Abstract

In this study, the milling performance of Inconel 718 alloys produced by forging (WP1), Inconel 718 produced by Selective Laser Melting (SLM) (WP2), and Inconel 718 (WP3) subjected to heat treatment after SLM, under different cooling/lubrication conditions, was evaluated using experimental and artificial intelligence-based approaches. Microstructural analysis showed a homogeneous fine-grained structure in WP1, while WP2 exhibited dendritic features and porosity. Heat treatment improved the microstructural homogeneity of WP3. The hardness values of WP1, WP2, and WP3 were 457 Hv, 303.33 Hv, and 391 Hv, respectively. Milling experiments yielded cutting forces of 336.5–1185.9 N, surface roughness values of 0.22–1.39 µm, and cutting temperatures of 168–658 °C. Compared with dry machining, MQL reduced average cutting force and cutting temperature by 15.5% and 18.65%, respectively, while improving tool wear and surface integrity. Machine learning models including LR, DTR, SVR, and GPR were developed to predict machining responses. GPR provided the highest prediction accuracy, achieving 98.72% for cutting force and 98.99% for cutting temperature. The results demonstrate that manufacturing route and cooling strategy significantly affect the machinability of Inconel 718 and that machine learning techniques can effectively support machining process optimization.

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