Experimental Investigation and Analysis of Cutting Parameters in CNC Turning on Aluminium

Experimental Investigation and Analysis of Cutting Parameters in CNC Turning on Aluminium Ch.Siva Ramakrishna Department of Mechanical Engineering, Vignan s Institute of Information Technology Visakhapatnam, India. Karanam Krishna Department of Mechanical Engineering, Vignan s Institute of Information Technology Visakhapatnam, India ABSTRACT Machining is the important manufacturing process, which involves the process of removing material from a work piece in the form of chips. Machining is necessary where tight tolerances on dimensions and finishes are required. Being such an important process in manufacturing industry, a machining process is considered for investigation in the present work. This paper presents the experimental investigations on the effects of cutting variables Like Spindle, feed rate and depth of cut on the rate. The experiments were conducted on Aluminum work piece on a CNC turning machine using carbide insert. The experiments were conducted as per the design of experiments (DOE). Initial trial experiments were conducted to fix the ranges for the control parameters. After conducting the experiments the MRR is measured and recorded. The effects were studied after plotting the graphs between the Input process parameters versus the output response. The results obtained in this study can by further used for optimizing the process parameters there by the optimized results help the operator to enhance the quality as well as machining rate. 6 Keywords: Spindle, of Cut, rate, rate, Experimental, CNC Lathe 1. INTRODUCTION In a present competitive environment every organization are trying to control the cutting cost and improve the quality of machined components. So, it is necessary to identify the process parameters on material rate of a component. The machining time reduces causes to reduce overall costs which depend on amount of material to be removed and machining parameters like spindle, feed rate and depth of cut. Increasing the productivity and the quality of the machined parts are the main challenges of the industry which involves the making of a part through machining of materials. A carbide tool, constructed of a material harder than the part (Aluminum) being formed, is forced against the part, causing material to be cut from it. Machining, also referred to as cutting, metal cutting, or material, is the dominant manufacturing shaping process. Machining is the term generally used, rather than material removed or cutting. Nearly all castings and products formed by deformation processing [bulk or sheet metal] require some machining to obtain the desired final shape or surface characteristics. Aluminum is frequently used to produce the automobile components by turning process. It is highly efficient that products with good surface quality are manufactured in less time. Surface roughness and material rate is generally dependent on the cutting parameters such as spindle, feed rate and depth of cut. Better selections of these cutting parameters are essential in order to produce components with good quality and high tolerance in least time. High material rate is required in order to decrease the machining time. A lot of work has been carried out to improve the product quality and efficiency in machining in the last few decades, turning operation is the basic machining operation on lathe machine. With the advent of CNC technology, the machining processes are automated through which high quality of the machined components; high material rates can be achieved. There are many factors which affect material rate (MRR),

i.e. tool material, nose radius, geometry, tool vibration, work piece properties cutting parameters i.e. cutting, cutting feed, depth of cut, machining time, spindle forces etc. 1.1 New Technology in Manufacturing Industry These days computers are widely used in Manufacturing Industries. They are applied to all fields of an industry e.g. Technical, Financial and Organizational fields. The Engineering applications of computers fall mainly in to following areas.(i)computer Numeric Control (CNC) (ii)computer Aided Drafting and design (CAD) (iii) Computerized s Handling (Robotics) (iv)computer Aided Quality assurance (CAQA) (v)computer Aided Production control and Management (CAPM) (vi)computer Integrated Manufacturing (CIM) In this module only CNC machines are being dealt with.cnc involves the use of computers for the precise control of the physical movement of the machine tools by letters, numbers and symbols. A CNC Machine runs on a program fed to it. The program consists of precise instructions about the Methodology of Manufacture as well as the movements 1.2 objective of the project The main objective of the project is the CNC Turning process is applied to the aluminum work piece and the optimum cutting parameters(spindle, feed rate, depth of cut) is applied and investigate the output responses like MRR ( rate),spindle load produced during machining is observed. The major consideration for selecting aluminum material because of lot of advantages like better mechanical properties as well as light weight, good appearance suitable for all automobile components and aero plane applications. Aluminum is having good conductivity as well as better corrosion resist properties is suitable for all future design components. 1.3 Cutting parameters In turning process, the cutting and cutting tool motion is specified through different process parameters. Out of these parameters mainly consider the main parameters like spindle, feed and depth of cut is taken as important parameters. Spindle (N-rpm): It is the spindle rotational and tool in revolutions per minute (rpm). The cutting divided by the circumference of the tool is equal to the spindle. of cut (mm): of cut is also main influence parameter during CNC machining, and feed come together with DOC (depth of cut) to find out the MRR (material rate), which is the volume of work-piece material that can be removed per unit time. rate (mm/rev): The cutting tool movement relative to the work-piece as the tool makes a cut. The feed rate is measured in mm per revolution. 1.4 Carbide Insert Properties These are stable and medium expensive. These are available in different "grades" containing different percentages of Tungsten Carbide and binder (usually Cobalt). These materials are resistance to abrasion. High solubility in iron requires the additions of Tantalum Carbide and Niobium Carbide for Steel usage. Its main use is in turning tool bits although it is very common in milling cutters and saw blades. The tool has hardness up to HRC 9. Sharp edges generally not recommended. 2. LITRATURE REVIEW The performance of hard turning is measured in terms of material rate, surface finish, cutting forces, power consumed and tool wear. rate influences functional properties of machined components. rate in hard turning has been found to be influenced by a number of factors such as spindle, feed rate, spindle, work material 61

