The present study reports on the application of accelerated cooling environment (ACE) in hard turning of AISI D2 steel (55 ± 1HRC) using mixed ceramic insert (Al2O3 + TiCN) which is rarely being investigated and to address the major problems of brittle fracture of tool tip that arises through cutting forces and friction at tool-work and chip-tool interface. In spraying process, some portion of spraying coolant vaporize due to heat when it reaches to cutting zone where as remaining portion of coolant easily penetrate in cutting zone through capillary action and reduces friction as well as heat in cutting zone. Abrasion and chipping are noticed to be dominant wear mechanism. Cutting speed and depth of cut are significant for flank wear as well as cutting temperature whereas feed is significant for average surface roughness. Serrated chips have been identified at higher cutting speed and higher feeds. Optimal parametric combination is found to be d1-f1-v2 (0.1mm-0.04 m/min-108 m/min) and tool life and machining cost per part are 70 minutes and Rs 76.76 respectively. Investigation shows the worthy of application of ACE in hard turning in industrial sectors ecologically and economically. Empirical models reveal statistically significance due to higher coefficient of correlations.

Silicon carbide (SiC) particulate impregnated Al 7075 matrix composite was fabricated by stir casting method and then heat treated to T6 condition. It was then machined with multiple layer of TiN coated tungsten carbide (WC) inserts in dry environment and pollution free Spray Impingement Cooling (SIC) environment to compare the machining performance. SIC environment presented better machining performance with respect to cutting tool temperature (T), average roughness of the machined surface (Ra) and tool flank wear (VBc). Quadratic response surface models were developed by computing the experimental data. Weighted Principal Component Analysis (WPCA) based Taguchi technique was adopted to optimize the multiple responses simultaneously, which resulted 40 m/min of cutting speed (V), 0.05 mm/rev of feed (f) and 0.2 mm of cutting depth (d) was the optimal combination of process parameters.

Turning experiments were conducted on a novel aluminum alloy (LM6)/fly ash composite based on the response surface and face centered central composite design methodology. The effects of cutting parameters on surface roughness and tool wear were investigated. Multiple regression models were developed for the responses and the adequacies of the developed models were tested at 95% confidence interval using the analysis of variance (ANOVA) technique. Carbide inserts (Model: CNMG 120408-M5) were used for turning the specimens in a CNC turning machine (model: LT-16). The test for significance of the regression models, the test for significance on individual model coefficients and the lack-of-fit tests were performed using the statistical Design-Expert7.0v software environments. R2 indicated the model significance and the value was more than 97%, revealed that the relation between cutting responses and input parameters held good for more than 97% and the model was adequate.

Two sets of nine different silicon carbide particulate (SiCp) reinforced Al 7075 Metal Matrix Composites (MMCs) were fabricated using liquid metallurgy stir casting process. Mean particle size and weight percentage of the reinforcement were varied according to Taguchi L9 Design of Experiments (DOE). One set of the cast composites were then heat treated to T6 condition. Optical micrographs of the MMCs reveal consistent dispersion of reinforcements in the matrix phase. Mechanical properties were determined for both as-cast and heat treated MMCs for comparison of the experimental results. Linear regression models were developed for mechanical properties of the heat treated MMCs using list square method of regression analysis. The fabrication process parameters were then optimized using Taguchi based grey relational analysis for the multiple mechanical properties of the heat treated MMCs. The largest value of mean grey relational grade was obtained for the composite with mean particle size 6.18 µm and 25 weight % of reinforcement. The optimal combination of process parameters were then verified through confirmation experiments, which resulted 42% of improvement in the grey relational grade. Finally, the percentage of contribution of each process parameter on the multiple performance characteristics was calculated through Analysis of Variance (ANOVA).

The current research copes with the optimization of the surface heat transfer coefficients of a square mild steel test specimen by spray impingement cooling. A laboratory scale experimental setup was developed at School of Mechanical Engineering KIIT University, Odisha, India to investigate the role of various process parameters to enhance the heat transfer from the surface of the heated steal specimen. The mild steel plates of dimension 120 mm × 120 mm, and different thicknesses of 4 mm, 6 mm and 8 mm were used in the experiment. The effect of the process parameters such as thickness of the tested plate, nozzle to plate distance, air and water pressure upon the surface heat transfer coefficient (HTC) was optimized. The optimization of the controlling parameters was carried out by using the desirability functions. The Design Expert 8 software was used to analyze the experimental results. A new correlation was developed for optimization of the surface heat transfer coefficient.

This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.950C whose desirability is one.