Machining is the most important process to produce finished parts from work piece materials. The challenge of modern machining industries is mainly focused on the achievement of high quality, in terms of work piece dimensional accuracy, high surface finish, high production rate, less wear on the cutting tools, economy of machining in terms of cost saving and enhanced performance of the product with reduced environmental impact. Surface roughness has received serious attention for many years. Surface roughness is the major indication of surface quality. Surface roughness of the material also depends on the tool geometry such as nose radius, rake angles, clearance angles and cutting condition such as cutting speed, feed rate and depth of cut. The parameters such as surface roughness, cutting forces, material removal rate (MRR), power consumption, tool life, tool wear, cutting temperature decide the productivity, product quality and quality of machining. When compared to grinding process there is reduction in machining costs, increase in the flexibility and efficiency, part handling costs and finally decrease in the set-up times. Turning in a lathe is to remove excess material from the work piece to produce a conical or a cylindrical surface. Machining of workpiece having hardness in the range of 45-70 HRc is considered under hard machining. Hard turning is the process machining in which the hardness value of the material is more than the 45HRc in order to obtain finished workpieces directly from hardened parts. Hard turning is best accomplished with cutting inserts made from CBN (Cubic Boron Nitride), cermet or ceramic. Alloy steel, bearing steels, hot and cold work tool steels, high speed steels, die steels, case hardened steels, etc., are the typical materials which are hard turned. Considerable research is being carried out in this area these days. A brief literature review of turning/hard turning is presented below. Varaprasad. Bh et al. [1] studied the effects of cutting speed, feed rate and depth of cut on tool wear in the hard turning experimentally. AISI D3 steel was machined using Al2O3/TiC mixed ceramic tool with corner radius 0.8 mm. They develop a model and predicted tool flank wear of hard turned AISI D3 hardened steel using response surface methodology (RSM). To determine which parameter is more significant to responses and which is not, analysis of variance (ANOVA) was carried out on the experimental data. The combined effects of cutting

2

parameters cutting speed of 165 m/min, feed rate of 0.05 mm/rev and depth of cut of 0.3 mm to achieve a low tool wear of 0.148 mm. The significant parameter for tool flank wear is depth of cut. The feed rate and speed have very little influence on the total variation. The relationship between performance characteristics and cutting parameters is expressed by a multiple regression equation that can be used to estimate the expressed values of the performance level for any parameter levels. Dr. C. J. Rao et al. [2] reported the significance of influence of feed rate, depth of cut and cutting speed on surface roughness and cutting force while working with ceramic tool and work material of AISI 1050 steel. Experiments were conducted on CNC lathe with the help of Taguchi method (L27 design - 3 level and 3 factor). The experiments were conducted as per the design with randomization. ANOVA was carried out to study significant factors. The results indicated that it is the feed rate which has significant influence both on cutting force as well as surface roughness. Depth of cut has significant influence on cutting force, but no significant influence on surface roughness. The interaction of feed and depth of cut was significant on cutting forces whereas no interaction effect was significant for surface roughness. Hamdi Aouici et al. [3] studied the effects of cutting speed, feed rate, work piece hardness and depth of cut on surface roughness and cutting force components in the hard turning experimentally. AISI H11 steel was machined using CBN tool. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs were conducted and statistical ANOVA were performed. Mathematical models for surface roughness and cutting force components were developed using the RSM. Results show that the cutting force components are influenced principally by the depth of cut and work piece hardness; on the other hand, both work piece hardness and feed rate have statistical significance on surface roughness. The cutting force and the feed force both are strongly influenced by the depth of cut, (56.77%) and (31.50%), respectively. On the opposite, the cutting speed has very little influence (0.14%). The best surface roughness was achieved at the lower feed rate and the highest cutting speed of the set experimental parameters. Hardeep Singh et al. [4] investigated the effect of cutting parameters like spindle speed, feed and depth of cut on surface finish and material removal rate on EN-8 using WIDIA Tool Bit CNMG120408. The results were optimized using Taguchi methodology. Results show that the feed rate, depth of cut and spindle speed approximate decreasing trend. The feed has the variable effect on surface roughness. It is interesting to note that spindle speed, feed rate and depth of cut for MRR have increasing trend. In the following section, experimental work is explained in detail.

AISI 4140 is the workpiece material used during experiment. Typical applications of AISI 4140 alloyed steel are shafts, gears, bolts, couplings, spindles, tool holders, sprockets, hydraulic machinery shafts etc. The dimensions of workpiece are 30 mm diameter and 100 mm length. The chemical composition of workpiece, AISI 4140 alloyed steel, is represented in table l.

C Si Ma P S Cr Mo

0.38/ 0.43 0.2/ 0.35 0.75/ 1.0 ≤0.035 ≤0.040 0.8/ 1.1 0.15/ 0.2

• Cut the material into required dimensions (32mm diameter and 100mm length)

• Preheat the material at 450°Cfor 90 minutes

• Material is Heated at 850°C for 90 minute

• Oil quenching is done after heating for 30 minutes

• Tempering is done at 350°C for 2 hours

A pre-cut with a 2 mm depth of cut was performed on each work piece to get 30mm diameter and 100mm length. This was done in order to remove the rust layer or hardened top layer from the outside surface and to minimize any effect of in homogeneity on the experimental results. It is hardened to 55±2HRc.

