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Application of artificial intelligence in microstructure image recognition and quantification of metallic materials
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Chunguang SHEN1, 2, Shuo SUN1, 2, Wei XU3, *, Shijian ZHENG1, 2, *
Science & Technology Review | 2025, 43(24) : 44 - 60
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Science & Technology Review | 2025, 43(24): 44-60
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Application of artificial intelligence in microstructure image recognition and quantification of metallic materials
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Chunguang SHEN1, 2, Shuo SUN1, 2, Wei XU3, *, Shijian ZHENG1, 2, *
Affiliations
  • 1State Key Laboratory of High−Performance Roll Materials and Composite Forming, Hebei University of Technology, Tianjin 300401, China
  • 2Tianjin Key Laboratory of Material Layered Composite and Interface Control Technology, Hebei University of Technology, Tianjin 300401, China
  • 3State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China
Published: 2025-12-28 doi: 10.3981/j.issn.1000-7857.2025.09.00122
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Artificial intelligence (AI) technology is profoundly transforming the research paradigms in the field of materials science, driving the analysis methods for material microstructures to shift from traditional human−experience−dominated approaches to data−driven intelligent recognition. AI−based microstructure recognition and quantification, characterized by high accuracy and efficiency, have significantly advanced the development of high−throughput microstructure analysis techniques. This review focuses on the emerging field of AI−assisted microstructure analysis of metallic materials. Following the development from qualitative analysis toward refined quantitative analysis of microstructures, it systematically summarizes the research progress in traditional machine learning algorithms, deep learning−based classification, object detection, and semantic segmentation algorithms for the classification, recognition, and quantification of metallic material microstructures. Particular emphasis is placed on the current state of widely adopted semantic segmentation algorithms. Meanwhile, addressing the challenges faced by semantic segmentation in this domain, such as high microstructural complexity and limited annotated samples, the innovative strategies proposed by researchers in data augmentation and model architecture improvements, along with their enhanced performance, are discussed. Finally, the existing limitations and future directions of AI−based microstructure analysis methods are summarized and outlooked.

artificial intelligence  /  metallic materials  /  microstructure  /  computer vision  /  image recognition  /  quantification
Chunguang SHEN, Shuo SUN, Wei XU, Shijian ZHENG. Application of artificial intelligence in microstructure image recognition and quantification of metallic materials[J]. Science & Technology Review, 2025 , 43 (24) : 44 -60 . DOI: 10.3981/j.issn.1000-7857.2025.09.00122
Year 2025 volume 43 Issue 24
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doi: 10.3981/j.issn.1000-7857.2025.09.00122
  • Receive Date:2025-09-29
  • Online Date:2026-01-14
  • Published:2025-12-28
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  • Received:2025-09-29
  • Revised:2025-11-10
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Affiliations
    1State Key Laboratory of High−Performance Roll Materials and Composite Forming, Hebei University of Technology, Tianjin 300401, China
    2Tianjin Key Laboratory of Material Layered Composite and Interface Control Technology, Hebei University of Technology, Tianjin 300401, China
    3State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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