Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm
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 b...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2025-03-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.03.014 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850269723745320960 |
|---|---|
| author | FAN Meng TONG Bo GAO Chen YAO Zhongyuan ZHANG Yu HU Bo |
| author_facet | FAN Meng TONG Bo GAO Chen YAO Zhongyuan ZHANG Yu HU Bo |
| author_sort | FAN Meng |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-e57457aa10d74331b9253023043802db |
| institution | OA Journals |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-e57457aa10d74331b9253023043802db2025-08-20T01:52:59ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692025-03-014711312088383266Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithmFAN MengTONG BoGAO ChenYAO ZhongyuanZHANG YuHU BoMetal 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%.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.03.014Weak magnetic detectionMetal structureBP neural networkWhale algorithmIWOA-BP neural network |
| spellingShingle | FAN Meng TONG Bo GAO Chen YAO Zhongyuan ZHANG Yu HU Bo Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm Jixie qiangdu Weak magnetic detection Metal structure BP neural network Whale algorithm IWOA-BP neural network |
| title | Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm |
| title_full | Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm |
| title_fullStr | Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm |
| title_full_unstemmed | Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm |
| title_short | Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm |
| title_sort | quantitative study on weak magnetic detection defects of metal structure based on iwoa bp algorithm |
| topic | Weak magnetic detection Metal structure BP neural network Whale algorithm IWOA-BP neural network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.03.014 |
| work_keys_str_mv | AT fanmeng quantitativestudyonweakmagneticdetectiondefectsofmetalstructurebasedoniwoabpalgorithm AT tongbo quantitativestudyonweakmagneticdetectiondefectsofmetalstructurebasedoniwoabpalgorithm AT gaochen quantitativestudyonweakmagneticdetectiondefectsofmetalstructurebasedoniwoabpalgorithm AT yaozhongyuan quantitativestudyonweakmagneticdetectiondefectsofmetalstructurebasedoniwoabpalgorithm AT zhangyu quantitativestudyonweakmagneticdetectiondefectsofmetalstructurebasedoniwoabpalgorithm AT hubo quantitativestudyonweakmagneticdetectiondefectsofmetalstructurebasedoniwoabpalgorithm |