Empirical Analysis of Deep Neural Networks for Classifying and Identifying 316L and AZ31B Mg Metal Surface Morphology
Description
Deep neural networks (DNNs) have demonstrated remarkable capabilities in image classification and pattern recognition, making them well-suited for material surface morphology analysis. This study presents an empirical analysis of DNNs for classifying and identifying surface morphology of 316L stainless steel and AZ31B magnesium alloy. High-resolution microscopic images of both metals were obtained and preprocessed to enhance feature extraction. Multiple DNN architectures, including convolutional neural networks (CNNs), were trained and evaluated to determine their efficacy in distinguishing surface textures influenced by processing methods such as machining, etching, and corrosion. Performance metrics such as accuracy, precision, recall, and F1-score were analyzed to assess model effectiveness. The results indicate that DNN models can successfully differentiate between the two metal surfaces with high accuracy, providing a robust framework for automated material characterization. These findings contribute to the advancement of intelligent material inspection systems, reducing manual effort and improving the reliability of surface morphology assessments in industrial applications.