TITLE:
Determination of Optimal Manufacturing Parameters for Injection Mold by Inverse Model Basing on MANFIS
AUTHORS:
Chung-Neng Huang, Chong-Ching Chang
KEYWORDS:
Optimal Manufacturing Parameter, Injection Mold, Multiple Adaptive Network Based Fuzzy Inference System (Manfis), Taguchi Method
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.2 No.1,
March
1,
2010
ABSTRACT: Since plastic products are with the features as light, anticorrosive and low cost etc., that are generally used in several of tools or components. Consequently, the requirements on the quality and effectiveness in production are increasingly serious. However, there are many factors affecting the yield rate of injection products such as material characteristic, mold design, and manufacturing parameters etc. involved with injection machine and the whole manufacturing process. Traditionally, these factors can only be designed and adjusted by many times of trial-and-error tests. It is not only waste of time and resource, but also lack of methodology for referring. Although there are some methods as Taguchi method or neural network etc. proposed for serving and optimizing this problem, they are still insufficient for the needs. For the reasons, a method for determining the optimal parameters by the inverse model of manufacturing platform is proposed in this paper. Through the integration of inverse model basing on MANFIS and Taguchi method, inversely, the optimal manufacturing parameters can be found by using the product requirements. The effectiveness and feasibility of this proposal is confirmed through numerical studies on a real case example.