Deep learning–based inline monitoring approach of mold coating thickness for Al-Si alloy permanent mold casting

Abstract

In the permanent mold casting process, the distribution of mold coating thickness is a significant variable with respect to the coating’s thermal resistance, as it strongly influences the mechanical properties of cast parts and the thermal erosion of expensive molds. However, efficient online coating thickness measurement is challenging due to the high working temperatures of the molds. To address this, we propose an indirect monitoring concept based on the analysis of the as-cast surface corresponding to the coated area. Our previous research proves linear correlations between the as-cast surface roughness parameter known as arithmetical mean height (Sa) and the coating thickness for various coating materials. Based on these correlations, we can derive the coating thickness from the analysis of the corresponding as-cast surface. In this work, we introduce a method to quickly evaluate the as-cast surface roughness by analyzing optical images with a deep-learning model. We tested six different models due to their high accuracies on ImageNet: Vision Transformer (ViT), Multi-Axis Vision Transformer (MaxViT), EfficientNetV2-S/M, MobileNetV3, Densely Connected Convolutional Networks (DenseNet), and Wide Residual Networks (Wide ResNet). The results show that the Wide ResNet50-2 model achieves the lowest mean absolute error (MAE) value of 1.060 µm and the highest R-squared (R2) value of 0.918, and EfficientNetV2-M reaches the highest prediction accuracy of 98.39% on the test set. The absolute error of the surface roughness prediction remains well within an acceptable tolerance of ca. 2 µm for the top three models. The findings presented in this paper hold significant importance for the development of an affordable and efficient online method to evaluate mold coating thickness. In future work, we plan to enrich the sample dataset to further enhance the stability of prediction accuracy.