Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process - Archive ouverte HAL Access content directly
Journal Articles Metals Year : 2021

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

Abstract

Data-driven modeling provides an efficient approach to compute approximate solutions for complex multiphysics parametrized problems such as induction hardening (IH) process. Basically, some physical quantities of interest (QoI) related to the IH process will be evaluated under real-time constraint, without any explicit knowledge of the physical behavior of the system. Hence, computationally expensive finite element models will be replaced by a parametric solution, called metamodel. Two data-driven models for temporal evolution of temperature and austenite phase transformation, during induction heating, were first developed by using the proper orthogonal decomposition based reduced-order model followed by a nonlinear regression method for temperature field and a classification combined with regression for austenite evolution. Then, data-driven and hybrid models were created to predict hardness, after quenching. It is shown that the results of artificial intelligence models are promising and provide good approximations in the low-data limit case.
Fichier principal
Vignette du fichier
PIMM_M_2021_DEROUICHE.pdf (2.46 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03288060 , version 1 (16-07-2021)

Identifiers

Cite

Khouloud Derouiche, Sevan Garois, Victor Champaney, Monzer Daoud, Khalil Traidi, et al.. Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process. Metals, 2021, 11 (5), pp.738. ⟨10.3390/met11050738⟩. ⟨hal-03288060⟩
44 View
74 Download

Altmetric

Share

Gmail Facebook X LinkedIn More