response, reduce production cost and enhance enterprise competence.