
	Information Technology Journal
	Year: 2009 \| Volume: 8 \| Issue: 6 \| Page No.: 847-854 DOI: 10.3923/itj.2009.847.854

Quality Prediction Model of Injection-Molded Rib Design using Back-Propagation Network

Tian-Syung Lan and Ming-Yung Wang

Abstract: In this study, an analytical model of a rectangular thermoplastic ABS (Acrylonitrile Butadiene Styrene) plastic cover with rib of a given thickness (2.8 mm) was introduced and the dimensions as well as width of the rib were selected as the control factors for simulation. Additionally, the deflection under a constant force of 150 Newton at the back centre of the cover was defined as quality characteristic. Moreover, the L₉(3⁴) orthogonal array for four factors and three levels from Taguchi method was additionally considered to layout the 3⁴ = 81 sets of full simulations. By commencing the BPN (Back-Propagation Network) to learn the selected 45 sets of simulated results. The remaining 36 sets of simulated results are then employed to verify and construct a quality predictor of rib design. Considering the learning rate as 1 and momentum factor as 0.5, the results of 20000 times of BPN training through a hidden layer indicated that the accuracy of deflection prediction reached 95.87%. In this study, the full FEM (Finite Element Method) simulated results from the 81 sets of combinations layout by Taguchi method are learned and verified by BPN for the design of injection-molded rib. It is shown that the quality of a plastic rib can surely be effectively found with the proposed economic and prospective BPN. This study exactly contributes an economical technique to the quality prediction of rib design for plastic injection industry in minimizing the development period of a new product.

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How to cite this article:

Tian-Syung Lan and Ming-Yung Wang, 2009. Quality Prediction Model of Injection-Molded Rib Design using Back-Propagation Network. Information Technology Journal, 8: 847-854.

DOI: 10.3923/itj.2009.847.854

URL: https://scialert.net/abstract/?doi=itj.2009.847.854

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