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.

The present study investigates performance and feasibility of application of low cost cemented carbide insert in dry machining of AISI 52100 steel hardened to (55 ± 1 HRC) which is rarely researched as far as machining of bearing steel is concerned. Machinability studies i.e. flank wear, surface roughness and morphology analysis of chip has been investigated and statistical regression modeling has been developed. The test has been conducted based on Taguchi L16 OA taking machining parameters like cutting speed, feed and depth of cut. It is observed that uncoated cemented carbide insert performs well at some selected runs (Run 1, 5 and 9) which show its feasibility for hard turning applications. The developed serrated saw tooth chip of burnt blue colour adversely affects the surface quality. Adequacy of the developed statistical regression model has been checked using ANOVA analysis (depending on F value, P value and R2 value) and normal probability plot at 95% confidence level. The results of optimal parametric combinations may be adopted while turning hardened AISI 52100 steel under dry environment with uncoated cemented carbide insert.

The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

Machining of hardened work materials with appropriate levels of process parameters is still a burning issue in manufacturing sectors and challenging. It is because of pressing demand of surface quality which adversely affected by evolution of tool wear. Therefore the present investigation is undertaken to make a decision on parametric optimization of multi-responses such as flank wear and surface roughness during machining hardened AISI 52100 steel (55±1) steel using mixed ceramic insert under dry environment through grey relational analysis combined with Taguchi approach. Also predicted mathematical models of 1st and 2nd order have been developed for responses and checked for its accuracy. Second order mathematical model presented higher R2 value and represents best fit of the model and adequate compared to first order model. Model indicates good correlations between the experimental and predicted results. The proposed grey-based Taguchi methodology has been proved to be efficient for solving multi-attribute decision making problem as a case study in hard machining environment.

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.

The objective of the study is to assess the performance of multilayer coated carbide insert in the machining of hardened AISI D2 steel (53 HRC) using Taguchi design of experiment. The experiment was designed based on Taguchi L27 orthogonal array to predict surface roughness. The S/N ratio and optimum parametric condition are analysed. The analysis of variance has also been carried out to predict the significant factors affecting surface roughness. Based on Taguchi S/N ratio and ANOVA, feed is the most influencing parameter for surface roughness followed by cutting speed whereas depth of cut has least significant from the experiments. In regression model, the value of R2 being 0.98 indicates that 98 % of the total variations are explained by the model. It indicates that the developed model can be effectively used to predict the surface roughness on the machining of D2 steel with 95% confidence intervals.

The paper presents the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment. The paper also investigates the effect of process parameter on flank wear (VBc). The experiments have been conducted using three level full factorial design techniques. The machinability model has been developed in terms of cutting speed (v), feed (f) and machining time (t) as input variable using response surface methodology. The adequacy of model has been checked using correlation coefficients. As the determination coefficient, R2 (98%) is higher for the model developed; the better is the response model fits the actual data. In addition, residuals of the normal probability plot lie reasonably close to a straight line showing that the terms mentioned in the model are statistically significant. The predicted flank wear has been found to lie close to the experimental value. This indicates that the developed model can be effectively used to predict the flank wear in the hard turning. Abrasion and diffusion has been found to be the dominant wear mechanism in machining hardened steel from SEM micrographs at highest parametric range. Machining time has been found to be the most significant parameter on flank wear followed by cutting speed and feed as observed from main effect plot and ANOVA study.

Turning hardened component with PCBN and ceramic inserts have been extensively used recently and replaces traditional grinding operation. The use of inexpensive multilayer coated carbide insert in hard turning is lacking and hence there is a need to investigate the potential and applicability of such tools in turning hardened steels. An attempt has been made in this paper to have a study on turning hardened AISI 4340 steel (47 ± 1 HRC) using coated carbide inserts (TiN/TiCN/Al2O3/ZrCN) under dry environment. The aim is to assess the tool life of inserts and evolution of flank wear with successive machining time. From experimental investigations, the gradual growth of flank wear for multilayer coated insert indicates steady machining without any premature tool failure by chipping or fracturing. Abrasion is found to be the dominant wear mechanisms in hard turning. Tool life of multilayer coated carbide inserts has been found to be 31 minute and machining cost per part is Rs.3.64 only under parametric conditions chosen i.e. v = 90 m/min, f = 0.05 mm/rev and d = 0.5 mm. The mathematical model shows high determination coefficient, R2 (99%) and fits the actual data well. The predicted flank wear has been found to lie very close to the experimental value at 95% confidence level. This shows the potential and effectiveness of multilayer coated carbide insert used in hard turning applications.

Cutting force and chip reduction coefficient is the important index of machinability as it determines the power consumption and amount of energy invested in machining actions. It is primarily influenced by process parameters like cutting speed, feed and depth of cut. This paper presents the application of Taguchi’s parameter design to optimize the parameters for individual responses. For multi-response optimization, Taguchi’s quality loss function approach is proposed. In the present investigation, optimal values of cutting speed, feed and depth of cut are determined to minimize cutting force and chip reduction coefficient during orthogonal turning. The effectiveness of the proposed methodology is illustrated through an experimental investigation in turning mild steel workpiece using high speed steel tool.

The present work deals with the cutting forces and cutting temperature produced during turning of titanium alloy Ti-6Al-4V with PVD TiN coated tungsten carbide inserts under dry environment. The 1st order mathematical models are developed using multiple regression analysis and optimized the process parameters using contour plots. The model presented high determination coefficient (R2 = 0.964 and 0.989 explaining 96.4 % and 98.9 % of the variability in the cutting force and cutting temperature, which indicates the goodness of fit for the model and high significance of the model. The developed mathematical model correlates the relationship of the cutting force and temperature with the process parameters with good degree of approximation. From the contour plots, the optimal parametric combination for lowest cutting force is v 3 (75 m/min) – f 1 (0.25 mm/rev). Similarly, the optimal parametric combination for minimum temperature is v 1 (45 m/min) – f 1 (0.25 mm/rev). Cutting speed is found to be the most significance parameter on cutting forces followed by feed. Similarly, for cutting temperature, feed is found to be the most influencing parameter followed by cutting speed.