Deep learning in alloy material microstructures: Application and prospects

This review explores the applications, challenges, and prospects of deep learning in the microstructure analysis of alloy materials. First, it introduces the significance of alloy materials in modern industry, along with the continuous advancements in microscopy techniques and deep learning. Next, it briefly outlines the fundamental concepts and workflow of deep learning. In the critical section of this review, it elucidates how deep learning, through learning and training, can extract features from a large volume of alloy microstructure data and accurately classify these microstructures. Furthermore, deep learning can also be applied to tasks such as image segmentation in alloy microstructure images, including object detection and instance segmentation, facilitating the extraction of pertinent information from complex images. This, in turn, allows the establishment of the microstructure-performance relationship for effective performance prediction. Finally, it summarizes the prospects of deep learning in alloy material applications and the challenges related to data acquisition, model training,and performance optimization.

Automated summary

This review explores the applications, challenges, and prospects of deep learning in the microstructure analysis of alloy materials. It highlights how deep learning can extract features from a large volume of alloy microstructure data and accurately classify them, leading to the establishment of the microstructure-performance relationship for effective performance prediction. It also discusses the use of deep learning in tasks like image segmentation in alloy microstructure images, facilitating the extraction of pertinent information from complex images.