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Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm
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Meng FAN1, Bo TONG2, Chen GAO2, Zhongyuan YAO3, Yu ZHANG3, Bo HU1
Journal of Mechanical Strength | 2025, 47(3) : 113 - 120
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Journal of Mechanical Strength | 2025, 47(3): 113-120
• ·Vibration·Noise·Monitoring·Diagnosis· •
Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm
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Meng FAN1, Bo TONG2, Chen GAO2, Zhongyuan YAO3, Yu ZHANG3, Bo HU1
Affiliations
  • 1.Key Laboratory of Non-Destructive Testing of Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China
  • 2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
  • 3.Jiangsu Clean Energy Branch, Huaneng Power International Inc., Nanjing 210015, China
Published: 2025-03-15 doi: 10.16579/j.issn.1001.9669.2025.03.014
Outline
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Metal structures are widely used in industry. Metal structures in service are prone to crack defects under tensile and compressive fatigue load.In order to realize quantitative detection of metal structures’ crack defects, a quantitative analysis method of metal structures’ weak magnetic detection based on back propagation (BP) neural network was studied. In view of the poor effect and low efficiency of BP neural network in parameter adjustment, the improved whale optimization algorithm (IWOA) based on Sine chaotic mapping was adopted to optimize the BP neural network parameter adjustment mode,giving consideration to global optimization while improving the local optimization ability, and then the optimal parameters searched by IWOA were assigned to BP neural network, improving the quality of initial network parameters.The length, width and depth of the artificial rectangular slot were quantified by inversion. The results show that the average prediction accuracy of IWOA-BP neural network is above 80%, and the prediction accuracy of depth, length and width is improved respectively by 106.72%, 9.68% and 6.86%.

Weak magnetic detection  /  Metal structure  /  BP neural network  /  Whale algorithm  /  IWOA-BP neural network
Meng FAN, Bo TONG, Chen GAO, Zhongyuan YAO, Yu ZHANG, Bo HU. Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm[J]. Journal of Mechanical Strength, 2025 , 47 (3) : 113 -120 . DOI: 10.16579/j.issn.1001.9669.2025.03.014
Funding
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  • China Huaneng Group Headquarters Technology Project(HNKJ20-H72)
  • Graduate Innovation Foundation of Nanchang Hangkong University(YC2022-088)
  • Key Research and Development Program of Jiangxi Province(20243BBG71005)
  • Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province(20243BCE51052)
Appendix
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Year 2025 volume 47 Issue 3
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67
33
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Article Info
doi: 10.16579/j.issn.1001.9669.2025.03.014
  • Receive Date：2023-03-14
  • Online Date：2026-03-17
  • Published：2025-03-15
Article Data
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History
  • Received：2023-03-14
  • Revised：2023-08-23
Funding
China Huaneng Group Headquarters Technology Project(HNKJ20-H72)
Graduate Innovation Foundation of Nanchang Hangkong University(YC2022-088)
Key Research and Development Program of Jiangxi Province(20243BBG71005)
Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province(20243BCE51052)
Affiliations
    1.Key Laboratory of Non-Destructive Testing of Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China
    2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
    3.Jiangsu Clean Energy Branch, Huaneng Power International Inc., Nanjing 210015, China

Corresponding:

HU Bo, E-mail：
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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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