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Nevin Manimala Statistics

Performance evaluation in micro-milling of Inconel 718 with coated tools through an integrated optimization framework

Sci Rep. 2026 Jul 16. doi: 10.1038/s41598-026-60496-9. Online ahead of print.

ABSTRACT

Miniaturized systems demand micro-components exhibiting high dimensional fidelity, superior surface quality, and complex geometries for aerospace, biomedical, and microelectronic applications. Micro-milling offers flexibility and precision, but consistent performance remains challenging by rapid tool degradation, burr generation, and size-effect-dominated cutting mechanism, particularly in high strength alloys. This study investigates machinability of Inconel 718 in low-speed micro-milling using uncoated and three coated micro-end mills, with feed rates defined relative to cutting-edge radius. Taguchi L16 orthogonal array methodically assesses the impact of cutting speed, feed rate, depth of cut, and tool coating across four distinct levels each, on surface roughness, tool wear, and burr formation. The experimental outcomes were evaluated statistically using Analysis of Variance (ANOVA), while Grey Relational Analysis (GRA) was applied to identify optimal machining conditions. Results reveal that TiAlN coatings improve surface finish, nACo suppresses burr formation, and uncoated tools enhance wear resistance. The optimal condition from GRA is 10.5 m/min cutting speed, 1.5 μm/tooth feed, and 120 μm depth of cut with uncoated tool. Response Surface Methodology (RSM) yielded average reductions of 23.81%, 11.49%, and 18.11% in surface roughness, tool wear, and burr formation, respectively. This integrated Taguchi-ANOVA-GRA-RSM Optimization (TANGRO) framework provides robust insights for optimal machining performance.

PMID:42463766 | DOI:10.1038/s41598-026-60496-9

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