Title: Optimisation of process parameters for laser-assisted micro-milling of Inconel718

Authors: Chen Cong; Jiachen Hao; Xiaohong Lu; Zhe Liu; Steven Y. Liang

Addresses: State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' The George W. Wood Ruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA

Abstract: Inconel718 has high corrosion resistance and strength, making it widely used in aerospace and other demanding fields. Laser-assisted micro-milling (LAMM) offers a potential method for high-quality, efficient machining of Inconel718 micro-components. However, achieving both high surface quality and efficiency is challenging due to the complex, multi-physics nature of the process. This study investigates the effects of spindle speed, feed per tooth, axial cutting depth, and laser power on surface roughness. Surface roughness and material removal rate (MRR) models are established, with the average and maximum relative error of 10.22% and 11.13%. Interaction effects significantly influence surface roughness, particularly among spindle speed/laser power and feed per tooth/axial cutting depth. Targeting low surface roughness and high MRR, a genetic algorithm was employed to optimise parameters, yielding a Pareto optimal solution set. This research provides guidance for process parameter selection in LAMM Inconel718. [Submitted 12 September 2024; Accepted 26 June 2025]

Keywords: laser-assisted micro-milling; LAMM; Inconel718; parameters optimisation; surface roughness prediction; genetic algorithm; GA.

DOI: 10.1504/IJMR.2024.149093

International Journal of Manufacturing Research, 2024 Vol.19 No.4, pp.481 - 500

Received: 12 Sep 2024
Accepted: 26 Jun 2025
Published online: 13 Oct 2025 *

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