Abstract
Accurate monitoring of draw-in behaviour during sheet metal forming is crucial for understanding material flow, optimizing process parameters, and validating finite element (FE) simulations. This study presents an integrated approach combining high-resolution optical measurement, laser displacement sensors, and numerical simulations to analyse draw-in variations during the first forming operation of an automotive front door inner panel. A dedicated optical system was employed to capture sequential images of the blank edge, which were calibrated and processed using computer vision techniques to extract precise draw-in values at predefined locations. The results demonstrate that optical monitoring provides reliable insights related to the sheet metal forming process, highlighting the influence of real-world process disturbances. Furthermore, the study explores the feasibility of integrating measured draw-in data into an adaptive control framework, applying artificial intelligence techniques to refine process stability. By utilizing experimental data alongside numerical predictions, this methodology enhances process understanding and enables data-driven decision-making in industrial sheet metal forming. The findings contribute to the development of intelligent forming control strategies, bridging the gap between modelling and real-world manufacturing conditions to improve product quality and production efficiency.
Keywords
Sheet Metal Forming, Draw-in, Finite Element Analysis, Artificial Neural Network
Reference
Chezan, T., Dhawale, T., Pilthammar, J., Barlo, A., & Aeddula, O. (2025). Integrating Optical Draw-In Measurements with Finite Element Analysis for Enhanced Process Insights in Sheet Metal Forming. In MATEC Web Conferences (Vol. 408, Article 01065). EDP Sciences. Presented at 44th Conference of the International Deep Drawing Research Group (IDDRG 2025), Lisbon, June 1-5, 2025. https://doi.org/10.1051/matecconf/202540801065
