Optimization of Cutting Parameters on CNC Lathe to

International Conference on Manufacturing Excellence (MANFEX 2013)

Optimization of Cutting Parameters on CNC Lathe to Improve the Surface Roughness by Response Surface Methodology (RSM) Ashish Srivastava1#, Rahul Kumar Singh2, Gaurav Kumar Agarwal3, S.P. Diwedi4, Shubham Sharma5 1#

Assistant Professor, 2,3Research Scholar, 4Assistant Professor 1,2,3,4 Department of Mechanical Engineering Noida Institute of Engineering & Technology, Greater Noida- India 5 Assistant Professor, Department of Mechanical Engineering Amity University, Noida-India 1 [email protected]

Abstract: Surface finish is one of the most prime requirement of customer and it is also a significant tool to reduce the cycle time of any machine operation as well as the overall cost of the production. In the recent years, quality of product is a essential demand of customer which turned to the fast and rapid technologies of production. This paper presents an experimental study on aluminium alloy-2024 to investigate the effects of three cutting parameters like spindle speed, feed and depth of cut for three levels of each parameters on surface finish. Response surface methodology (RSM) technique has been applied to optimize cutting parameters for minimum surface roughness. The analysed result shows that, by increasing the spindle speed, surface roughness decreases while depth of cut and feed increases the surface roughness. RSM gives a optimum combination of all three parameters for the best results. Keywords: surface roughness, response surface methodology, Al-2024.

1.

INTRODUCTION

In the modern era of science and technology the demand for the precise engineering products has became the vital manufacturing sector to produce dimensionally accurate product .From the past research and experiment it has been explained that surface roughness has a great impact on the functioning of the machined parts. [1, 2] The properties such as corrosion resistance, fatigue resistance, load bearing capacity, noise reduction, are influenced by the surface roughness. Several manufacturing process such as casting, powder metallurgy, hot working uses the integration of man, machine and material which suffers the surface irregularities due to error. Whatever may be the manufacturing process used, it is not possible to produce perfectly smooth surface. The imperfection and irregularities are to bind to occur. However optimum process and its parameter can be utilised to overcome irregularities to a great extent. In regard to better surface finish and the surface roughness and metal

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removal rate, the selection of proper combination of machining variables is required. [2] In the present work, the statically data has been made and analysed to predict the surface roughness with the help of Response Surface Method (RSM) and design of experiment [3]. Implementation of RSM (Response Surface Method) methodology is practically accurate and easy. By Response Surface Method, optimisation procedure is selected to optimize the output response, surface roughness and metal removal rate of work under turning operation namely speed, feed and depth of cut. [4] The work material used under study is Al-2024. This alloy has copper as the primary alloying element. The composition of Al-2024 include 4.4% copper, 0.6% manganese, 1.4% magnesium and less half percent of silicon, zinc, nickel, chromium, lead and bismuth. Al-2024 has density of 2.78gm/cm3 and young modulus as 73 GPa. It is wieldable only frictional welding and has average machinability. It has poor corrosion resistance. Al-2024 finds its application in aircraft structure, especially wings, shear web and ribs and structural areas where stiffness, fatigue, performance and good strength are required. The turning process was carried on CNC. Lathe machine model HE-100-CNC-PC with basic specification swing over bed 100mm.The choice of high speed CNC process is for lower cost productivity and quality requirement generally aiming at higher productivity with best quality of precision surface quality within the given constraint for precision component machining has been the key issue for the study. 2.

LITERATURE REVIEW

Phani Sastry Dr. M. Naga, Devi K. Devaki, Reddy Dr, K. Madhava (2012) presented the Analysis and Optimization of Machining Process Parameters using Design of Experiments. Current investigation on turning process is a

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International Conference on Manufacturing Excellence (MANFEX 2013)

