Simulation of laser-assisted machining of the GH2135 alloy using molecular dynamics: effects of machining parameters on cutting performance

Abstract

The process of laser-assisted machining (LAM) of the GH2135 iron–nickel-based high-temperature alloy is simulated through molecular dynamics in this study. A comprehensive study is conducted on machining force, temperature distribution, strain/stress, and surface morphology to reveal the influence and mechanism of machining parameters on the cutting performance of the GH2135 alloy. The results demonstrate that both the machining force and surface damage are significantly reduced in the LAM process compared to conventional cutting without laser assistance. As the laser power increases, the amorphous structure generated by cutting also increases, which in turn improves the material cutting efficiency. The machining force decreases, while the stress and temperature exhibit an inverse relationship. As the cutting speed increases, the machining force, stress, and temperature continue to increase. It indicates an increase in material removal efficiency during high-speed cutting. However, high temperature and stress could also contribute to a reduction in the tool life. The surface morphology and damage analyses indicate that increasing either the laser power or cutting speed is advantageous for promoting non-destructive cutting. Additionally, the competition between thermal stress and mechanical stress is observed during the LAM process. Therefore, it is crucial to select appropriate processing parameters to optimize the LAM process.

Graphical abstract: Simulation of laser-assisted machining of the GH2135 alloy using molecular dynamics: effects of machining parameters on cutting performance

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Article information

Article type
Paper
Submitted
13 Mar 2025
Accepted
09 Aug 2025
First published
21 Aug 2025

Nanoscale, 2025, Advance Article

Simulation of laser-assisted machining of the GH2135 alloy using molecular dynamics: effects of machining parameters on cutting performance

B. Liu, Y. Wang, Y. Bai, Z. Kuai and X. Jing, Nanoscale, 2025, Advance Article , DOI: 10.1039/D5NR01064K

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