62 characteristics, work hardness, cutting time, tool nose radius and tool geometry, stability of the machine tool and the work piece set-up, the use of cutting fluids, etc. [1].Hassan, K. et al. (212) [1] has done the experimental investigation of material rate (MRR) in CNC turning of C34 using Taguchi method using L 27 array. When the MRR is optimized alone the MRR comes out to be 8.91. The optimum levels of process parameters for simultaneous optimization of MRR have been identified. Optimal results were verified through confirmation experiments. It was concluded that MRR is mainly affected by cutting and feed rate. [2]. Rodrigues L.L.R [3] has done a significant research over Effect of Cutting Parameters on Surface Roughness and Cutting Force in Turning of Mild Steel.[3].Jaharah, A.G. et al (29) [5] has studied the effect of uncoated carbide tool geometries in turning AISI 145. This paper presents the application of Finite element method (FEM) in simulating the effect of cutting tool geometries on the effective stress and temperature increased in turning. The tool geometries studied were various rake (α) and clearance (β) in the different ranges. The minimum effective stress of 17MPa is achieved using rake and clearance angles of 5 and 5 respectively with cutting of 3mm/min, and feed rate of.25mm/rev.[4].n.h.rafai, This paper presents experimental and analytical results of a preliminary investigation into dimensional accuracy and surface finish achievable in dry turning. The Taguchi method and Pareto ANOVA analysis is used to determine the effects of the three major controllable machining parameters, viz. cutting, feed rate and depth of cut on dimensional error, surface roughness and circularity, and subsequently to find their optimum combination. The results indicate that while the cutting parameters have varying influence on the quality characteristics at different levels, the utilization of low feed rate can optimize the dimensional error, surface roughness and circularity of cylindrical component parts concurrently[5].gopalswamy et al (29) used Taguchi method in determining the optimal process parameters in hard machining of hardened steel. They observed that the Cutting is the most influencing parameter on tool life and surface roughness.[6]diwakar Reddy et al. (211) has conducted an experimental investigation on turning of medium carbon steel using uncoated carbide tool. This work dealt with cutting parameters such as, feed and depth of cut and the response as surface roughness. ANN modeling is applied to find optimal cutting parameters. It is concluded that the model has been proved to be successful in terms of agreement with experimental results.[7].sharma et al.(212) has conducted an experimental study on Hard turning of EN8 steel using High steel tool. This work deals with prediction of tool wear with application of Image processing with considering are cutting, feed rate and depth of cut as cutting parameters. It was concluded with comparison of deviation of results for tool wear between conventional method and image processing.[8]. Ghani, M.U. et al. (27) has presented results of an investigation into the tool life and the tool wear behavior of low content CBN cutting tools used in hard turning of hardened H13 tool steel using finite element thermal modeling. It involved measuring the cutting forces, cutting temperatures, tool wear and the contact area.[9]. Richard Dewes et al (23) carried out the study on rapid machining of hardened AISI H13 and D2 moulds, dies and press tools. The primary objective was to assess the drilling and tapping of AISI D2 and H13 with carbide cutting tools, in terms of tool life, work piece quality, productivity and costs. The secondary aim was to assess the performance of a number of water-based dielectric fluids, intended primarily for EDM operations, against a standard soluble oil cutting fluid, in order to assess the feasibility of a duplex machining arrangement involving HSM and EDM on one machine tool.[1].durai et al (212) studied the cutting parameters that ensure less power consumption in high tare CNC machines. The data acquisition system was used to measure the output characteristics. From the results, it was concluded that the feed rate and the depth of cut significantly influence the energy consumption.[11].vishnu et al (213) determined the optimum cutting parameters to achieve