Response Surface Methodology applied on the most effective process parameters i.e. feed, cutting speed and depth of cut while machining Aluminium alloy and resin as the two types of work pieces with HSS cutting tool. The main effects (independent parameters), quadratic effects (square of the independent variables), and interaction effects of the variables have been considered separately to build best subset of the model. Three levels of the feed, three levels of speed, three values of the depth of cut, two different types of work materials have been used to generate a total 20 readings in a single set. After having the data from the experiments, the performance measures surface roughness (Ra) of the test samples was taken on a profilometer and MRR is calculated using the existing formulae. To analyze the data set, statistical tool DESIGN EXPERT-8 (Software) has been used to reduce the manipulation and help to arrive at proper improvement plan of the Manufacturing process & Techniques. Hypothesis testing was also done to check the goodness of fit of the data. A comparison between the observed and predicted data was made, which shows a close relationship. Thangarasu V. S., Devaraj G., Sivasubramanian R.(2012) present a paper on High speed CNC machining of AISI 304 stainless steel; Optimization of process parameters by MOGA. This work is to establish the relationship with the basic parameters to the responses namely Surface roughness (Ra) and Material Removal Rate (MRR). The Taguchi based Box-Behnken RSM (Response Surface Methodology) method is used to develop prediction formula and Multi Objective Genetic Algorithm (MOGA) is used for High speed CNC milling process optimization with higher Spindle speed, Feed rate and Depth of cut for better surface finish and material removal rate. Janardhan M. and Krishna A. Gopala (2012) suggest the multi-objective optimization of cutting parameters for surface roughness and metal removal rate in surface grinding using response surface methodology . In there work, empirical models are developed for surface roughness and metal removal rate by considering wheel speed, table speed and depth of cut as control factors using response surface methodology. The second order mathematical models in terms of machining parameters were developed for metal removal rate (MRR) and Surface roughness on the basis of experimental results. The model selected for optimization has been validated with F-test. The adequacy of the models is tested on output responses have been established with analysis of variance (ANNOVA). Yanda H., Ghani J.A., Rodzi M.N., Othman K. and Haron C.H.C. (2010) present a paper on optimization of material removal rate, surface roughness and tool life on conventional dry turning of fcd700. There study aims to investigate the effect of the cutting speed, feed rate and depth of cut on material removal rate (MRR), surface roughness, and tool life in conventional turning of ductile

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cast iron FCD700 grade using TiN coated cutting tool in dry condition. The effect of cutting condition (cutting speed and feed rate) on MRR, surface roughness, and tool life were studied and analyzed. Lalwani D.I., Mehta N.K., Jain P.K (2007) has done the Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. In the present study, an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate and depth of cut) on cutting forces (feed force, thrust force and cutting force) and surface roughness in finish hard turning of MDN250 steel (equivalent to 18Ni(250) maraging steel) using coated ceramic tool. The machining experiments were conducted based on response surface methodology (RSM) and sequential approach using face centered central composite design. The results show that cutting forces and surface roughness do not vary much with experimental cutting speed in the range of 55–93 m/min. Depth of cut is the dominant contributor to the feed force, accounting for 89.05% of the feed force whereas feed rate accounts for 6.61% of the feed force. In the thrust force, feed rate and depth of cut contribute 46.71% and 49.59%, respectively. In the cutting force, feed rate and depth of cut contribute 52.60% and 41.63% respectively QianLi, Hossan Mohammad Robiul (2007) has presented a paper on Effect on cutting force in turning hardened tool steels with cubic boron nitride inserts Numerical simulations of high-speed orthogonal machining were performed to study the finish hard-turning process as a function of cutting speed, feed, cutter geometry, and workpiece hardness. In the simulations, properties representative of AISI 52100 bearing steel, AISI H13 hot work tool steel, AISI D2 cold work steel, and AISI 4340 low alloy steel were assumed for the workpiece. Cubic boron nitride (CBN) or polycrystalline (PCBN) inserts are widely used as cutting tool material in such high-speed machining of hardened tool steels—due to high hardness, high abrasive wear resistance, and chemical stability at high temperature. 3.

EXPERIMENTAL SETUP

3.1. Work piece Turning experiment was performed in dry condition using a high speed CNC model, HE-100-CNC-PC, with spindle speed 50 to 3000 rpm. Alloy 2024 was introduced in 1931 as an alclad sheet. It was the first Al-Cu-Mg alloy to have a yield strength approaching 50,000-psi and generally replaced 2017-T4 (Duralumin) as the predominant aircraft alloy. With its relatively good fatigue resistance, especially in thick plate forms, It has good Machinability but only fair corrosion resistance. Not recommended for brazing or

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Optimization of Cutting Parameters on CNC Lathe to Improve the Surface Roughness by Response Surface Methodology (RSM)

soldering. Good workability. Good appearance. It contains Aluminium as main composition along with following alloying elements. Table 3.1 Composition of Al-2024

Element Copper manganese magnesium silicon, zinc, nickel, chromium, lead and bismuth Al

Composition (in %) 4.4 0.6 1.4 Less than 0.5 Rest portion

The cylindrical shaped work piece of Al-2024 is used with following Dimension. Table 3.2 Dimensions of Work-piece

Dimension Length Diameter

Value (in mm) 152.0 25.4

Al-2024 has following Characteristics:-

Physical

and

Mechanical

Table 3.3 Characteristics of Al-2024

Properties Density Hardness, Brinell Ultimate Tensile Strength Tensile Yield Strength Elongation at Break Modulus of Elasticity Poisson's Ratio Fatigue Strength Machinability Thermal Conductivity Melting Point

Value 2.78 g/cc 120 469 MPa 324 MPa 19% 73 GPa 0.33 138 MPa 80% 121 W/mK 600oC

4.

