A neural network-based approach for dynamic quality prediction in a plastic injection molding process

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A neural network-based approach for dynamic quality prediction

in a plastic injection molding process

Wen-Chin Chen

a

, Pei-Hao Tai

a

, Min-Wen Wang

b

, Wei-Jaw Deng

c

, Chen-Tai Chen

d,* a_{Graduate Institute of Industrial Engineering and System Management, Chung Hua University, 707 Wufu Road, Section 2, Hsinchu 300, Taiwan}

b_{Department of Mechanical Engineering, National Kaohsiung University of Applied Sciences, 415 Chien Kung Road, Kaohsiung 807, Taiwan} c

Graduate School of Business Administration, Chung Hua University, 707 Wufu Road, Section 2, Hsinchu 300, Taiwan

d

Department of Computer Science and Information Engineering, Ta Hwa Institute of Technology, 1 Tahwa Road, Chiunglin, Hsinchu 307, Taiwan

Abstract

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A

self-orga-nizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three

SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated

to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural

network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which

six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of

120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality

(weight) and can likely be used for various practical applications.

2007 Elsevier Ltd. All rights reserved.

Keywords: Neural network-based prediction system; Injection molding process; Self-organizing map; Back-propagation neural network; Dynamic quality predictor; Taguchi’s parameter design method

1. Introduction

Plastic injection molding (PIM) is one of the most

com-plex manufacturing processes due to the strong

nonlinear-ities, even though numerous people regard it as a simple

and common manufacturing process. This process includes

four phases: plasticization, injection, packing, and cooling

(

Seaman, 1994

). In previous injection molding research,

many process parameters, such as the melting temperature,

mold temperature, injection pressure, injection velocity,

injection time, packing pressure, packing time, cooling

temperature, and cooling time, were found to possibly

inﬂuence the quality of injection-molded plastic products

(

Kurtaran & Erzurumlu, 2006; Zhao & Gao, 1999

). Several

PIM control process parameters have been used (

Chiang &

Chang, 2006; Huang & Tai, 2001; Wu & Liang, 2005

).

Huang and Tai (2001)

presented six process parameters

(mold temperature, melt temperature, gate dimension,

packing pressure, packing time, and injection time) to

determine the optimal initial process parameter settings

for injection-molded plastic parts with a thin shell feature

and under a single quality characteristic (warpage)

consid-eration.

Wu and Liang (2005)

employed six process

param-eters

(mold

temperature,

packing

pressure,

melt

temperature, injection velocity, injection acceleration, and

packing time) to discuss the eﬀects of process parameters

on the weld-line width of an injection-molded plastic

prod-uct.

Chiang and Chang (2006)

proposed four control

pro-cess parameters (mold temperature, melt temperature,

injection pressure, and injection time) to determine the

optimal initial process parameter settings for an

injection-molded plastic part with a thin shell feature in a model with

*

Corresponding author. Tel.: +886 3 592 7700x2929; fax: +886 3 592 5679.

E-mail address:jactchen@thit.edu.tw(C.-T. Chen).

www.elsevier.com/locate/eswa

Available online at www.sciencedirect.com

Expert Systems with Applications 35 (2008) 843–849

Expert Systems

with Applications

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predictor had an RMSE of up to 0.015 and its test

preci-sion amount was an RMSE of 0.0017, whereas the training

precision of the BPNN quality predictor had an RMSE

of up to 0.019 and its testing precision amount was an

RMSE of 0.0029. Apparently, the performance of the

SOM+BPNN quality predictor was better than that of

the BPNN quality predictor.

8. Conclusions

Plastic injection molding process control and optimal

parameter settings for the product properties require very

accurate quality prediction. When the quality predictor is

precise, the quality controller can adjust the controllable

parameters closer to the targets of the process control,

and an eﬃcient optimization model can be obtained for

the initial parameter settings. Therefore, it is essential to

extract the dynamic data of the process parameters in order

to enhance the prediction precision of the product quality

in the dynamic system. This research presents a

self-orga-nizing map plus a back-propagation neural network

(SOM-BPNN) model for the dynamic quality predictor.

Three SOM-based extraction parameters with six

manufac-turing process parameters were dedicated to training and

testing the networks. In addition, another

back-propaga-tion neural network (BPNN) model was employed for

comparison. The numerical results revealed that the

pro-posed dynamic model not only eﬀectively increased the

pre-diction performance of product quality but also obtained

more-reliable product quality in advance. In future

exten-sions, the proposed dynamic approach will be employed

to the process control for improving the prediction

preci-sion of the product quality in the dynamic PIM system.

Acknowledgements

This research has been conducted as part of a project

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