Predicting and identifying factors affecting sheet metal bending times using explainable AI

Abstract. This study investigates the prediction of bending times in a sheet metal workshop using geometric information of parts without a bending sequence computation. Logs from two different sizes of bending machines from an industrial workshop are used, and data-centric machine-learning approaches are applied to achieve accurate predictions. Furthermore, the reduction in bending time with increasing production volume is analyzed as the learning effect in human-operated air bending. Explainable AI techniques, such as Partial Dependence Plots (PDP) and SHAP values, are used to interpret how model predictions are made. The learning effect is examined alongside geometric factors representing bending complexity: the number of bends and the total cutting length. The performances of XGBoost, random forest, and support vector machines (SVM) are compared in terms of bending time prediction, thereby demonstrating the approach's effectiveness.