A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies
Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solv...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/22/4570 |
| _version_ | 1797458501929271296 |
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| author | Fengtian Chang Guanghui Zhou Kai Ding Jintao Li Yanzhen Jing Jizhuang Hui Chao Zhang |
| author_facet | Fengtian Chang Guanghui Zhou Kai Ding Jintao Li Yanzhen Jing Jizhuang Hui Chao Zhang |
| author_sort | Fengtian Chang |
| collection | DOAJ |
| description | Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies. |
| first_indexed | 2024-03-09T16:38:59Z |
| format | Article |
| id | doaj.art-40cda72129554c3e8f63d7306f9eed52 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T16:38:59Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-40cda72129554c3e8f63d7306f9eed522023-11-24T14:54:02ZengMDPI AGMathematics2227-73902023-11-011122457010.3390/math11224570A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive BodiesFengtian Chang0Guanghui Zhou1Kai Ding2Jintao Li3Yanzhen Jing4Jizhuang Hui5Chao Zhang6Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaInstitute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaInstitute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaResistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.https://www.mdpi.com/2227-7390/11/22/4570automotive bodiesresistance spot weldingwelding qualityonline detectionedge–cloud collaboration |
| spellingShingle | Fengtian Chang Guanghui Zhou Kai Ding Jintao Li Yanzhen Jing Jizhuang Hui Chao Zhang A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies Mathematics automotive bodies resistance spot welding welding quality online detection edge–cloud collaboration |
| title | A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies |
| title_full | A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies |
| title_fullStr | A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies |
| title_full_unstemmed | A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies |
| title_short | A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies |
| title_sort | cnn lstm and attention mechanism based resistance spot welding quality online detection method for automotive bodies |
| topic | automotive bodies resistance spot welding welding quality online detection edge–cloud collaboration |
| url | https://www.mdpi.com/2227-7390/11/22/4570 |
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