lower surface roughness and higher material rate using Taguchi methodology and Response Surface Methodology. The experimental results show that cutting and depth of cut are significant variables to the surface roughness of mild steel. Surface roughness gets decreased with increase in spindle and increased with increase in depth of cut. With the increase in feed rate the surface roughness gets increased and when the cutting is decreased the surface roughness becomes increased. The MRR gets increased with increase in cutting and when the feed rate is increased the MRR gets increased.[12]. Vikas et al (213) optimized and evaluated the machining parameters on EN8 steel. The optimum parameters were found based on Taguchi and ANOVA. They concluded that minimum trust force on normal tool shape, moderate cutting, and lesser depth of cut and lowest feed are the optimum parameters.[13].lalwani and Mehta [12] used response surface to investigate the influence of cutting parameters on surface roughness in finish hard turning of MDN25 steel.by reviewing above literature, it is identified that the prediction of rate has great importance and it is analysed by conducting number of experiments on Aluminum specimens. 3.METHODOLOGY To investigate the process parameters for MRR on aluminum the following experimental procedure is carried out: STEP 1: The raw material (metal rods) is fed into the CNC Turning lathe Machine. STEP 2: The Metal rods are clamped in the machine. STEP 3: The program is written in the computer console according to the required cutting parameters i.e. spindle Speed, of Cut and Rate.STEP 4: The process of turning has been done in the following three cases (i)varying while keeping the of Cut and Rate constant(ii) Varying Rate and keeping the Spindle Speed and of Cut constant and (iii) Varying of Cut while keeping the Spindle Speed and Rate constant. Fig 3.1 CNC Machine Fig. 3.2 Principle of machining The machining of a work piece by a CNC program requires axis and a coordinate system to be applied to the machine tool. 3.1 Axes of CNC Turning Machine 63

Fig 3.3 Co-ordinate system of CNC Lathe Fig 3.4 cutting tool inserts The axis along machine spindle axis of turning machine is Z axis and along the cross traverse is X-axis. There is no traverse along the Y axis derived from the right hand coordinate system. The direction of Z axis away from the machine headstock is positive and towards the lathe headstock is negative. The direction of X axis from traverse (X axis) towards the center axis is negative and away from the center axis is positive. 3.2 programming of CNC Machine Tools CNC programming refers to the methods for generating the instructions that drive the CNC machine tool. In a CNC program, the machining steps (operations) for producing a part on the machine tool are laid down in a form that the control system can understand. For two-dimensional components with little geometric complexity 2 axis programming is used. Three distinct techniques are adopted for creating CNC Programs: Manual CNC Programming, Computer assisted part programming, Programming using CAD/CAM software. The following should be taken into account when writing a part program: The machine features and capacity, The size of the component, The material to be machined, Component datum s,machine coordinates, The sequence of operations, The tooling to be used, Component holding and location In-process inspection requirements 3.3 Removal Rate: The material rate has been calculated from the difference of weight of work piece before and after machining by using following formula. MRR = W i -W f /ρ a t mm 3/ /sec, Where, W i = Initial weight of work piece in gm, W f = Final weight of work piece in gm, t = Machining time in seconds, ρ s = Density of Aluminum (2.7 g/mm 3 ) 3.4 Parameters and levels for selection 1. rate (.8,.1, and.12 mm/rev): It is known from the fundamentals of metal cutting that feed rate influences the material rate. Various researchers have observed the effect of feed rate on the MRR, spindle force during machining of aluminum. Thus, these feed rates are chosen based on the design of experiments at +1 and -1 levels 3. Spindle (15, 16, 17 rpm): Previous studies have indicated that the MRR is influenced by the spindle. Therefore, to 64

study the effect of spindle in detail, these values of spindle has been considered based on levels. 4. of cut (.6,.8, 1 mm): The depth of cut also influences the MRR, which in turn influences the tolerance and fit of the components. 3.5 Design of Experiments (DOE) The experiments were conducted on a high precision CNC lathe machine of FANUC Series Oi Mate TC in machining centre. Aluminum is taken as the work piece material for investigation. The specimen is prepared with the dimensions of 71mm length and 32mm diameter for turning and carbide insert is used for experimentation. The control factors considered for experiments are spindle, feed and depth of cut while, Metal rate, spindle force considered as the output responses. The ranges of the process control variables are given in table 1. The experiments are conducted based on L9 orthogonal array as shown in Table 2.After conducting the experiments, the output responses were measured and recorded. MRR is calculated as the ratio of volume of material removed from work piece to the machining time. The spindle force is also recorded directly from the machine. In order to determine the volume of material removed after machining, the weights of work piece before machining and after machining are measured. Machining time taken for each cut is automatically displayed by the machine. Table 1. Control factors and their levels Table 4.1 Levels of Control Factors Table 4.2 Design of Experiments S.No Control Factor Symbol -1 Level Level +1 Level 1 N 15 16 17 rpm Units 2 F.8.1.12 mm/min 3 of cut DOC.6.8 1 mm w/s No. Speed (rpm) (mm/rev) of Cut (mm) 1 15.8.6 2 15.1.8 3 15.12 1 4 16.8.8 5 16.1 1 6 16.12.6 7 17.8 1 8 17.1.6 9 17.12.8 4. RESULTS AND GRAPHS 4.1. Direct effect of process parameters on output response-mrr The main effects of the process variables on MRR are studied after plotting the graphs by using Design of experiments. The cutting variables spindle ; feedrate and depth of cut have a major effect upon the material rate, which has a major role in determining the power requirements. (i) Effect of spindle on material rate 65

