Paper Titles
Preface
Advanced Control of Stretch-Reducing Mills Using Artificial Intelligence
p.1
Proxy-Physics-Informed and Transfer Learning Networks for Radial-Axial Ring Rolling (RARR) under Varying Data Availability
p.13
Material Parameter Identification Using Bayesian Data Assimilation and Biaxial Tensile Test
p.29
Innovation of the “Cut-Clamp-Play” Concept for Robust and Efficient Sheet Metal Material Characterization
p.37
Method of an Automated Tool Design of Punch-Bending Processes Using the Asset Administration Shell
p.43
Uncertainty Detection in Sheet Metal Bending Processes with Machine Learning
p.55
AI-Driven Design and Optimization of Bending Processes
p.65
Hybrid Numerical and Data-Driven Modelling for Defect Prediction in Screw Press Hot Bulk Forging of the En AW-6060 Part
p.75

Innovation of the “Cut-Clamp-Play” Concept for Robust and Efficient Sheet Metal Material Characterization

Article Preview
View Pdf By email

Abstract:

Material Testing 2.0 (MT2.0) couples full‑field deformation measurements (Digital Image Correlation, DIC) with inverse identification methods (Virtual Fields Method, VFM) to extract constitutive parameters from a small number of heterogeneous experiments. This paper presents the Cut‑Clamp‑Play concept: an integrated industrial MT2.0 solution that unifies specimen design, automated testing hardware, and a computationally efficient VFM identification chain to deliver fast, user‑friendly sheet‑metal characterization. A perforated cruciform specimen is optimized for parameter identifiability of the Yld2000‑2d anisotropic yield function and used in a single biaxial test. A working prototype has been built at KU Leuven and used to collect representative DIC data; the measured displacement/strain response is double‑symmetric, confirming correct mechanical operation. Projected and early prototype results indicate that the Cut‑Clamp‑Play approach can reduce operator actions by roughly 70% and produce identification results within one hour for typical sheet‑metal cases, while further work is required to make the fully automated “Play” stage robust for industrial deployment.

You have full access to the following eBook
Lionel Fourment on: Optimization, AI and Inverse Analysis in Forming Read eBook

Info:

Periodical:

Key Engineering Materials (Volume 1049)

Pages:

37-42

DOI:

https://doi.org/10.4028/p-TF8dpD

Citation:

Cite this paper

Online since:

April 2026

Authors:

Andraž Maček, Bojan Starman, Miroslav Halilovič, Fabrice Pierron, Pascal Lava, Sam Coppieters*

Keywords:

DIC, Material Characterization, MT2.0, Plastic Anisotropy, Virtual Fields Method

Funder:

The publication of this article was funded by the KU Leuven 10.13039/501100004040

Export:

RIS, BibTeX

Permissions:

Creative Commons CC BY 4.0

Share:

Citation:

* - Corresponding Author

References

[1] Zhang Y., Yamanaka A., Cooreman S., Kuwabara T., Coppieters S., Inverse identification of plastic anisotropy through multiple non-conventional mechanical experiments. Int J Solids Struct 2023;285:112534.

DOI: 10.1016/j.ijsolstr.2023.112534

Google Scholar

[2] Pierron F., Grédiac M., Towards Material Testing 2.0. A review of test design for identification of constitutive parameters from full-field measurements. Strain 57, 2021

DOI: 10.1111/str.12370

Google Scholar

[3] Barlat F. et al., Plane stress yield function for aluminum alloy sheets—part 1: theory. International Journal of Plasticity 19, 1297–1319 (2003).

DOI: 10.1016/s0749-6419(02)00019-0

Google Scholar

[4] Marek A., Davis F.M., Pierron F., Sensitivity-based virtual fields for the non-linear virtual fields method. Comput Mech 60, 409–431 (2017)

DOI: 10.1007/s00466-017-1411-6

Google Scholar

[5] Halilovič, M., Starman, B., Coppieters, S., 2024. Computationally efficient stress reconstruction from full-field strain measurements. Comput. Mech., 74(4).

DOI: 10.1007/s00466-024-02458-4

Google Scholar