The statistical mathematical models are developed to investigate the influence of cutting parameters on surface roughness, tool wear, cutting force, tangential force and the work piece vibration in boring of AISI 4340 steels. A full factorial design of experiments is used to conduct 27 experiments on AISI 4340 as the work piece material and TiCN–Al₂O₃–TiN multi-layered coated carbide inserts. Online data acquisition of cutting forces on the cutting tool and the work piece vibrations are measured by using piezo-electric dynamometer and laser Doppler vibrometer, respectively. This paper proposes optimization method using grey relational analysis (GRA) and predictive models like support vector machine and response surface method are used to predict the GRG values and optimize the machining parameters. The GRA is used for converting multi response optimization problem into optimization of single objective of grey relational grade (GRG). Finally confirmation test was performed and also optimized the machining parameters to minimize the surface roughness (Ra), tool wear (VB), cutting force (Fx), tangential force (Fz) and work piece vibration (VA).

Powder metallurgy (P/M) electrodes used in electric discharge machining (EDM) often exhibit higher surface roughness (SR) than their solid, ground counterparts, particularly when fabricated from micron-scale powders. This adversely affects friction, wear, and corrosion resistance. These challenges can be addressed by carefully selecting suitable electrode materials. In EDM, key electrode attributes such as material type, composition, and particle size (PS) are crucial in enhancing process performance. In this study, tungsten carbide (WC)/ cobalt (Co) P/M electrodes were developed with PSs varied from the micron to the nano-scale to investigate their effect on EDM performance. The findings indicated that wear and corrosion behaviour were strongly influenced by the PS used to fabricate the tool electrode. Due to their low thermal conductivity and high surface reactivity, nanoparticle-based electrodes promoted greater surface alloying, reduced arcing, and enhanced process stability. The machine tool parameters also altered the size of the surface features such as cavities, globules, and micro-cracks. Consequently, wear and corrosion resistance improved by 77.92% and 84.27%, respectively, due to the formation of carbides and oxides, and microstructural modifications on the machined surface. The results are correlated with the coefficient of friction, SR, hardness, white layer thickness, and microstructural changes, elucidating the mechanisms behind the improved surface characteristics.

In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO₄, and CaF₂ to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L₈ mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO₄ and Mo at 10Wt% and CaF₂ at 5Wt% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.