Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process

Derouiche, Khouloud; Garois, Sevan; Champaney, Victor; Daoud, Monzer; Traidi, Khalil; Chinesta, Francisco

doi:10.3390/met11050738

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…There is an exponential increase in the number of publications reporting that modeling route, in many technology domains: machining and drilling [17,25,68], additive manufacturing [143], reactive extrusion [16,63], induction hardening [28], chemical reactions [136], among many others.…”

Section: Data-driven Processesmentioning

confidence: 99%

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review

Chinesta

2022

Adv. Model. and Simul. in Eng. Sci.

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Simulation-based engineering has been a major protagonist of the technology of the last century. However, models based on well established physics fail sometimes to describe the observed reality. They often exhibit noticeable differences between physics-based model predictions and measurements. This difference is due to several reasons: practical (uncertainty and variability of the parameters involved in the models) and epistemic (the models themselves are in many cases a crude approximation of a rich reality). On the other side, approaching the reality from experimental data represents a valuable approach because of its generality. However, this approach embraces many difficulties: model and experimental variability; the need of a large number of measurements to accurately represent rich solutions (extremely nonlinear or fluctuating), the associate cost and technical difficulties to perform them; and finally, the difficulty to explain and certify, both constituting key aspects in most engineering applications. This work overviews some of the most remarkable progress in the field in recent years.

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Section: Data-driven Processesmentioning

confidence: 99%

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review

Chinesta

2022

Adv. Model. and Simul. in Eng. Sci.

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“…Then, an interpolation technique is applied to the set of POD modal coefficients { ki } N i=1 and for each k, a surrogate model is constructed using a regression method such as sparse proper generalized decomposition (sPGD) [31][32][33], support vector regressions (SVRs) [34], gaussian processes (GPs) [35], etc.…”

Section: Pod With Interpolation (Podi)mentioning

confidence: 99%

Real-time prediction by data-driven models applied to induction heating process

et al. 2022

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Using the approximated function, it is possible to evaluate any parameter value in the training intervals with a reasonable computational time. This method is already used for different applications, see [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites

Jacot

Champaney

Chinesta

et al. 2023

Sensors

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This paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a dataset of possible damage localization, fiber and matrix failure ratios is generated through high-fidelity simulations (ABAQUS software). Then, a reduced model in a lower-dimensional approximation subspace based on the so-called sparse proper generalized decomposition (sPGD) is constructed. The parametrized approach of the sPGD method reduces the computational burden associated with a high-fidelity solver and allows a faster evaluation of all possible failure configurations. However, during the testing phase, it turns out that classical sPGD fails to capture the influence of the damage localization on the solution. To alleviate the just-referred difficulties, the present work proposes an adaptive sPGD. First, a change of variable is carried out to place all the damage areas on the same reference region, where an adapted interpolation can be done. During the online use, an optimization algorithm is employed with numerical experiments to evaluate the damage localization and damage ratio which allow us to define the health state of the structure.

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Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process

Cited by 12 publications

References 28 publications

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review

Real-time prediction by data-driven models applied to induction heating process

Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites

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