Machinability analysis of Inconel 718 through parametric optimization using novel laser hybrid micro milling technique

Miniaturization is reshaping the landscape of advanced manufacturing to fulfil the demands of micro components with exceptional precision and functionality in sectors such as aerospace, biomedical and microelectronics. Hybrid techniques syndicate distinct machining processes to leverage their unique benefits while minimizing inherent limitations. This experimental study proposes a novel laser hybrid micro milling technique intended to synergize laser assistance with mechanical micro milling. To explore the machinability of Inconel 718, experiments were designed at feed rates below, at and above the cutting-edge radius with uncoated and three different coated tools using Taguchi L-16 orthogonal array. Cutting speed, feed rate, depth of cut and tool type, each evaluated across four discrete levels to analyze their impact on critical responses: surface roughness, tool wear and burr formation. Analysis of Variance (ANOVA) and Grey Relational Analysis (GRA) were employed to evaluate the significance of each parameter and to determine the optimal combination of variables. Uncoated tools yielded the lowest roughness and tool wear, TiAlN coatings minimized burrs. ANOVA of the regression model revealed the tool type as the most influencing factor with 73.68% contribution. GRA identified 9 m/min speed, 2.5 µm/tooth feed and 60 µm cutting depth with TiAlN coated tool as optimal run. Optimization using Response Surface Methodology (RSM) led to 23.08%, 24.04% and 23.64% average reduction in roughness, tool wear and burr formation, respectively, demonstrating the efficacy of the optimized process settings. The investigation significantly contributes by revealing parameter interactions and their impacts on burr mitigation, tool longevity, and surface quality.