Application of Artificial Intelligence Method and Taguchi Method for Parameter Optimization : Case Study in Injection Molding Machine

• Main Authors: CHEN-MING Hung, 洪晨銘
• Other Authors: Jui-Chin Jiang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/yz937w

碩士 === 中原大學 === 工業與系統工程研究所 === 107 === In the fierce competition generation. Can reduce the cost, improve the quality, improve the performance has always been the pursuit of everyone, especially in the traditional industrial system. In this study, the attribute Taguchi method in quality engineering and set up a kind of model to detect the defective rate. Through the injection molding process of the case, to prove that the development of the model can effectively reduce the defective rate. Through the counting type of Taguchi method, the best combination of parameters is obtained, including the cumulative analysis method that classifies the influence degree of each defect on the quality, and the conversion of the data type such as the percentage into the additivity value. There are two ways to effectively select the parameters that affect important processes. According to the results, the defective rate of shell outside the hard disk was reduced from 6.0% to 3.0%, and the amplitude of 50.0% was improved to prove the effectiveness and feasibility of the experiment. Finally, multi-layer perceptron (Multilayer Perceptron) is used to build a predictive model to reduce the need for case companies to conduct unnecessary experiments.