Study on Classification of Tensile Shear Strength of Spot Welding Joints Based on PSO-SVM

Spot welding is one of the main connection methods of automobile parts, and the tensile shear strength of spot welded joints is the most important factor to evaluate the quality of spot welding. In this paper, based on the preparation of a large number of spot welding samples, ultrasonic signal detection is carried out on the spot welding points of each sample, and the time-domain, frequency-domain and wavelet packet eigenvalues are obtained by using signal processing methods. Then, by analyzing the failure form of spot welding specimen in tension shear test, the grading standard of tensile shear strength of spot welding joint is established. BP neural network and neural network classifier based on Particle Swarm Optimization Support Vector Machine (PSO-SVM) are designed according to the test data. Finally, the ultrasonic signal eigenvalue of the sample is used as the input parameter to compare the accuracy of the two neural network models for the classification of tensile shear strength of spot welding samples. The experimental results show that PSO-SVM neural network combined with 9 ultrasonic signal eigenvalues has the highest accuracy of spot welding strength classification.