Integrating Optical Draw-In Measurements with Finite Element Analysis for Enhanced Process Insights in Sheet Metal Forming
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2025/02/matecconf_iddrg2025_01065.pdf |
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| Summary: | 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. |
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| ISSN: | 2261-236X |