Improving Industrial Injection Molding Processes with Explainable AI for Quality Classification
This work addresses the challenge of deploying AI in manufacturing with constrained sensor technology, offering an incremental improvement for more efficient and interpretable quality control.
The paper tackled the problem of limited interpretability and incomplete data in machine learning for industrial quality control by applying explainable AI techniques to reduce features in an LSTM model for injection molding classification, resulting in improved generalization and maintained high performance with a small speed increase.
Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.