METHODOLOGY

In this work Experimental result were used for modelling using response Surface methodology, is a practical, accurate and easy for implementation. The experimental data obtained from Response surface method(RSM) were used to built 1st order, 2nd order mathematical model .This developed mathematical models were optimized by using RSM optimization method for the output responses Surface roughness for input machining parameter namely Speed, feed and depth of cut. 4.1 Design of Experiment The study of most important variable affecting quality characteristics and a plan for conducting such experiment is called design of experiment. 4.2 Response Surface Methodology (RSM) Response Surface Methodology is combination of mathematical and statistical technique, used to develop the mathematical model for analysis and optimization[6]. By conducting experiment trails and applying the regression analysis, the output responses can be expressed in terms of input machining parameters namely table speed, depth of cut and spindle speed. The major steps in Response Surface Methodology are: 1.

Identification of predominate factors which influences the surface roughness.

2.

Developing the experimental design matrix, conducting the experiments as per the above design matrix.

3.

Developing the mathematical model.

4.

Determination of developed model.

3.2 Machine and other specifications

5.

Testing the significance of the coefficients.

For this present paper turning process was carried on CNC with the Specifications as in table 3.4.

6.

Adequacy test for the developed model by using analysis of variance (ANNOVA).

Table 3.4 Specification of machine and tool

7.

Analyzing the effect of input machining parameters on output responses, surface roughness. [7]

Model Swing over Bed Distance between Centre Spindle Speed Spindle Motor Standard Cutting tool Size Rapid Feed rate X Rapid Feed rate Z Lubrication pump Motor

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HE-100-CNC-PC 100 mm 310 mm 50 to 3000 rpm DC motor,2 hp 16X16 mm 700mm/min 700mm/min 0.1hp

5.

constant

coefficients

of

the

EXPERIMENTAL DETAILS

A set of experiments were conducted on CNC Lathe machine to determine effect of machining parameters namely speed(rpm), feed(mm/rev), depth of cut (mm) on output responses namely surface roughness. Material used for the experiment is Aluminium 2024, having 4.4% and copper 0.6% as major compositions. Three levels and three factors used to design the orthogonal array by using design

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International Conference on Manufacturing turing Excelle Excellence (MANFEX 2013)

Table 5.1 Levels of control factors

of experiment (DOE) and relevant ranges of paramete parameters as shown in Table5.1. S. No.

Control Factor

Symbol

1 2 3

Speed Feed Depth of cut

N F D

6.

Fig. 5.1 Tomlinson Roughness Meter (Thomas, 1999)

Levels of factors Unit 1 2 100 150 0.15 0.2 0.2 0.4

3 200 0.25 0.6

RPM mm/rev Mm

MEASURMENT TECHNIQUES TECHNIQUE

Stylus Instruments are used as a measurement tool, it this tool a probe is running on the surface of the metal piece which detects the variation of the surface in terms of height as a function of distance (Thomas, 1999). A schematic representation epresentation of this instrument is depicted in Figure Fig 5.1. In the next step the difference in vertical displacements displaceme are converted in electrical digital signals with the help he of a transducer incorporated with the stylus. This signal signa can then be processed by the instrument to calculate a suitable surface roughness. The selected combination of speed, depth and feed conduct the experiment is shown in Table 5.2 5 along with the output responses, surface roughness. These results were further used to analyze the effect e of input machining parameters on output responses with the help h of RSM and design expert software.

Table 5.2 Experimental Observations

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Optimization of Cutting Parameters on CNC Lathe to Improve the Surface Roughness by Response Surface Methodology (RSM)

7.

RESULT AND DISCUSSION

Results are coming out by applying the response surface methodology. All the input variables are analysed and compared by the actual values with predicted. Table 6.1 ANOVA table by the Response surface methodology

Table 6.1 shows the ANOVA table with 17 different combination of speed, feed and depth of cut. In the RSM Sofware A stands for speed, B stands for feed and C for depth of cut.

Table 6.2 Standard deviation and mean value

Std. Dev. Mean

0.27 4.17

The Model F-value of 12.50 implies the model is significant. There is only a 0.15% chance that a "Model FValue" this large could occur due to noise.Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C, AB are significant model terms Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve the model.

C.V. %

6.43

PRESS

2.95

The "Lack of Fit F-value" of 0.56 implies the Lack of Fit is not significant relative to the pure error. There is a 66.75% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good.

"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 13.062 indicates an adequate signal. This model can be used to navigate the design space.

