Hybrid Approach Integrating Deep Learning-Autoencoder With Statistical Process Control Chart for Anomaly Detection: Case Study in Injection Molding Process

Detecting anomalies in the injection molding process remains a challenging task, demanding significant resources, data, and expertise due to their impact on cost and time reduction. While traditional methods like statistical process control (SPC) using control charts are widely used for detecting irregularities, they can catch predefined patterns such as systematic, upward shift, downward shift, cyclic, and mixture patterns. However, they still have limitations in identifying anomalies beyond theses common patterns. Numerous unnatural patterns may exist in process data, indicating that the process is out of control. In our study, we propose an innovative strategy to enhance anomaly detection by integrating Statistical Process Control (SPC) with a Long Short-Term Memory (LSTM) based Autoencoder (AE). The main objective is to detect variations in the Melt cushion parameter, a crucial aspect of the injection molding process. The LSTM-AE model determines optimal threshold levels based on reconstruction loss rates across all time-series sequences, complementing traditional control charts’ upper and lower limits. With a model achieving a coefficient of determination (R-squared) of 0.993 and a Mean Absolute Error of 0.0146 through training, and incorporating multiple process limits—Upper Limit Control (ULC), Lower Limit Control (LLC), Maximum Threshold, and Minimum Threshold—the enhanced control chart exhibits significant advancements in anomaly detection. In four distinct scenarios, the integrated model demonstrates its capability to detect anomalies in the Melt cushion that exceed predefined limits, as well as anomalies showing ascending and descending trends. This ultimately enhances the robustness and efficiency of anomaly detection in injection molding processes.