Image Processing Technology and Algorithm for Microstructure Evolution of Aluminum Alloy Wire

Aluminum alloy wires play a crucial role in high-voltage transmission lines and power supply lines due to their outstanding cost-effectiveness, excellent oxidation resistance, and relatively low density. The performance of aluminum alloy wires is closely related to their microstructure, in which the size, morphology, orientation of grains, and distribution of different phases are key factors affecting the material's conductivity, mechanical strength, and corrosion resistance. In addition, defects such as microcracks and inclusions that may exist inside the material can also have adverse effects on its overall performance. This article focuses on the research of image processing technology for the microstructure evolution of aluminum alloy wires, aiming to achieve accurate identification and quantitative evaluation of material microstructure through advanced image analysis methods. On this basis, this article proposes an image recognition algorithm based on deep learning (DL), which can automatically learn and extract microstructural features, achieving efficient and accurate classification and recognition of the microstructure of aluminum alloy wires. The experimental results show that the algorithm can significantly improve the accuracy and efficiency of image processing.