Research and Improvement of the Generalization Performance of Spot Welding Quality Diagnosis Model

doi:10.3901/JME.2025.18.064

Abstract

Abstract: Resistance spot welding (RSW) is a fundamental process in thin-walled structure manufacturing. Utilizing process signals during welding to achieve online quality diagnosis of the welds is a crucial approach to ensuring product reliability and advancing the transformation and upgrade of manufacturing modes. However, assembly deviations in mass production can lead to random fluctuations in welding conditions, impacting the quality of joint formation. Enhancing the generalization ability of the quality diagnosis model from normal conditions to abnormal ones is the key to improving the reliability of online detection. In this paper, a dataset of weld quality label under various conditions and multi-sensor signal data is established. A feature extraction method for sensor signals based on an autoencoder and domain adversarial training is proposed. The process signal is compressed into a feature vector in a high-dimensional feature space through the autoencoder, and the extraction of features with strong expressive ability is realized by combining the attention mechanism. Domain adversarial training is then utilized to reduce distributional differences in signal features across different conditions, thereby improving cross-condition quality diagnosis accuracy. Data from normal and abnormal welding conditions are used as training and test samples, respectively, to compare the generalization performance of the proposed method against manual methods and three commonly used feature extraction algorithms(PCA, Isomap, and LLE)in the task of spot weld quality diagnosis. Results show that the proposed method improves quality diagnosis accuracy in the abnormal condition test set from 83.18% to 93.64%. The root mean square error (RMSE) for nugget diameter prediction is reduced by nearly 0.33 mm compared the other four methods, effectively enhancing the generalization ability of the quality diagnosis model for out-of-distribution data.

Key words: resistance spot welding, online quality diagnosis, attention mechanism, domain adversarial network, out-of-distribution generalization

CLC Number:

TG441

SONG Qiang, WANG Wenjie, HE Xiaobin, XIA Yujun, LI Yongbing. Research and Improvement of the Generalization Performance of Spot Welding Quality Diagnosis Model[J]. Journal of Mechanical Engineering, 2025, 61(18): 64-75.

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