Machine Learning for the Prediction of Springback in High Tensile Strength Steels after V-Bending Process Using Tree-Based Learning

Abstract

Sheet metal bending is a typical operation and springback is an unintended consequence of this operation. Since it causes fitting issues in the assembly, which leads to quality problems, anticipating it long before the bending operation is done is essential in today's production, so that machining parameters can be adjusted accordingly. In order to predict springback with minimum errors, this paper presents the idea for the development of machine learning models using tree-based learning algorithms (A class of machine learning algorithms). Tree-based learning algorithms are employed because they are precise, consistent, and easy to understand. Experimental studies provided the data for training and testing the models. The model's input parameters were sheet Material, Thickness, Width, Initial Angle (Desired angle), and Machine used to perform the bending. Following the training and testing of different tree-based learning algorithms, the results were evaluated using MAE and MSE. It was determined that Gradient boosting algorithms (a class of tree-based learning) gave the best results. Later on further evaluation of algorithms, it was found that LightGBM produced the best results, with MAE and MSE of 0.41 and 0.25, respectively.