Early Porosity Detection in High-Pressure Die Casting Using Explainable Multimodal Data Fusion

Early and reliable detection of defects in the metal industry is crucial for improving quality, reducing costs and minimizing the environmental impact of manufacturing processes. In High-Pressure Die Casting (HPDC), porosity is one of the most severe defect types. This paper presents an explainable, multimodal approach to the early detection of porosity, which can be used as a preliminary step to reduce the number of parts requiring further, more time-consuming and expensive tests. Our work combines different data sources, such as machine sensor measurements, process time-series data and thermal images, to extract meaningful information and improve the final classification. The main classifier used is based on Fuzzy Cognitive Maps (FCMs), which provide a much higher interpretability of results and decisions, as well as of the model, compared to deep learning approaches. Building on our previous research, we have developed new feature extraction methods, classification approaches, and weight-selection strategies. Additionally, we investigated the DINOv2 foundation model for extracting features from thermal images and reducing the feature space using Principal Component Analysis (PCA). The experimental results show that the proposed approach improves defect detection performance compared to previous approaches and the HP-GAN algorithm without losing interpretability, allowing analysis of the relationship between input features and classification decisions. The proposed approach demonstrates how explainable artificial intelligence can support quality monitoring in real-life industrial use cases. Unfortunately, a performance advantage of the multimodal classifier over the strongest single-modality approach has not been demonstrated, and this will be further investigated as one of the future research directions.