MRR MRR International Journal of Engineering Technology, Management and Applied Sciences The experiments were conducted by feed rate as well as depth of cut constant and the spindle is varying at three level as shown in Table:4.1.Fig. 4.1 shows the direct effect of spindle on material rate. As spindle I increases the material rate increases. Table 4.1 vs MRR Spindle ( rpm ) ( mm/rev ) of cut ( mm ) rate (mm 3 /sec) 5 vs MRR 15.1.8.213 16.1.8.284 17.1.8.499 (ii)effect of on material rate 14 15 16 17 18 Spindle Speed Fig 4.1 Effect of spindle on MRR The experiments were conducted by spindle as well as depth of cut constant and the feed is varying at three level as shown in Table:4.2. As the feed rate is increased, the material per unit time also becomes more as shown in Figure 4.2 As the tool movement per unit time increases; the greater amount of material is removed. Table 4.2 vs MRR Cutting ( rpm ) ( mm/rev ) of cut ( mm ) rate(mm 3 /sec) 16.8.8.132 16.1.8.284 16.12.8.499 (iii) Effect of of Cut on material rate 6 4 2 vs MRR.6.8.1.12.14 Fig 4.2 Effect of feed rate on MRR The experiments were conducted by spindle as well as feed constant and the depth of cut is varying at three level as shown in Table: 4.3. The more the depth of cut, the more the material rate as shown in Figure 4.3. The chips removed per unit time will be more and thereby quantity of material removed is also high. As the depth of cut increases, the cutting force increases Thereby increase in of material. 66

MRR MRR International Journal of Engineering Technology, Management and Applied Sciences Spindle ( rpm ) Table 4.3 DOC vs MRR of (mm/rev) cut (mm) rate (mm 3 /Sec) 16.1.6.176 16.1.8.36 16.1 1.586 of Cut vs MRR 1 5.4.6.8 1 1.2 of Cut Fig 4.3 Effect of depth of cut on MRR 4.2. Interactive Effects of Machining Parameters on Removal Rate The interactive effects of the process variables on MRR were studied after plotting the graphs by using Design of Experiments. The cutting variables Spindle, feed and depth of cut have a major effect upon the material rate, which has a major role in determining the power requirements. (i) Effect of spindle and depth of cut on material rate Nine experiments were conducted by feed is kept constant and the spindle as well as depth of cut is varying at three level as shown in Table:4.4. As the feed rate is increases, the material per unit time also becomes more as shown in Figure 4.4 As the tool movement per unit time increases; the greater amount of material is removed. spindle ( rpm ) Table4.4Effect of Speed & DOC vs MRR ( mm/rev ) of cut ( mm ) rate(mm 3 /sec) Speed & of cut VS MRR 15.1.6.132 16.1.8.284 17.1 1.36 1.5 1.5 15 16 17 MRR of cut Fig. 4.4 Effect of Spindle & DOC on MRR (ii) Effect of spindle and feed on material rate The experiments were conducted by keeping depth of cut is constant and the spindle as well as feed is varying at three level as shown in Table: 4.5. As the feed rate is increases, the material per unit time also becomes more as shown in Figure 4.5 As the tool movement per unit time increases; the greater amount of material is removed. 67

