Turning is the primary process in most of the production activities in the industry. In turning process, a single point cutting tool moves along the axis of a rotating work piece. Turning is used to reduce the diameter of the work piece, usually to a specified dimension, and to produce a smooth finish on the metal. Turning produces rotational, typically axis symmetric parts that have many features, such as holes, grooves, threads, tapers, various diameter steps, and even contoured surfaces. 1
Fig 1 Basic Turning Operation 7
1.1.1 Principle of Turning
Turning is a machining operation for generating external surfaces of revolution are generated. The work piece tool is generated the work piece or tool is rotated about its axis and the cutting tool or work piece is also given feed motion in a direction normal to cutting operation. 2
1.1.2 Basic Operations Carried On Lathe
The important timing operations carried out on a lathe are –
1. Cylindrical Turning
4. External Threading
The work piece is rotated about its own axis by the lathe and the motion is called the primary motion. The cutting tool is set to the desired depth to cut and moves forward at a uniform rate causing a continuous removal of chip. This motion is called feed motion. 2
1.2 VARIOUS CUTTING PARAMETERS
1. Cutting Speed
3. Depth of cut
4. Metal Removal Rate
5. Machining time
1.3 PROCESS OPTIMISATION
Process optimization is the discipline of adjusting a process to optimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost, maximizing efficiency. This is one of the major quantitative tools in industrial decision making. Optimization of machining parameters not only increases the utility for machining economics, but also the product quality to a great extent The surface roughness greatly varies with the change of cutting process parameters. 1
Fig-2 Possible Optimization Goals 1
Turning is a very important machining process in which a single point cutting tool removes unwanted material from the surface of a rotating cylindrical work piece. The cutting tool is fed linearly in a direction parallel to the axis of rotation. Turning produces three cutting force components,(the main cutting force) i.e. thrust force, (FZ), which produces in the cutting speed direction, feed force, (FX), which produces in the feed rate direction and the radial force, (FY), which produces in radial direction and which is normal to the cutting speed). Out of three force components the cutting force (main force) constitutes about 70% to 80% of the total force ‘F’ and is used to calculate the power ‘P’ required to perform the machining operation. Power is the product of main cutting force and the cutting velocity and is a better criterion for design and selection of any machine tools. Power consumption may be used for monitoring the tool conditions. 3
Machining involves the shaping of a part through removal of material. A tool, constructed of a material harder than the part being formed, is forced against the part, causing material to be cut from it. Machining, also referred to as cutting, metal cutting, or material removal, is the dominant manufacturing shaping process. It is both a primary as well as a secondary shaping process. The device that does the cutting or material removal is known as the machine tool. 4
Turning operation using a single point cutting tool has been one of the oldest and popular methods of metal cutting. It has even replaced grinding in several applications with reduced lead time without affecting the surface quality. Surface roughness is also a vital measure as it may influence frictional resistance, fatigue strength or creep life of machined components. As far as turned components are concerned, better surface finish (low surface roughness) is important as it can reduce or even completely eliminate the need of further machining. 5
The cutting tool is fed linearly in a direction parallel to the axis of rotation. Turning is carried on lathe that provides the power to turn the work piece at a given rotational speed and feed to the cutting tool at specified rate and depth of cut. Therefore three cutting parameters namely cutting speed, feed rate and depth of cut need to be optimized in a turning operation. Turning operation is one of the most important operations used for machine elements construction in manufacturing industries i.e. aerospace, automotive and shipping. 6
Govindan P and Vipindas M P 1 used the Taguchi approach and examined that this approach has a potential for savings in experimental time and cost on product or process, development and quality improvement as it requires minimum number of experiments. Taguchi Method uses the idea of fundamental functionality, which will facilitate people to identify the common goal because it will not change from case to case and can provide a robust standard for widely and frequently changing situations.
Harsh Y Valera, Sanket N Bhavsar 3 presented an experimental study of power consumption and roughness characteristics of surface generated in turning operation of EN-31 alloy steel with TiN+Al2O3+TiCN coated tungsten carbide tool under different cutting parameters. This study defined the influences of three cutting parameters like spindle speed, depth of cut and feed rate affecting surface roughness as well as power consumption while turning operation of EN-31 alloy steel. It can be concluded that spindle speed, feed and depth of cut significantly affect the surface roughness and power consumption while tuning EN-31 alloy steel work material using coated carbide cutting tool. To optimize the cutting parameters for achieving better surface finish with reduced power consumption detailed design of experimentation is needed for the work piece material under investigation.
G Harinath Gowd et al. 4 used geared lathe for doing experiments on EN31 steel. Depth of cut, cutting speed and feed were taken as process parameters and the output responses were force and temperature. This study optimizes the force and temperature which are the outputs in the turning process of lathe by adjusting the speed, feed, and depth of cut which are the influencing parameters in the turning process by applying ANN methodology for the EN-31. It has been found that the speed and the depth of cut have great significance on the force and temperature, whereas the feed has less significance on both the outputs.
Dr. C. J. Rao et al. 5 examined the influence of speed, feed and depth of cut on cutting forces and surface roughness while working with tool made of ceramic with an Al2O3+TiC matrix and the work material of AISI1050 steel. Experiments were conducted using Industrial type of CNC lathe. The Taguchi method was used for the experiments. Analysis of variance with adjusted approach has been adopted. The results have indicated that it is feed rate which has significant influence both on cutting force as well as surface roughness. Depth of cut has a significant influence on cutting force, but has an insignificant influence on surface roughness. The interaction of feed and depth of cut and the interaction of all the three cutting parameters have significant influence on cutting force, whereas, none of the interaction effects have a significant influence on the surface roughness produced.
