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Defect Feature Analysis and Influence of Defect on Properties of High Pressure Cast Aluminum Alloy Based on Image Recognition
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Zhongyuan Qiu1, 2, Liangxi Pu2, 3, Yutong Yang2, 3, Xianhui Wang1, Qiufeng Wang3, Shiyao Huang2, 4
Automobile Technology & Material | 2023, (8) : 1 - 6
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Automobile Technology & Material | 2023, (8): 1-6
Defect Feature Analysis and Influence of Defect on Properties of High Pressure Cast Aluminum Alloy Based on Image Recognition
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Zhongyuan Qiu1, 2, Liangxi Pu2, 3, Yutong Yang2, 3, Xianhui Wang1, Qiufeng Wang3, Shiyao Huang2, 4
Affiliations
  • 1 Nanjing University of Science and Technology, Nanjing 210009
  • 2 Yangtze Delta Region Institute of Advanced Materials, Suzhou 215000
  • 3 Xi’an Jiaotong-Liverpool University, Suzhou 215000
  • 4 Nanjing Tech University, Nanjing 210009
Published: 2023-08-20 doi: 10.19710/J.cnki.1003-8817.20230167
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In this paper, the influence of defects on mechanical properties of high-pressure die-cast aluminum alloy was studied by means of greenhouse stretching, scanning electron microscopy and automatic defect identification and statistics program based on deep learning and threshold segmentation. The results show that the mechanical properties of high-pressure die-cast aluminum alloy castings fluctuate at different positions. The accuracy of the image recognition program was verified by comparing the results of the image recognition program and the manual statistics of the defect area of the fracture. The relationship between the fracture defect area and mechanical properties shows that the porosity and maximum defect size are correlated with the elongation. When the porosity or maximum defect size increases, the elongation of high-pressure cast aluminum alloy shows a downward trend.

High-pressure die casting aluminum alloy  /  Image recognition  /  Defect feature  /  Elongation
Zhongyuan Qiu, Liangxi Pu, Yutong Yang, Xianhui Wang, Qiufeng Wang, Shiyao Huang. Defect Feature Analysis and Influence of Defect on Properties of High Pressure Cast Aluminum Alloy Based on Image Recognition[J]. Automobile Technology & Material, 2023 , (8) : 1 -6 . DOI: 10.19710/J.cnki.1003-8817.20230167
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doi: 10.19710/J.cnki.1003-8817.20230167
  • Online Date：2026-01-12
  • Published：2023-08-20
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Affiliations
    1 Nanjing University of Science and Technology, Nanjing 210009
    2 Yangtze Delta Region Institute of Advanced Materials, Suzhou 215000
    3 Xi’an Jiaotong-Liverpool University, Suzhou 215000
    4 Nanjing Tech University, Nanjing 210009
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https://castjournals.cast.org.cn/joweb/qcgyycl/EN/10.19710/J.cnki.1003-8817.20230167
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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