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A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies

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  • Fengtian Chang

    (Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Guanghui Zhou

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Kai Ding

    (Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Jintao Li

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yanzhen Jing

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jizhuang Hui

    (Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Chao Zhang

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

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.

Suggested Citation

  • Fengtian Chang & Guanghui Zhou & Kai Ding & Jintao Li & Yanzhen Jing & Jizhuang Hui & Chao Zhang, 2023. "A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies," Mathematics, MDPI, vol. 11(22), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4570-:d:1275762
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    References listed on IDEAS

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    1. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
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