Torque based defect detection and weld quality modelling in friction stir welding process

Efforts have been made towards the monitoring of friction stir welding (FSW) process using real-time torque signals in this research work. Signals were analyzed using discrete wavelet transform and statistical features namely dispersion, asymmetry and excess are computed. The computed features are further processed to develop effective methodology for internal defect identification in FSW process. A new indicator has been proposed combining the computed statistical features. The proposed indicator shows appreciable deviations for defective and defect free welds. Apart from defect detection using the computed signal features, they are also presented as inputs to a support vector machine learning based modelling tool for the prediction of ultimate tensile strength of the welded joints. The prediction accuracy of the model with computed signal features are found to be more than the model developed with process parameters. The comparison of the developed support vector regression (SVR) model with artificial neural network (ANN) model and general regression model yields that prediction performance of SVR is superior to ANN and general regression model. The proposed work can be modified for its successful use in real-time modelling of friction stir welding process.