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 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. This proposed prediction method lays the foundation for metal tube bending wrinkling detection.

Machine Learning for Predicting Fracture Strain in Sheet Metal Forming

“…Applications of artificial intelligence (AI) techniques have recently been proposed to predict, detect, and classify the occurrence of defects in metal forming processes [18][19][20][21][22][23][24][25][26][27][28][29]. Among these, there are AI-based techniques that have been used to classify surface defects of hot rolling strips based on techniques such as generative adversarial networks (GAN) [22] and convolutional neural networks (CNN) [23,24], which rely on datasets of collected defects images; CNN-based approaches used to predict the buckling instability of automotive sheet metal panels [25]; machine learning-based techniques used to predict and account for springback in steel and aluminium parts [26][27][28], as well as for predicting wrinkling [29].…”