Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm
In the process of production, automobile steel forgings are prone to various cracks, which affect the product quality. At present, forgings defects are mainly detected by fluorescent magnetic particle inspection and manual inspection. Aiming at the problems of low detection accuracy and efficiency i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9839575/ |
| _version_ | 1828519184550592512 |
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| author | Tang Yu Wang Chen Gao Junfeng Hua Poxi |
| author_facet | Tang Yu Wang Chen Gao Junfeng Hua Poxi |
| author_sort | Tang Yu |
| collection | DOAJ |
| description | In the process of production, automobile steel forgings are prone to various cracks, which affect the product quality. At present, forgings defects are mainly detected by fluorescent magnetic particle inspection and manual inspection. Aiming at the problems of low detection accuracy and efficiency in this method, an improved convolutional neural network model is proposed. The fluorescent magnetic particle inspection images of two typical forgings were intelligently inspected. Firstly, a deep learning model with EfficientNet as the backbone and Feature Pyramid Network (FPN) as the fusion layer is constructed. Secondly, in order to improve the convergence speed and detection accuracy, the calculation method of intersection over union is improved, and the network is improved by using the Attention Mechanism. Finally, Particle Swarm Optimization algorithm (PSO) with adaptive parameters is introduced to optimize the hyperparameters of neural network, and a fluorescent magnetic particle inspection image acquisition platform is built for verification. The mean Average Precision (mAP) of the best model of EfficientNet-PSO on the validation set is 95.69%. F1 score is 0.94 and FLOPs is 1.86B. Compared with other five deep learning neural network models, this method effectively improves the defect detection efficiency and accuracy of flange plate and cylinder head, which can meet the defect detection requirements. |
| first_indexed | 2024-12-11T19:08:52Z |
| format | Article |
| id | doaj.art-32afd21318f3406a8c31bac4269667d4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T19:08:52Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-32afd21318f3406a8c31bac4269667d42022-12-22T00:53:50ZengIEEEIEEE Access2169-35362022-01-0110795537956310.1109/ACCESS.2022.31936769839575Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic AlgorithmTang Yu0https://orcid.org/0000-0003-2495-4186Wang Chen1Gao Junfeng2Hua Poxi3Department of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, ChinaDepartment of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, ChinaIndustrial Product Quality Inspection and Testing Institute, Shiyan, ChinaDepartment of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, ChinaIn the process of production, automobile steel forgings are prone to various cracks, which affect the product quality. At present, forgings defects are mainly detected by fluorescent magnetic particle inspection and manual inspection. Aiming at the problems of low detection accuracy and efficiency in this method, an improved convolutional neural network model is proposed. The fluorescent magnetic particle inspection images of two typical forgings were intelligently inspected. Firstly, a deep learning model with EfficientNet as the backbone and Feature Pyramid Network (FPN) as the fusion layer is constructed. Secondly, in order to improve the convergence speed and detection accuracy, the calculation method of intersection over union is improved, and the network is improved by using the Attention Mechanism. Finally, Particle Swarm Optimization algorithm (PSO) with adaptive parameters is introduced to optimize the hyperparameters of neural network, and a fluorescent magnetic particle inspection image acquisition platform is built for verification. The mean Average Precision (mAP) of the best model of EfficientNet-PSO on the validation set is 95.69%. F1 score is 0.94 and FLOPs is 1.86B. Compared with other five deep learning neural network models, this method effectively improves the defect detection efficiency and accuracy of flange plate and cylinder head, which can meet the defect detection requirements.https://ieeexplore.ieee.org/document/9839575/Machine learningindustry applicationsobject detection |
| spellingShingle | Tang Yu Wang Chen Gao Junfeng Hua Poxi Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm IEEE Access Machine learning industry applications object detection |
| title | Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm |
| title_full | Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm |
| title_fullStr | Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm |
| title_full_unstemmed | Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm |
| title_short | Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm |
| title_sort | intelligent detection method of forgings defects detection based on improved efficientnet and memetic algorithm |
| topic | Machine learning industry applications object detection |
| url | https://ieeexplore.ieee.org/document/9839575/ |
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