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- 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 188 OPTIMIZATION OF INPUT PARAMETERS OF CNC TURNING OPERATION FOR THE GIVEN COMPONENT USING TAGUCHI APPROACH Prabhat kumar sinha, Manas tiwari, Piyush pandey, Vijay kumar Mechanical Engineering Department Sam Higginbottom Institute of Agriculture Technology and sciences, Allahabad ABSTRACT Quality is inversely proportion to variability. In the other words as variability reduces, Quality improves. This approach has been used in this work. In the present work variability in the dimension of the manufactured part has been reduced. Reduction in the surface roughness as well as tolerance is the basic aim of this work. The increase of consumer needs for quality metal cutting related products (more precise tolerances and better product surface finish) has driven the metal cutting industry to continuously improve quality control of metal cutting processes. Within these metal cutting processes the turning process is one of the most fundamental cutting processes used in the manufacturing industry. Surface finish and dimensional tolerance, are used to determine and evaluate the quality of a product, are two of the major quality attributes of a turned product. The project work has been carried out at Neeraj Industries, Badli Industrial Area, Delhi in which the optimization of input parameter has been done for improvement of quality of the product in turning operation on CNC machine. Feed Rate, Spindle speed & Depth of cut are taken as the input variables and the dimensional tolerances and the surface roughness are taken as quality output. In the reduction of variation of performance characteristics and quality measures, Taguchi approach is very useful in the design of experiments. In the present work L9 Array has been used in design of experiment for optimization of input parameters. This project attempts to introduce and thus verifies experimentally as to how the Taguchi parameter design could be used in identifying the significant processing parameters and optimizing the surface roughness of the turning operation. There are two purposes of this research .The first is to demonstrate a systematic approach of using Taguchi parameter design of process control of individual CNC turning machine.The second is to demonstrate the use of Taguchi parameter design in order to identify the optimum surface roughness and dimensional tolerance performance with a particular combination of cutting parameters in a CNC turning operation.The present work shows that the spindle speed is key factor for minimizing the dimensional variation and feed rate is most effective input parameter for minimizing the surface roughness. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 4, July - August (2013), pp. 188-196 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
- 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 189 1- INTRODUCTION The project deals with the manufacturing of coupling of the juicer mixer grinder (JMG) for a known group which is planning to introduce new juicer mixer grinder in the market. The critical dimension is the diameter of the cylindrical projected part at the center of the coupling which has to be fit with the other part so other important quality parameter for this product is the surface roughness of the cylindrical part. The process involved for manufacturing this coupling is the turning process for which CNC lathe is used so that the close tolerances can be achieved. TURNING OPERATION Turning is the removal of metal from the outer diameter of a rotating cylindrical 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. Often the work piece will be turned so that adjacent sections have different diameters. Turning is the machining operation that produces cylindrical parts. In its basic form, it can be defined as the machining of an external surface: With the work piece rotating. With a single-point cutting tool, and With the cutting tool feeding parallel to the axis of the work piece and at a distance that will remove the outer surface of the work. Figure 1.1: Adjustable parameters in turning operation Turning is carried on a lathe that provides the power to turn the work piece at a given rotational speed and to feed to the cutting tool at specified rate and depth of cut. Therefore three cutting parameters namely cutting speed, feed and depth of cut need to be determined in a turning operation. The purpose of turning operation is to produce low surface roughness of the parts. Surface roughness is another important factor to evaluate cutting performance. Proper selection of cutting parameters can produce precise and lower surface roughness.
