Big and Smart Experimental Databases for Mechanical
il y a 3 semaines
**Big and smart experimental databases for mechanical model calibration: development of an automatized solution**:
- Réf **ABG-124453**
- Sujet de Thèse- 06/06/2024- Contrat doctoral- Institut de Recherche Dupuy de Lôme (IRDL), UMR CNRS 6027- Lieu de travail- Lorient (56100) - Bretagne - France- Intitulé du sujet- Big and smart experimental databases for mechanical model calibration: development of an automatized solution- Champs scientifiques- Sciences de l’ingénieur
- Robotique
- Mots clés- Metallic materials, Mechanical behavior, Automatisation, Tensile and simple shear tests, Plasticity model calibration**Description du sujet**:
**Aims of the PhD**. The development of the automated mechanical testing itself, for a uniaxial tensile loading and quasi-homogeneous tests, and the robustness of the automated model calibration is the research hypothesis of the PhD. It is therefore related to mechanical engineering and design, material mechanical behavior, mechanical modelling, inverse methods for material parameters identification and robotization. Two main steps are planned: the first one is related to the automation of the test itself and the second one to the calibration step.**Methodology.** Performing a mechanical test corresponds to several tasks, which control, and accuracy are decisive in the repeatability and the quality of the output data. Replacing a human operator by an automated one requires several mechanical adaptations of the grips, especially to take the sample, settle it in the machine and remove it. The feasibility a IRDL has already been established, using a cobot (of brand Universal Robot) and a machine designed by the French company MatAndSim which software is developed within Linux environment. The MatchID solution for DIC has been chosen, as it proves to be open to line commands, and it brings tools to quantify the quality of the measure as well as filters to make a reliable comparison with finite element calculations. The cobot is programmed to control the machine and a digital camera and acts as a master. The first goal of the PhD is to develop a robust robotized mechanical testing unit. It should be emphasized that, performing a mechanical test today, in the field of plasticity at large strains, with DIC measurement, is a qualified task that requests a dedicated training and a clear and accurate procedure. Indeed, the quality of a test is still highly dependent on the operator. The quality of the test is validated by plotting the evolution of some raw data. A skilled operator can obtain rapidly repeatable and reliable results and the technical issue is to transfer this knowledge into an automatic procedure, based on an artificial intelligence (AI) tool for test validation, with some basics elements to check, as the quantified gap between load-strain data for tests performed under the same conditions, the initial slope deviation from a known Young’s modulus,This tool, as well as the fully automated calibration process, represent the scientific issues to be solved. Repeatability of the mechanical behavior should also be considered, it is usually investigated by repeating the same test under the same condition at least 3, and sometimes up to 5, times. Only the sample is changed from one test to the other. Then, an automatic selection of a unique representative test, to add to the database, is performed. This selection will be based on basics criteria like the closeness of the output signals of the representative test to all the others. This validation corresponds to the implementation of the skills and knowledge of a human operator into an automated process.The second step is dedicated to model calibration based on inverse methodology. The validation of the calibration process is a key point. Indeed, today, the human operator validates the optimized parameter based mainly on visual inspection (data plotting) and by checking local mínima of the cost function of the optimisation process. A tool based on AI will be developed, built from the know-how of the team. This tool will use indicators from the optimisation process (cost function values, rate of convergence, closeness of the material parameters to the constraints,) to validate the calibration. Finally, results obtained from a known material, already tested and for which a mechanical model is already calibrated, will be compared with results obtained in a fully automated way, starting from the blank sheet, and ending up with the parameter set.
**Prise de fonction**:
- 02/09/2024**Nature du financement**:
- Contrat doctoral**Précisions sur le financement**:
**Présentation établissement et labo d'accueil**:
- Institut de Recherche Dupuy de Lôme (IRDL), UMR CNRS 6027La conception de matériaux nouveaux, les progrès dans la maîtrise du comportement des matériaux lors de leur élaboration, en condition d’usage ou en situation exceptionnelle nécessitent la compréhension de phénomènes couplés et leur modélisati
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