Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method.

Resistance spot welding (RSW) ultrasonic testing signal contains nugget size and internal defect information which can reflect the mechanical property of spot-welded joint. The mechanical property of spot-welded joint is the most direct indicator for evaluation of spot welding quality. In this paper, 100 samples of different quality spot-welded joints are detected by ultrasonic detection technology, then ultrasonic signals are processed by fast Fourier transform (FFT) and wavelet packet transform (WPT). After that, mathematical statistical methods are used to feature extraction for ultrasonic detection signals in time domain, frequency domain, and wavelet domain based on WPT. 100 samples are subjected to tensile-shear tests to obtain the maximum tensile-shear strength (MTSS) that is used as the classification identifier here. Finally, back-propagation (BP) neural network classifier and particle swarm optimization support vector machine (PSO-SVM) classifier are used to classify the MTSS of spot-welded joints and comparing the accuracy of the two classifiers with different number of features. The results show that the PSO-SVM classifier with all 9 features has a good accuracy, which verifies the feasibility and correctness of the spot welding quality classification method proposed in this paper.