TNGA 160408 S01020 L1-C TB 310 removable cutting tool insert was used during the experiment.

Experiments are performed at Government College of Engineering, Karad on a SIEMENS 828D controller, 2 axis CNC lathe, MAXTURN PLUS+.

Shyam G. Rathod1*, S. S. Mohite2, M. H. Yadav3 2

Workpiece AISI 4140 alloy steel Size Ф100 mm × 30 mm Cutting condition With coolant Tool holder MTJNR 2020 K16 Surface roughness tester SURFTEST (Mitutoyo Make) Cutting force measuring instrument Kistler dynamometer type 5070 (Swiss make) Three cutting process parameters and three output responses are considered, which are listed in the following table lll.

Cutting speed (m/min) Cutting force (N) Feed rate (mm/rev) Surface roughness (µm) Depth of cut (mm) MRR (cc/min) Following steps are performed to obtain ANOVA for different cutting process parameters- A. DOE for pilot experimentation. B. Deciding the levels for each cutting process parameter from pilot experimentation. C. Design of experiment (Taguchi L9 OA) and perform the L9 OA. out the percentage contribution of each cutting process parameter.

It was decided to conduct the pilot study for hard turning operation. The conventional method of variation of one parameter at a time has been used to study the effect on cutting force, surface roughness and MRR with respect to important cutting process parameters. In first stage of experimentation it is observed that as the cutting speed increases MRR increases, and there is random increase and decrease in the value of surface roughness, hence to obtain lower cutting force, lower surface roughness and higher MRR it is desirable to keep cutting process parameters at suitable level and this is achieved by concept of normalization. Xi* (K) = ……………….(1) Xi* (K) = ……………….(2) Equation (1) is used for lower the better characteristics, i.e., for cutting force and surface roughness and equation (2) is used for higher the better characteristics, i.e., for MRR. Pilot reading for each input cutting process parameters. i) Cutting speed

40 264 0.300 1.2 60 296 0.227 1.8 80 316 0.224 2.4 100 300 0.280 3.0 120 306 0.290 3.6 140 277 0.267 4.2 160 288 0.300 4.8 180 296 0.358 5.4

2

40 1.000 0.433 0.000 60 0.539 0.977 0.125 80 0.246 1.000 0.250 100 0.477 0.582 0.375 120 0.393 0.507 0.500 140 0.809 0.679 0.625 160 0.650 0.432 0.750 180 0.534 0.000 0.875 200 0.000 0.179 1.000

ii) Depth of cut

0.3 300 0.222 3.0 0.5 348 0.229 5.0 0.6 446 0.344 6.0 0.7 481 0.183 7.0

0.3 1.000 0.794 0.000 0.7 0.538 1.000 0.444 0.9 0.340 0.668 0.666

1.2 0.000 0.000 1.000

iii) Feed rate

0.05 199 0.177 1.5 0.08 244 0.225 2.4 0.11 263 0.285 3.3 0.14 288 0.297 4.2 0.17 348 0.406 5.1 0.20 338 0.582 6.0 0.23 337 1.076 6.9 0.26 414 1.214 7.8

0.05 1.000 1.000 0.000 0.08 0.790 0.953 0.142 0.11 0.702 0.895 0.285 0.14 0.584 0.884 0.428 0.17 0.307 0.779 0.571 0.20 0.355 0.609 0.714 0.23 0.357 0.130 0.857 0.26 0.000 0.000 1.000

Shyam G. Rathod1*, S. S. Mohite2, M. H. Yadav3 2

From above fig 2, fig 3 and fig 4, levels for cutting speed, feed rate and depth of cut are selected as shown in table 7. This level gives better results for cutting force, surface roughness and MRR. B. Selection of levels Table X shows that cutting process parameters level selection and their respective values for further experimentation.

Vc m/min 120 140 160 d mm 0.50 0.70 0.90

f mm/rev 0.10 0.15 0.20

Taguchi L-9 OA for 3 levels and three factors was used to perform further experiments.

1 120 0.5 0.10 2 120 0.7 0.15 3 120 0.9 0.20

1 356 06.0 0.353 2 600 12.6 0.680 3 732 21.6 0.955 4 454 10.5 0.661 5 671 19.6 0.931 6 539 12.6 0.377 7 507 16.0 0.896 8 467 11.2 0.350 9 694 21.6 0.533

Source dof Adj SS Adj MS F-Value % cont. Vc 2 0.00890 0.00445 1.96 1.79

D 2 0.00153 0.00077 0.34 0.31

f 2 0.48352 0.24176 106.49 96.99

Experiment was conducted to determine the effect of cutting speed, feed rate and depth of cut on the cutting force, surface roughness and MRR. Table Xl and Xll illustrate the experimental results of cutting force, surface roughness and MRR. A. Analysis of Variance (ANOVA) Analysis of variance (ANOVA) is used to determine the most significant factor affecting the performance measures. Table Xlll, XlV and XV show the results of ANOVA, for cutting force, surface roughness and MRR, respectively. The last columns of tables show the percentage contribution on the total variation indicating the degree of influence on the results.

