A Recurrent Neural Network for Warpage Prediction in Injection Molding

Abstract

Injection molding is classified as one of the most flexible and economical manufacturing processes with high volumeof plastic molded parts. Causes of variations in the process are related to the vast number of factors acting during aregular production run, which directly impacts the quality of final products. A common quality trouble in finishedproducts is the presence of warpage. Thus, this study aimed to design a system based on recurrent neural networksto predict warpage defects in products manufactured through injection molding. Five process parameters areemployed for being considered to be critical and have a great impact on the warpage of plastic components. Thisstudy used the finite element analysis software Moldflow to simulate the injection molding process to collect data inorder to train and test the recurrent neural network. Recurrent neural networks were used to understand the dynamicsof the process and due to their memorization ability, warpage values might be predicted accurately. Results show thedesigned network works well in prediction tasks, overcoming those predictions generated by feedforward neuralnetworks.