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Title: Automated process parameter resetting for injection moulding: A fuzzy-neuro approach
Authors: He, W.
Zhang, Y.F. 
Lee, K.S. 
Fuh, J.Y.H. 
Nee, A.Y.C. 
Keywords: Back-propagation neural networks
Fuzzy sets
Injection moulding
Moulding defects
Process parameter
Issue Date: 1998
Source: He, W.,Zhang, Y.F.,Lee, K.S.,Fuh, J.Y.H.,Nee, A.Y.C. (1998). Automated process parameter resetting for injection moulding: A fuzzy-neuro approach. Journal of Intelligent Manufacturing 9 (1) : 17-27. ScholarBank@NUS Repository.
Abstract: Optimal process parameter setting for injection moulding is difficult to achieve due to a large number of factors involved. Current practice in industry is to adjust the parameters based on the products' defects of test-run through trial and error. This process, however, requires enormous experience and is often time consuming. This paper reports an intelligent system employing fuzzy sets and neural networks which is able to predict the process parameter resetting automatically to achieve better product quality. The system is designed to be used in the test-run of injection moulding. Seven commonly encountered injection moulded product defects (short shot, flash, sink-mark, flow-mark, weld line, cracking, and warpage) and two key injection mould parameters (part flow length and flow thickness) are used as system input which are described using fuzzy terms. On the other hand, nine process parameter adjusters (pressure, speed, resin temperature, clamping force, holding time, mould temperature, injection holding pressure, back pressure, and cooling time) are the system output. A back-propagation neural network has been constructed and trained using a large number of {defects} → {parameter adjusters} expert rules. The system is able to predict the exact amount to be adjusted for each parameter towards reducing or eliminating the observed defects. Testing in several real cases showed that the system produced satisfying results. © 1998 Chapman & Hall.
Source Title: Journal of Intelligent Manufacturing
ISSN: 09565515
Appears in Collections:Staff Publications

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