Machine learning-aided network for process-property prediction of injection-molded polyamide-6 composite parts

Abstract

Although process parameters significantly affect mechanical properties of injection-molded parts, optimizing them is challenging due to cost/time-intensive works for engineering applications. While design of experiments (DOE) is commonly used for process design, it still cannot fully address these challenges without understanding fundamental processing mechanisms. Therefore, this study proposes a machine learning-aided process-property prediction network (PPN), enabling reliable virtual experiments. PPN, integrating multilayer perceptron (MLP) and long short-term memory (LSTM) models, was developed using 135 DOE-based experiments on injection-molded polyamide-6 composite parts, compounded with aluminum diethylphosphinate and chopped carbon fibers, considering injection pressure, injection speed, packing time and packing pressure. PPN demonstrated superior predictive accuracy with a mean absolute percentage error (MAPE) of 0.0557, outperforming previous studies employing single model approaches (0.281 for MLP and 0.147 for LSTM). PPN also achieved a low root mean squared error (RMSE) of 1.1904 and high coefficients of determination (R-2 > 0.832) for predicted tensile properties with a prediction uncertainty under 2.02 % quantified by a deep ensemble technique. Notably, PPN not only enhanced prediction accuracy by up to 81.8 % compared to the conventional DOE, but also successfully predicted tensile properties for 1000 virtual experiments, providing results superior to the DOE analysis. This study demonstrated that PPN can effectively predict tensile properties for unseen process parameters, showing its potential for rapid design optimization by enabling comprehensive virtual experiments.