Product Mass Prediction with Artificial Neural Network Model Approach in Injection Plastic Molding Moh. Hartono (a*), Anggit Murdani (a), Ramadhan Araya Ismoyo (a)
a) Department of Mechanical Engineering, State Polytechnic of Malang, Jl. Soekarno Hatta, No. 9, Malang 65141, Indonesia
*moh.hartono[at]polinema.ac.id
anggitm[at]polinema.ac.id
rama.araya14[at]gmail.com
Abstract
Nowadays, the injection molding process is proliferating, which is characterized by the rise of standard equipment produced by injection molding, such as household appliances, carpentry tools, and medical equipment. Setting process parameters significantly affects product quality, especially packaging products that consider product mass. This study aims to minimize product mass and provide optimal parameter recommendations. The method built in this research is the Artificial Neural Network (ANN) model, which is one of the branches of Artificial intelligence (AI) with this modeling, several experiments were analyzed to predict the mass of products from an injection molding process accurately and precisely. The results of this study show that the ANN model can predict the mass of the product accurately with a low RMSE value. In addition, experiment III shows the results with the lowest product mass compared to experiments I and II.