Vc 2 110 55.1 0.10 0.09 D 2 72075 36037.62.99 57.12

2

f 2 52838 26419.1 46.18 41.87 Error 2 1144 572.1 0.90 Total 8 126168 100.00

In table Xlll, the ANOVA results showed that the depth of cut (the most significant factor) contributed 57.12%, feed rate (the second most significant factor) contributed 41.47%, and cutting speed contributed the least, i.e., 0.09% for cutting force.

In table XlV, ANOVA results show that the contribution for feed rate is 96.99%, whereas cutting speed and depth of cut contributed only 1.79% and 0.31% for surface roughness.

Source dof Adj SS Adj MS F-Value % cont. Vc 2 13.05 6.52 2.11 5.54

D 2 90.61 45.30 14.66 38.51

f 2 125.45 62.72 20.30 53.32 Error 2 6.18 3.09 - 2.63 Total 8 235.28 - - 100.00

In table XV, the ANOVA results showed that the contribution for depth of cut and feed rate was 38.51% and 53.32%, whereas the cutting speed contributed only 5.54% for MRR. B, Main Effect Plot

From above graphs it is seen that the depth of cut strongly changes cutting force. So it can be

Shyam G. Rathod1*, S. S. Mohite2, M. H. Yadav3 2

factor in case of cutting force.

1 562.7 439.0 454.0 2 554.7 579.3 582.7 3 556.0 655.0 636.7

Delta 8.0 216.0 182.7 Rank 3 1 2

From above graph it is clearly observed that the feed rate strongly changes surface roughness. So from above graph it can be concluded that the feed rate is more dominating factor.

From above graph it is seen that the feed rate strongly changes MRR. So from above graph it can be concluded that the feed rate is more dominating factor in case of MRR. Table XVlll Response table for means of cutting force

The objective of this research paper is to find out the most significant cutting process parameter in hard turning for cutting force, surface roughness and MRR. The foremost conclusions which can be drawn are as follows: 1) The cutting force is strongly influenced by the depth of cut and feed rate, 57.12% and 41.47%, respectively. On the contrary, cutting speed has very little influence 0.087%. 2) The surface roughness is strongly influenced by the feed rate (96.99%), whereas cutting speed and depth of cut has insignificant influence, 1.79% and 0.31%, respectively. 3) MRR is strongly influenced by Feed rate and depth of cut, 53.32% and 38.51%,

2

This work was completed in the CIM laboratory at GCE, Karad. We sincerely thank all teaching staff members of Mechanical Engineering Department, for providing the support. We genuinely thank Mr. Chandane, Department of Mechanical Engineering, GCE, Karad, for helping in conducting experiments on CNC lathe. Finally we take this opportunity to thank for financial support R & D scheme under TEQIP-ll.

[1] Varaprasad. Bh, Srinivasa Rao. Ch and P.V. Vinay, “Effect of Machining Parameters on Tool Wear in Hard Turning of AISI D3 Steel” Procedia Engineering , 2014, pp. 338-345. [2] Hardeep Singh, Rajesh Khanna and M. P. Garg, “Effect of Cutting Parameter on MRR and Surface Roughness in Turning EN-8” Current Trends in Engineering Research Vol. 1, No. 1, 2011. [3] Dr. C. J. Rao, Dr. D. Nageswara Rao and P. Srihari, “Influence of cutting parameters on cutting force and surface finish in turning operation” Procedia Engineering, 2013, pp.1405-1415. [4] Hamdi Aouici, Mohamed Athmane Yallese, Kamel Chaoui, Tarek Mabrouki and Jean-Francois Rigal, “Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization” Measurement, 344-353, 2012. [5] E. D. Derakhshan and A. A. Akbari, “Experimental investigation on the effect of work piece hardness and cutting speed on surface roughness in hard turning with CBN tools” Proceeding of the world congress on engineering, Vol II, 2009. [6] Wuyi Chen, “Cutting forces and surface finish when machining medium hardness steel using CBN tools” International Journal of Machine Tools & Manufacture 40, 2000, pp. 455–466. [7] Meenu Sahu and Komesh Sahu, “Optimization of Cutting Parameters on Tool Wear, Work piece Surface Temperature and Material Removal Rate in Turning of AISI D2 Steel” International journal of Advanced Mechanical Engineering, Volume 4, issue 3, 2014, pp. 291-298. roughness during finish hard turning AISI52100 grade steel, Procedia CIRP 1, 2012, pp.651-656.

P.G. Scholar (Mechanical Engineering) GCE, Karad