L. B. Abhang and M. Hameedullah 6 investigated the power consumption in turning EN-31 steel with tungsten carbide tool under different cutting conditions. The experimental runs were planned according to 24+8 added center point factorial design of experiments, replicated thrice. The data collected was statistically analyzed using Analysis of Variance techniques, first order and second order power consumption prediction models were developed by using response surface methodology (RSM). It was concluded that second-order model is more accurate than the first-order model and fit well with the experimental data. The model can be used in the automotive industries for deciding the cutting parameters for minimum power consumption and hence maximum productivity. The smallest the values of the cutting speed, feed rate, depth of cut and tool nose radius, the lowest is the metal cutting power consumption.
W.H. Yang and Y.S. Tarng 7 worked on Taguchi method to find the optimal cutting parameters for turning operations. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) are employed to investigate the cutting characteristics of S45C steel bars using tungsten carbide cutting tools. Through this study, not only can the optimal cutting parameters for turning operations be obtained, but also the main cutting parameters that affect the cutting performance in turning operations can be found.
H. K. Dave et al. 8 investigated the machining characteristics of different grades of EN materials in CNC turning process using TiN coated cutting tools. This research has been focused on the analysis of optimum cutting conditions to get the lowest surface roughness and maximum material removal rate in CNC turning by Taguchi method. Optimal cutting parameters for each performance measure were obtained employing Taguchi techniques. The orthogonal array, signal to noise ratio and analysis of variance were employed to study the performance characteristics in dry turning operation. ANOVA has shown that the depth of cut has significant role for generation of higher MRR and insert has significant role for generation of lower surface roughness. Thus, it is possible to increase machine utilization and decrease production cost in an automated manufacturing environment.
Harish Kumar et al. 9 have done experimental work for the optimization of input parameters to improve of quality of the product of turning operation on CNC machine. Feed Rate, spindle speed & depth of cut are taken as the input parameters and the dimensional tolerances as output parameter. The experimental work defined that spindle speed is the key factor for minimizing the dimensional variation for minimizing the surface roughness. The experiment verified that the Taguchi approach gives us the optimal parameters in the CNC turning process by using HSS cutting tools, the optimum set of speed, feed rate and depth of cut are the most affecting parameters having the impact of 59.9% is speed.
D. Philip Selvaraj and P. Chandramohan 10 carried out the dry turning of AISI 304 Austenitic Stainless Steel (ASS). The influence of cutting parameters like cutting speed, feed rate and depth of cut on the surface roughness of austenitic stainless steel during dry turning were presented in his work. Taguchi optimization method was applied to find the optimal process parameters, which minimized the surface roughness during the dry turning of AISI 304 Austenitic Stainless Steel bars using TiC and TiCN coated tungsten carbide cutting tool. Taguchi orthogonal array, the (S/N) ratio and (ANOVA) were used for the optimization of cutting parameters. ANOVA results shows that feed rate, cutting speed and depth of cut affects the surface roughness by 51.84%, 41.99% and 1.66% respectively.
Ashvin J. Makadia and J.I. Nanavati 11 studied the application of RSM on the AISI 410 steel is carried out for turning operation. Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius.
Praveen Kumar et al. 12 investigated the effect of process parameters during the machining of EN-31steel using Taguchi method. In this Speed, feed and doc are considered as the process parameters. Here Taguchi L27 orthogonal array is selected as for machining EN-31 and tungsten carbide inserts are used as the tool. Signal to noise ratio and ANOVA are used to identify the optimum parameter combination for getting better surface finish and MRR. The conclusion drawn from this study is that the optimum condition for getting better surface finish is the spindle speed with level 3(2250 rpm), feed rate with level 1 (0.15 m/min) and depth of cut with level 2 (0.6 mm) and for MRR, the optimum condition for getting larger MRR is spindle speed with level 3 (2250 rpm), feed rate with level 3 (0.25 m/min) and depth of cut with level 3 (0.8mm). From ANOVA it is concluded that the feed rate is the most significant factor affected on surface roughness and spindle speed is the most significant parameter affected on MRR.
Chandan Kumar et al. 13 carried an experimental study of turning on Austenitic Stainless steel of grade AISI 202 by a TiAlN coated carbide insert tool. The objective was to find the optimum machining parameters so as to minimize the surface roughness for the selected tool and work materials in the chosen domain of the experiment. The experiment was conducted in an experiment matrix of 20 runs designed using a full-factorial Central Composite Design (CCD). Surface Roughness was measured using a Talysurf. ANOVA analysis was carried out and it is observed that feed is the most significant factor affecting the surface roughness, closely followed by cutting speed and depth of cut, while the only significant factor affecting the tool wear was found to be the depth of cut.
D. Rajasekhar Reddy and AV Hari Babu 14 examined the multi-response optimization of turning technique in machining of EN-31 tool steel for an best parametric combination to render the lowest surface roughness (Ra) and cutting force with the maximal material-removal rate (MRR) using a Grey–Based Taguchi method. Turning parameters studied were cutting speed, feed rate and depth of cut on EN-31 steel with chemical vapor deposition (CVD) and physical vapor deposition (PVD) coated carbide tools. It was seen from the results that cutting speed is the most significant parameter for surface roughness followed by feed, whereas the depth of cut is found to be insignificant by the ANOVA analysis.