Coupling machine learning and synthetic image DIC-based techniques for the calibration of elastoplastic constitutive models
PRATES Pedro A., HENRIQUES Joan D. F., PINTO Jose, BASTOS Nelson, ANDRADE-CAMPOS Antonio
download PDFAbstract. Today, most design tasks are based on simulation tools. However, the success of the simulation depends on the accurate calibration of constitutive models. Inverse-based calibration methods, such as the Finite Element Model Updating and the Virtual Fields Method, have been developed for identifying constitutive parameters. These methods are based on mechanical tests that allow heterogeneous strain fields under the “Material Testing 2.0” paradigm in which digital image correlation plays a vital role. Although these methods have been proven effective, constitutive model calibration is still a complex task. A machine learning approach is developed and implemented to calibrate elastoplastic constitutive models for metal sheets, using datasets populated with finite element simulation results of strain field data from mechanical tests. Feature importance analysis is conducted to understand the importance of the different input features and to reduce the computational cost related with model training. Synthetic image DIC-based techniques were coupled with the numerically generated database, enabling the construction of a virtual experiments database that accounts for sources of uncertainty that can influence experimental DIC measurements. A robustness analysis of the methodology is performed for the boundary conditions of the test.
Keywords
Constitutive Model Calibration, Elastoplasticity, Machine Learning, DIC
Published online 4/19/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: PRATES Pedro A., HENRIQUES Joan D. F., PINTO Jose, BASTOS Nelson, ANDRADE-CAMPOS Antonio, Coupling machine learning and synthetic image DIC-based techniques for the calibration of elastoplastic constitutive models, Materials Research Proceedings, Vol. 28, pp 1193-1202, 2023
DOI: https://doi.org/10.21741/9781644902479-130
The article was published as article 130 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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