https://popups.uliege.be/esaform21/index.php?id=4388 Index terms fr 0 https://popups.uliege.be/esaform21/index.php?id=2622 Advanced inverse material identification procedures rely on the richness of strain fields generated in a complex specimen. Currently, the design of a complex specimen is mainly based on engineering judgement and experience that are often user-specific. This intuitive approach forms the crux of the problem, addressed in the current research. To this end, the paper embarks on devising a generic and automated method to design mechanical heterogeneous experiments. A notched tensile specimen is optimized to maximize a previously proposed heterogeneity indicator-IT. The effectiveness of this procedure for identifying the anisotropic parameters of the Hill48 yield criterion is validated using two independent methodologies, namely the identifiability method and the Finite Element Model Updating (FEMU) approach to assess the parameter identification quality. The latter approach is based on carefully generated synthetic experiments including the metrological aspects of Digital Image Correlation (DIC) while having access to the ground truth material behavior. For the plane stress Hill48 anisotropic yield criterion, it is shown that the IT-based design procedure correlates with both the identifiability method and the identification accuracy obtained through FEMU. Wed, 24 Mar 2021 18:33:07 +0100 Sat, 10 Apr 2021 13:21:42 +0200 https://popups.uliege.be/esaform21/index.php?id=2622 https://popups.uliege.be/esaform21/index.php?id=1486 The development of full-field measurement techniques paved the way for the design of new mechanical tests. However, because these mechanical tests provide heterogeneous strain fields, no closed-form solution exists between the measured deformation fields and the constitutive parameters. Therefore, inverse identification techniques should be used to calibrate constitutive models, such as the widely known finite element model updating (FEMU) and the virtual fields method (VFM). Although these inverse identification techniques follow distinct approaches to explore full-field measurements, they all require using an optimisation technique to find the optimum set of material parameters. Nonetheless, the choice of a suitable optimisation technique lacks attention and proper research. Most studies tend to use a least-squares gradient-based optimisation technique, such as the Levenberg-Marquardt algorithm. This work analyses optimisation algorithms, gradient-based and -free algorithms, for the inverse identification of constitutive model parameters. To avoid needless implementation and take advantage of highly developed programming languages, the optimisation algorithms available in optimisation libraries are used. A FEMU based approach is considered in the calibration of a thermoelastoviscoplastic model. The material parameters governing strain hardening, temperature and strain rate are identified. Results are discussed in terms of efficiency and the robustness of the optimisation processes. Mon, 22 Mar 2021 20:01:49 +0100 Mon, 05 Apr 2021 18:00:57 +0200 https://popups.uliege.be/esaform21/index.php?id=1486