Data-driven quality prediction in injection molding: An autoencoder and machine learning approach

In the injection molding process, the pressure within the mold cavity is crucial to the quality of the final product. Due to the inability to directly observe the process, sensor technology is required to acquire data. Traditionally, experts interpret and encode pressure curves, but this method has limitations. This study proposes an innovative pressure curve encoding technique to overcome these limitations and achieve automation to obtain more comprehensive pressure information. The study employs mold flow analysis software and autoencoders to capture and encode pressure data, classifying pressure curves into global pressure and local pressure values. Subsequently, a multilayer perceptron (MLP) neural network is used for machine learning to predict multiple qualities. Results indicate that local pressure features perform better in predicting multiple-quality targets than global pressure features, exhibiting smaller prediction ranges and higher prediction stability. Although domain knowledge-based indicator features slightly outperform in terms of predictive capability, the low error results of the local pressure feature method validate the effectiveness of the autoencoder approach, demonstrating its potential for digital information extraction and practical quality prediction in the injection molding process. Highlights: Develops a product quality prediction system for efficient injection molding. Autoencoders extract key features from pressure data without domain knowledge. ML models predict quality indicators, optimizing injection molding processes. Compares pressure features, showing data-driven methods' prediction accuracy.