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Projeto de investigação
Digital twin development in metal forming: calibration aided by digital image correlation and artificial intelligence
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Identification of Orthotropic Elastic Properties of Wood by a Synthetic Image Approach Based on Digital Image Correlation
Publication . Henriques, João; Xavier, José; Andrade-Campos, António; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DEMI - Departamento de Engenharia Mecânica e Industrial; Molecular Diversity Preservation International (MDPI)
This work aims to determine the orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood from a single uniaxial compressive experimental test, under quasi-static loading conditions, based on two different specimen configurations: (a) on-axis rectangular specimens oriented on the radial-tangential plane, (b) off-axis specimens with a grain angle of about 60◦ (radial-tangential plane). Using digital image correlation (DIC), full-field displacement and strain maps are obtained and used to identify the four orthotropic elastic parameters using the finite element model updating (FEMU) technique. Based on the FE data, a synthetic image reconstruction approach is proposed by coupling the inverse identification method with synthetically deformed images, which are then processed by DIC and compared with the experimental results. The proposed methodology is first validated by employing a DIC-levelled FEA reference in the identification procedure. The impact of the DIC setting parameters on the identification results is systematically investigated. This influence appears to be stronger when the parameter is less sensitive to the experimental setup used. When using on-axis specimen configuration, three orthotropic parameters of Pinus pinaster (ER, ET and νRT ) are correctly identified, while the shear modulus (GRT ) is robustly identified when using off-axis specimen configuration.
Identification of Swift Law Parameters Using FEMU by a Synthetic Image DIC-Based Approach
Publication . Henriques, João; Conde, Mariana; Andrade-Campos, António; Xavier, José; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DEMI - Departamento de Engenharia Mecânica e Industrial
Computer-aided engineering systems rely on constitutive models and their parameters to describe the material behaviour. The calibration of more elaborated material models with a larger number of parameters becomes very time and cost consuming. The development of image-based technology has enhanced the interest in inverse identification methods, which, when coupled with full-field measurements, have the potential to reduce the number of experimental tests required to accurately identify material properties. This work aims to identify the Swift hardening law parameters of a dual-phase steel using a tensile test on a heterogeneous dogbone specimen under uniaxial and quasi-static loading conditions using the finite element model updating (FEMU) technique. The numerical results were used to generate synthetic images, which were then processed by digital image correlation (DIC) and used as the reference in the identification procedure. Two different approaches were tested: (i) directly comparing the numerical results to the reference; (ii) using DIC-levelled numerical data by iteratively generating synthetic images and using the DIC filter with the same settings as were used on the reference (virtual experiment). The identification results obtained from both approaches are compared and discussed.
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Fundação para a Ciência e a Tecnologia
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2021.05692.BD