‘Data-Driven Modelling of Metal Bending Processes’ portrays a sub-project of the priority program ‘Data-Driven Process Modelling in Metal Forming Technology’, funded by the DFG. In order to improve the quality of complex bent wire parts, it is possible to combine multi-stage mechatronic straighteners with similar bending units. However, controlling cross-stage and quantity-dependent effects of such mechatronic bending machines (MSA) represents a challenging task to experts. Machine Learning surrogate models can help to tackle this challenge by modelling defects and track data across multiple stages and piece numbers. The goal of this project is the construction of an explainable, hybrid surrogate model that allows the integration of domain knowledge such that experts have the possibility of interactively analysing accruing data of the MSA. In this project, we are collaborating with the Fraunhofer (IEM) and the University of Paderborn.