Towards an accurate estimation of heat flux distribution in metal cutting by machine learning
Paper in proceeding, 2023

This study presents a machine learning-based approach for inverse identification of heat flux distribution on the rake face of the cutting tools in machining. This approach includes temperature measurements from thermocouples embedded in the tool and heat transfer finite element (FE) simulations to create the data required to train the ML model. The identified heat flux distribution is compared with the distribution from FE machining simulations for validation. The results show a clear potential to estimate the heat flux distribution in machining more efficiently by using an ML-based inverse approach.

Metal cutting

Heat transfer simulation

Heat flux

Temperature

Machining

Machine Learning

Inverse identification

Author

Ahmet Semih Ertürk

Chalmers, Industrial and Materials Science, Material and Computational Mechanics

Amir Malakizadi

Chalmers, Industrial and Materials Science, Materials and manufacture

Ragnar Larsson

Chalmers, Industrial and Materials Science, Material and Computational Mechanics

Procedia CIRP

22128271 (eISSN)

Vol. 117 359-364

19th CIRP Conference on Modeling of Machining Operations, CMMO 2023
Karlsruhe, Germany,

A simulation based guide to machinability assessment

VINNOVA (2016-05397), 2017-09-01 -- 2020-11-27.

Subject Categories

Mechanical Engineering

DOI

10.1016/j.procir.2023.03.061

More information

Latest update

7/26/2023