Work

Data-Driven Inverse Problem for Optimizing the Induction Hardening Process of C45 Spur-Gear

Year: 2023

Type: article

Abstract: Inverse problems can be challenging and interesting to study in the context of metallurgical processes. This work aims to carry out a method for inverse modeling for simultaneous double-frequency induction hardening process. In this investigation, the experimental measured hardness profiles were considered as input data, while the output data were the process parameters. For this purpose experiments were carried out on C45 steel spur-gear. The method is based on machine learning algorithms and data treatment for dealing with inverse approach issues. In addition to the inverse modeling, a forward problem-based verification completes the study. It was found that according to promising results that this method is suitable and applicable for inverse problem of hardness modeling.

Source: Metals

DOI: 10.3390/met13050997

Authors: Sevan Garois, Monzer Daoud, Francisco Chinesta

Institutions: Arts et Métiers, IRT M2P

Language: English

Topic: Microstructure and Mechanical Properties of Steels

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,224

Funders: Agence Nationale de la Recherche, CHIST-ERA