Targeting the lap joint of multilayer aluminum tabs and an aluminum sheet,a real-time monitoring system for the laser welding process based on multi-band photoelectric coaxial sensing was established.Experiments on laser welding processes with different laser powers and defocusing conditions were conducted,and multi-band photoelectric signals under different laser energies were collected in real-time.Secondly,a wavelet scattering network(WSN)was used to extract multi-scale high-dimensional features from the raw signals.Combined with a long short-term memory(LSTM)network for temporal dynamic modeling,this approach ultimately achieves the goal of real-time detection of pseudo welding defects.The results indicate that,with a small sample size,the constructed WSN-LSTM model achieves an accuracy of 99.6%,and its classification performance surpasses that of other recurrent neural networks and lightweight convolutional neural network models.Additionally,the lightweight characteristic of the WSN-LSTM model results in the shortest training time,with an average processing time per sample of only 0.15 ms,making it advantageous for rapid deployment on power battery production lines and real-time detection of pseudo welding defects.
维普
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