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R-Squared Adj RSquared Pred RSquared Adeq Precision

0.9414 0.8661 0.6574 13.062

The "Pred R-Squared" of 0.6574 is not as close to the "Adj R-Squared" of 0.8661 as one might normally expect. This may indicate a large block effect or a possible problem with the model and/or data. Things to consider are model reduction, response tranformation, outliers, etc.

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Final Equation in Terms of Actual Factors Surface Roughness = -2.29700+0.048545* Speed+28.03500* Feed+2.47000* Depth of Cut 0.14500* Speed * Feed -5.00000E-003* Speed * Depth of Cut+23.00000* Feed * Depth of Cut -1.09900E2 2 004* Speed -25.90000* Feed -5.18125* Depth of 2 Cut

The above figure 6.2 represents the graph between actual and predicted values of surface roughness. The actual values are coming out by the measurement of roughness of the sample pieces by measuring instruments and the predicted values are carried out by the Response surface methodology. The results shown that there is approximate a straight line curve is obtained by the analysis it indicates that the actual and predicted values are nearly equal and the error is very less.

The above equation carried out by the RSM methos which gives the value of surface roughness as a function of speed, feed and depth of cut.

Fig. 6.1 Graph between Normal % probability and residuals

Here graph represents the normal % probability v/s Residuals as shown in figure 6.1 For the better result normal % probability and residuals should must come in a straight line. Straight line curves shows that all the variable parameters (speed, feed, depth of cut) are fit with respect to surface roughness. The obtained results from the analysis shows that the results are best fit due to the less error ie the difference between actual and predicted values.

Fig. 6.3 Graph between surface roughness and speed

Figure 6.3 shows the graph between speed and surface roughness as the graph indicates that while increasing the speed, roughness will be decreases and follow the equation of a parabolic.

Fig. 6.4 Graph between Feed and surface roughness

Fig. 6.2 Graph between actual and predicted values

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Figure 6.4 shows the graph between feed and roughness. It indicates that while increasing the feed the surface

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Optimization of Cutting Parameters on CNC Lathe to Improve the Surface Roughness by Response Surface Methodology (RSM)

roughness will be increases. it also follow a parabolic curve and equation. [2]

[3]

[4]

[5]

[6] [7] Fig. 6.5 Graph between depth of cut and surface roughness

Figure 6.5 represents the graph between depth of cut and surface roughness. It indicates that while increasing the depth of cut, surface roughness will be increases and it follow the equation of parabola. 8.

CONCLUSION

The current study was done to study the effect of machining parameters on the surface roughness. The following conclusions are drawn from the study: Major factor affecting the Surface Roughness is cutting speed. Surface Roughness is inversely proportional to the cutting speed .A parabolic curve is obtained when plotted against each other. Decreasing parabolic curve is obtained with the increase in cutting speed and vice versa. Feed is directly related to Surface Roughness, it increases and decreases with the increase and decrease in feed along the parabolic curve but the curvature of the curve is less than parabolic curve obtained in Surface roughness and speed curve. Like the Feed curve Depth of Cut is also directly related to Surface Roughness but the curvature is more.

[8]

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[18] John, J.L., L. Yang and J.C. Chen (2001), A systematic approach for identifying optimum surface roughness performance in end-milling operations, Journal of Ind. Technol., vol. 17, pp1-8. [19] Kopac J., M. Bahor and M. Sokovic (2002), Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel workpieces, Machine Tools Manufacturing, vol 42, pp707-716 [20] Korkut I., Kasap M., Ciftci I. and Seker U (2004), Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel, Materials & Design, vol 25(4), pp 303-305 [21] M. Kaladhar, K. V. Subbaiah, Ch. SrinivasaRao and K. NarayanaRao (2011), Application of Taguchi approach and utility concept in solving the multi-objective problem when turning AISI 202 austenitic stainless steel, Journal Of Engineering Science And Technology, vol. 4 (1) pp 55-61

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[22] Nikolaos i. Galanis&dimitrios e. Manolakos (2010), Surface roughness prediction in turning of femoral head, int j advmanufacturing technology ,doi 10.1007/s00170-010-26164 [23] P. J. Ross (1996), Taguchi Techniques for Quality Engineering, McGraw-Hill Book Company, New York [24] Qian Li, Hossan Robiul Mohammad “Effect on cutting forces in turning hardened tool steel with cubic boron nitride insert”. (2007). [25] T. G. Ansalam Raj and V.N .Narayanan Namboothiri (2010), An improved genetic algorithm for the prediction of surface finish in dry turning of SS 420 materials, Manufacturing Technology Today Vol.47, pp 313-324 [26] Thomas, T.R., Rough Surfaces, 2nd ed., Imperial College Press, London (1999).

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