A hierarchical prediction method based on hybrid-kernel GWO-SVM for metal tube bending wrinkling detection

Abstract

Metal bending tube is widely used in industry while its forming defects extremely affect the bending quality. Among all defects, the bending-inside wrinkling caused by the non-uniform compressive stress is a zero-tolerated defect, particularly when the tube is for transportation. However, the current wrinkling detection approach, suffering from the lack of insight into wrinkling mechanism, is normally posteriori. To obtain the priori wrinkling condition for a certain go-to-bend tube, we put forward a metal tube bending wrinkling hierarchical prediction method based on hybrid-kernel gray wolf optimizer (GWO) support vector machine (SVM). Three typical kernel combinations are utilized for the GWO-SVM prediction model. To verify the proposed wrinkling prediction method, aluminum alloy series tubes are tested. By constructing the 12 typical designations of aluminum alloy tubes’ finite element bending simulation case base, the prediction model is trained through three hybrid-kernel GWO-SVMs, respectively. The results are compared with the traditional SVM and GWO-SVM, which show that the proposed hybrid-kernel GWO-SVM model has the best performance for hierarchically predicting bending wrinkling. Analysis of the predicted results shows that when the relative wall thickness is less than 0.015, wrinkling is very likely to occur with any relative bending radius within the range. On the contrary, there is less tendency to wrinkle. At the same time, the smaller the R/D, the higher the hierarchy of wrinkling. This proposed prediction method lays the foundation for metal tube bending wrinkling detection and prevention.