Study on the Recognition of Metallurgical Graphs Based on Deep Learning

Artificial intelligence has been widely applied in image recognition and segmentation, achieving significant results. However, its application in the field of materials science is relatively limited. Metallography is an important technique for characterizing the macroscopic and microscopic structures of metals and alloys. It plays a crucial role in correlating material properties. Therefore, this study investigates the utilization of deep learning techniques for the recognition of metallo-graphic images. This study selected microscopic images of three typical cast irons, including ductile, gray, and white ones, and another alloy, cast aluminum alloy, from the ASM database for recognition investigation. These images were cut and enhanced for training. In addition to coarse classification of material type, fine classification of material type, composition, and the conditions of image acquisition such as microscope, magnification, and etchant was performed. The MobileNetV2 network was adopted as the model for training and prediction, and ImageNet was used as the dataset for pre-training to improve the accuracy. The metallographic images could be classified into 15 categories by the trained neural networks. The accuracy of validation and prediction for fine classification reached 94.44% and 93.87%, respectively. This indicates that neural networks have the potential to identify types of materials with details of microscope, magnification, etchants, etc., supplemental to compositions for metallographic images.