Enhancing machining performance of AISI 52100 steel through surface-engineered CVD-wc inserts: Role of Laser surface texturing and nano-GNP based MQL

This study explores the combined influence of graphene nanoplatelet (GNP)-enhanced minimum quantity lubrication (MQL) and laser surface texturing (LST) on chemical vapor deposition (CVD)-coated tungsten carbide (WC) cutting tools, aiming to enhance the machinability of AISI 52100 steel during turning operations. Experiments were structured using Taguchi's L16 orthogonal array, considering three primary factors: lubrication method (dry, standard MQL, MQL with 0.5 wt.% GNP, and MQL with 1 wt.% GNP), tool surface texture (textured vs. untextured), and cutting speed (119 m/min and 184 m/min). Tool wear was assessed through flank wear measurements, while surface quality was evaluated via surface roughness analysis. Statistical techniques, including Taguchi's signal-to-noise (S/N) ratio and analysis of variance (ANOVA), were employed to interpret the data. Results revealed that the optimal performance in terms of minimal flank wear was achieved using laser-textured tools under MQL with 1 wt.% GNP at 119 m/min. This condition outperformed all others, followed by MQL with 0.5 wt.% GNP, standard MQL, and dry cutting. Similarly, the optimum surface finish corresponded to the 1 wt.% GNP-assisted MQL condition, with decreasing performance observed for 0.5 wt.% GNP, plain MQL, and dry cutting, respectively. Across all trials, laser-textured inserts consistently delivered better surface quality than untextured ones. Furthermore, Energy Dispersive X-ray Spectroscopy (EDS) analysis was conducted to confirm the elemental integrity of the CVD-coated inserts under optimized conditions. The EDS results validated the presence and stability of the TiN/Al₂O₃/TiCN multilayer coating after machining, indicating robust coating adhesion and thermal durability under nano-GNP-assisted lubrication.