MRR MRR International Journal of Engineering Technology, Management and Applied Sciences Table 4.5 Speed & vs MRR Spindle (rpm) (mm/rev) of cut (mm) rate (mm 3 /sec) 15.8.8.132 16.1.8.284 17.12.8.499 Speed, VS MRR.1.5 15 16 17 Fig 4.5 Effect of Speed, on MRR (ii) Effect of depth of cut and feed on material rate The experiments were conducted by is kept constant and the depth of cut as well as feed is varying at three levels as shown in Table: 4.6. As the feed rate and depth of cut is increased, the material per unit time also becomes more as shown in Figure 4.6 As the tool movement per unit time increases; the greater amount of material is removed. Table 4.6 DOC and vs MRR spindle (rpm) (mm/rev) of cut (mm) rate(mm 3 /sec) of Cut, VS MRR 16.8.8.284 16.1 1.586 1.5 1.5 16.12.6.284 16 16 16 Fig 4.6 Effect of of Cut, on MRR 5. CONCLUSIONS & FUTURE SCOPE OF WORK The present study was carried out to the effect of input parameters on the material rate. The following conclusions have been drawn from the study: 1. The parameters considered in the experiments are optimized to attain maximum material rate. The best setting of input process parameters for maximum material rate is developed. 2. As the spindle, feed rate and of cut increases, the of material per unit time also increases. The chips removed per unit time will be more and thereby quantity of material removed is also high. As the depth of cut increases, the cutting force increases thereby increase in of material. In interactive effect the best condition for maximum material rate is obtained by keeping feed rate constant and depth of cut increases.. 5.1Future Scope of Work 68

There is scope for work extending the study with various work materials like brass, magnesium, nickel, steel, thermo set plastic, titanium and zinc. The material of cutting tool used in the present project was carbide. The experiment can be performed with different cutting tools including Tungsten carbide electrode to assess the machining performance of CNC machine REFERENCES [1] Hassan, K. et al. (212) Experimental investigation of rate in CNC turning using Taguchi method International Journal of Engineering Research and application, vol. 2, no. 2, pp. 1581-159. [2] Rodrigues L.L.R., Kantharaj A.N., Kantharaj B., FreitasW. R. C. and Murthy B.R.N., Effect of Cutting Parameters on Surface Roughness and Cutting Force in Turning Mild Steel, Research Journal of Recent Sciences, International Science Congress Association ISSN 2277-252,Vol. 1(1), 19-26, October (212). [3]. Jaharah, A.G. et al. (29), The effect of uncoated carbide tool geometries in turning AISI 145 using finite element analysis European Journal of Scientific research, vol. 28, no.2, pp. 271-277. [4].. N. H. Rafai, M. N. Islam An Investigation into Dimensional Accuracy and Surface Finish Achievable in Dry Turning Preliminary Study [5] Bala Murugan Gopalswamy et al. 29. Taguchi method and ANOVA: An approach for process parameters of optimization of hard machining while machining hardened steel. Vol. 68, pp. 686-695. [6].. Diwakar Reddy V, Krishnaiah G, Hemanth Kumar A and Sushil Kumar Priya (211), ANN Based Prediction of Surface Roughness in Turning, International Conference on Trends in Mechanical and Industrial Engineering, (ICTMIE 211), December, pp. 165-177,Bangkok [7]. Madhu V N Ch., Sharma A V N L, Gopichand A and Pavan (212), Optimization of Cutting Parameters for Surface Roughness Prediction Using Artificial Neural Network in CNC Turning, IRACST Engineering Science and Technology: An International Journal (ESTIJ), Vol. 2, No. 2, ISSN: 225-3498. [8]. Ghani, M.U. et al. (27), An investigation of heat partition and tool wear in hard turning of H13 tool steel with CBN cutting tools International Journal of Advance Manufacturing and Technology, article id 17-7-1282-7. [9].Mohammad Reza Soleymani Yazdi and Saeed Zare Chavoshi. 21. Analysis and estimation of state variables in CNC face milling of AL661., Prod. Eng. Res. Devel. Vol.4, pp. 535 543, doi: 1.17/s1174-1-232 [1]. DuraiMatinsureshBabu, Mouleeswaran Senthilkumar, and Jothiprakashvishnuu (212), Optimization of Cutting Parameters for CNC Turned parts using Taguchi s Technique, International Journal of Engineering, Vol. 3, pp.493-496. [11].Vishnu. D. Asal, Chintan. A. Prajapati, Pintu. K. Patel. (213), Optimization of Turning Process Using Design of Experiment: A Review,Paripex - Indian Journal of Research, Vol.2,Issue 4, pp. 15-18. [12].Vikas B. Magdum and Vinayak R. Naik. (213), Evaluation and Optimization of Machining Parameter for Turning of EN 8 Steel, International Journal of Engineering Trends and Technology, Vol. 4, Issue5, pp. 1564-1568. [13]. D.I. Lalwani, N.K.Mehta, P.K.Jain, Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN25 steel, Journal of s Processing technology vol. 26, p.p 167-179, 28. 69