A Two-Stage Optimization System for the Plastic Injection Molding with Multiple Performance Characteristics

2012 ◽  
Vol 468-471 ◽  
pp. 386-390 ◽  
Author(s):  
Wen Chin Chen ◽  
G.L. Fu ◽  
Denni Kurniawan

This study proposes a two-stage optimization system to generate the optimal process parameter settings of multi-quality characteristics in the plastic injection molding (PIM) products. In the first stage, Taguchi orthogonal array was employ to arrange the experimental work and to calculate the S/N ratio to determine the initial process parameter settings. Then, S/N ratio predictor and S/N quality predictor was constructed by employed the back-propagation neural network (BPNN). In addition, S/N ratio predictor was along with simulated annealing (SA) used to search for the first optimal parameter combination in order to reduce the PIM process variance. In the second stage, BPNN quality predictor and particle swarm optimization (PSO) was intended to find the optimal parameter settings for the best quality specification. Results from the experimental work show that the proposed two-stage optimization system can create the best process parameter settings which not only meet the quality specification, but also effectively reduce cost.

2014 ◽  
Vol 945-949 ◽  
pp. 478-483
Author(s):  
Wen Chin Chen ◽  
Yen Fu Lin ◽  
Pen Hsi Liou

This study proposes an optimization system to find out the optimal process parameters of plastic injection molding (PIM). The system is divided into two phases. In the first phase, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. In the second phase, the back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the optimal parameter combination. The purpose of this stage is to reduce the process variance and promote product quality. Experimental results show that the proposed optimization system can not only satisfy the quality specification, but also improve stability of the PIM process.


2012 ◽  
Vol 2012 (0) ◽  
pp. _CO-JP-5-1-_CO-JP-5-4
Author(s):  
Ryosuke Onuki ◽  
Satoshi Kitayama ◽  
Koetsu Yamazaki

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