Prediction of Springback Behavior of Vee Bending Process of AA5052 Aluminum Alloy Sheets Using Machine Learning

This study explores the effect of Vee bending process parameter on the springback (SB) behavior of aluminum (AA5052) alloy at sheet thickness of (2 and 3mm) with die-opening (22, 35, and 50 mm) and punch-holding time (0, 5 and 10 second) which were experimentally examined. Furthermore, to see the relative effect of process parameter on SB behavior, a qualitative approach of analysis of variance (ANOVA) was used, whereas multi linear regression (MLR) and artificial neural network (ANN) were applied to optimize the SB behavior on specified process parameters. The experimental results revealed that as punch holding time and sheet thickness increase, SB behavior reduced, whereas in case of die opening, opposite phenomena observed. ANOVA results revealed that punch-holding time had the greatest effect on SB, followed by die opening and sheet thickness. Two-way parametric interactional effects between punch-holding time and dieopening had a significant effect on SB behavior. By contrast, the interactional effects of sheet thickness were insignificant. The comparative study of MLR and ANN shows that The ANN has better (99% SB predictability) as compared to MLR (73% SB Predictability). Furthermore, the predicted results of both models were compared with actual experimental results. It was observed that the predicted results were approximately near with actual measurements, whereas the performance of MLR and ANN model were measured from sum of absolute error and the sum of the absolute error of ANN was about 12% of that of MLR model. Therefore, ANN produced a superior SB prediction performance compared with MLR. This work demonstrates the formability of AA5052 aluminum alloy in cold work where Vee bending was performed with a punch radius of 0.8 mm.The bend specimens showed no cracks, checking, and surface roughness. (c) 2023 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved