Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

Arif Wahjudi (Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember)
Thenny Daus Salamoni (Department of Electrical Engineering, Polnam, Rumahtiga Ambon)
I Made Londen Batan (Institut Teknologi Sepuluh Nopember)
Dinny Harnany (Department of Electrical Engineering, Polnam, Rumahtiga Ambon)

JMES The International Journal of Mechanical Engineering and Sciences
Vol 5, No 2 (2021)

The polymer matrix composite (PMC) in use today is generally made of synthetic fibers which are expensive and not environmentally friendly. The use of synthetic fibers can be replaced with natural fibers, which are more environmentally friendly at a lower price. The natural fiber material used in this study is made from husks, with a particle size of 500 µm (mesh 35). In the PMC manufacturing process, rice husks are mixed with polypropylene (PP) and maleic anhydride polypropylene (MAPP) with a composition of 10 wt% RH, 85 wt% PP and 5 wt% MAPP. PMC materials using natural fibers are called biocomposite materials. The result of mixing PMC with natural fibers in the form of pellets is then carried out by the injection process using an injection molding machine. The printed results are in the form of tensile test specimens based on ASTM D 638-03 type V testing standards and impact test specimens based on ASTM D 256-04 testing standards. The research was conducted by optimizing the responses i.e. tensile strength and impact strength of the biocomposite material in the injection molding machine process, whereas varied process parameters, namely barrel temperature, injection pressure, holding pressure, injection velocity were selected as process parameters. The backpropagation neural network (BPNN) training method is used to recognize the pattern of the relationship between process parameters and response parameters based on the previous experiment, while the genetic algorithm (GA) optimization method is to determine the variation settings for process parameters that can optimize tensile and impact strength. The results of the BPNN training have a 4-9-9-2 network architecture consisting of 4 input layers, 2 hidden layers with 9 neurons, and 2 neurons in the output layer. Optimization with GA produces a combination of variable process parameters barrel temperature 217◦C, injection pressure 55 Bar, holding pressure 41 Bar and injection velocity 65 mm/sec. The results of statistical validation using one sample T test show that the average value of tensile strength and impact strength from the results of the confirmation experiment is the same as the value of the tensile strength and impact strength of the optimization prediction.