Work

Data-Efficient Machine Learning for Straightening Processes in Roll Forming Using FEA Data

Year: 2026

Type: article

Abstract: Roll forming is a sheet metal forming process characterised by high production rates and high material utilisation. Process-inherent inhomogeneous longitudinal strain distribution across the profiles cross-section requires the use of straighteners to maintain product quality within specifications during manufacturing. The straightening process is adjusted iteratively based on the expertise of line operators. A shortage of skilled workers leads to a loss of experience-based know-how in roll forming operations. To mitigate the effects on process productivity, machine learning (ML) can be applied to provide assistance to less experienced operators. In this context, force and position signals are recorded on a sensorically equipped straightener in order to predict corrective adjustments for line operators using convolutional neural networks. Furthermore, the integration of numerically generated data is investigated to reduce the required amount of labelled experimental data. Applying a Transfer Learning (TL) approach, the incorporation of numerical data reduces the mean error by 20.81% and the mean standard deviation by 36.62% for small experimental datasets.

Source: Applied Sciences

DOI: 10.3390/app16105024

Authors: Johannes Hofmann, Patrick Reining, Peter Groche

Institution: Technische Universität Darmstadt

Language: English

Topic: Metal Forming Simulation Techniques

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896