Study of spot distance on resistance spot welding quality: a 1DCNN-BiLSTM-Attention-based online inspection method

Abstract Resistance spot welding (RSW) is widely employed in the automotive and home appliance industries due to its high efficiency, low cost, and suitability for automation. However, traditional quality detection methods rely on destructive testing, leading to inefficiencies and resource wastage. This paper presents a novel quality inspection model for RSW that utilizes a one-dimensional convolutional neural network, bidirectional long short-term memory network, and attention mechanism (1DCNN-BiLSTM-Attention) to address the challenges of extracting temporal data under varying spot distances. The model integrates a residual linking mechanism and Kolmogorov–Arnold networks to enhance feature extraction and performance. Experimental results reveal that the model demonstrates strong predictive capabilities across different spot distances, with particularly notable performance at 10 mm spacing, achieving a mean absolute error of 0.0632, a root mean square error of 0.0603, and an R 2 value of 0.7513. These findings underscore the model’s ability to provide high-precision predictions, even under conditions influenced by significant shunt effects.