Optimization of Cable Ties Injection Molding Process Using Back Propagation Neural Network and Genetic Algorithm (BPNN-GA)

Abstract:

Setting parameters in the injection molding machine play an important role to the quality of cable ties product. They affect not only to the number of the rejection products but also to their tensile yield strength. The goal of this study is to obtain a combination of process parameters such as nozzle temperature, injection pressure, injection flow, and switch-over to holding pressure, which results the optimal tensile yield as the observed response using Back Propagation Neural Network-Genetic Algorithm (BPNN-GA). In this study, a 4-8-8-1 BPNN model was applied to predict the tensile yield based on a random combination of process parameters. The tensile yield then was optimized by genetic algorithm through several iterations. The optimal tensile yield of 28.44 MPa has been obtained using the following combination i.e. nozzle temperature of 250 oC, injection pressure of 1400 bar, injection flow of 40 cm³/s, and switch-over to holding pressure of 13,2 cm³.

• Periodical: Applied Mechanics and Materials (Volume 836)
• Pages: 159-164
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.836.159
• Online since: June 2016
• Authors: Arif Wahjudi*, Bobby Oedy Pramoedyo Soepangkat, Yang Fitri Arriyani
• Keywords: Back Propagation Neural Network, Genetic Algorithm, Injection Molding, Process Optimization

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