- 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 190 DIMENSIONAL ACCURACY Accuracy of an operation: closeness of the agreement between the actual value resulting from an operation and a target value of the quantity. Accuracy is a qualitative description. Uncertainty of an operation: parameter, associated with the result of an operation that characterizes the dispersion of the values that could reasonably be attributed to the quantity. SURFACE ROUGHNESS It is defined as closely spaced, irregular deviations on a scale smaller than that of waviness. Roughness may be superimposed on waviness. Roughness is expressed in terms of its height, its width, and its distance on the surface along which it is measured. Fig. 1.2 Surface Texture Waviness: It is a recurrent deviation from a flat surface, much like waves on the surface of water. It is measured and described in terms of the space between adjacent crests of the waves (waviness width) and height between the crests and valleys of the waves (waviness height). Flaws: Flaws, or defects, are random irregularities, such as scratches, cracks, holes, depressions, seams, tears, or inclusions as shown in Figure 1.2. Lay: Lay, or directionality, is the direction of the predominant surface pattern and is usually visible to the naked eye. Lay direction has been shown in Figure 1.2. FACTORS AFFECTING THE QUALITY OF TURNING PROCESS: Whenever two machined surfaces come in contact with one another the quality of the mating parts plays an important role in the performance and wear of the mating parts. The height, shape, arrangement and direction of these surface irregularities on the work piece depend upon a number of factors such as: A) The machining variables which include a) Cutting speed b) Feed, and c) Depth of cut. d) Cutting tool wears
- 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 191 (i) Depth of cut: Increasing the depth of cut increases the cutting resistance and the amplitude of vibrations. As a result, cutting temperature also rises. Therefore, it is expected that surface quality will deteriorate. (ii) Feed: Experiments show that as feed rate increases surface roughness also increases due to the increase in cutting force and vibration. (iii) Cutting speed: It is found that an increase of cutting speed generally improves surface quality. (iv) Cutting tool wears: The irregularities of the cutting edge due to wear are reproduced on the machined surface. Apart from that, as tool wear increases, other dynamic phenomena such as excessive vibrations will occur, thus further deteriorating surface quality. EXPERIMENTAL SETUP In this study, L9(33 ) orthogonal array of Taguchi experiment is selected for three parameters (speed, cutting depth, feed rate) with three levels in optimizing the multi-objective (surface roughness and dimensional tolerance) precision turning on an STORM-A50 CNC (Computerized Numerical Controlled) lathe. Through the examination of surface roughness (Ra) and the calculation of dimensional tolerance; the multiple objectives are then obtained. By using Grey Relational Analysis (GRA), the multiple objectives can additionally be integrated and introduced as the S/N (signal to noise) ratio into the Taguchi experiment. However, G.R.A. has not been included in the present work. The mean effects for S/N ratios are moreover analyzed by MINITAB software to achieve the optimum turning parameters. Through the verification results, it is shown that both surface roughness and dimensional tolerance from present optimum parameters are greatly improved in comparison to those from benchmark parameters. The precision diameter turning operation of Aluminium alloy (φ9.64 x 12 mm) work piece on an STORM-A50 CNC lathe is arranged for the research. The TOSHIBA WTJNR2020K16 tool holder with MITSUBISHI NX2525 insert is utilized as the cutting tool. MATERIALS AND METHODS The study proposed in this research was conducted in 2008. In this study, the multi-objective integration and parameter optimization technique for CNC turning operations on Aluminium Alloy are proposed using Taguchi method. TAGUCHI METHOD The Taguchi method is a robust design method technique, which provides a simple way to design an efficient and cost effective experiment. In order to efficiently reduce the numbers of conventional experimental tasks, the orthogonal array using design parameters (control factors) in column and standard quantities (levels) in row is proposed and further adopted. The performance measure, signal to noise ratio (S/N) proposed by Taguchi is used to obtain the optimum parameter combinations. The larger S/N means the relation to the quality will become better. The lower quality characteristic will be regarded as a better result when considering the smaller-the-better quality. The related S/N ratio is defined as:
- 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 192 (Equation:4.1) where, n is the number of experiments for each experimental set and yi expresses the quality characteristic at the ith experiment. On the contrary, the larger quality characteristic will have better result when considering the larger-the-better quality; therefore, by taking the inverse of quality characteristic into Eq. (4.1), the related S/N ratio can also be deduced and shown in Eq. (4.2). (Equation:4.2) In this study, the overall relational rating using GRA for multiple precision CNC machining objectives is introduced to the Taguchi experiment as the S/N ratio. Therefore, it is judged as the quality of larger the best. In addition to the S/N ratio, a statistical analysis of variance (ANOVA) [Wang and Lan, 2008] is to be employed to indicate the impact of process parameters. In this way, the optimal levels of process parameters can be estimated. EXPERIMENTAL SET UP The precision diameter turning operation of Aluminium alloy (φ9.64 x 12 mm) work piece on an STORM-A50 CNC lathe is arranged for the research. The TOSHIBA WTJNR2020K16 tool holder with MITSUBISHI NX2525 insert is utilized as the cutting tool. The relevant specification for the coupling of the JMG CONSTRUCTION OF ORTHOGONAL ARRAY In this study, three turning parameters (Cutting Speed, Feed Rate, and Depth of Cut) with three different levels (Table 4.1) are experimentally constructed for the machining operation. In Table 4.1, the three levels of cutting depth, feed rate and speed are identified from the machining handbook suggested by the tool manufacturer. The orthogonal array is then selected to perform the nine sets of machining experiments. The parameter levels for the experiments are illustrated in Table 4.2. PARAMETER TABLES AND LEVELS TABLE 4.1. PROCESS LEVELS AND PARAMETER DESIGNATION Parameter Designation Process Parameters Level 1 Level 2 Level 3 A Cutting speed(rpm) 1000 A3 1300 A2 1600 A1 B Feed rate(mm/rev) 0.02 B3 0.03 B2 0.04 B1 C Depth of cut(mm) 0.25 C3 0.30 C2 0.35 C1
- 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 193 TABLE 4.2: ORTHOGONAL ARRAY FOR 3 PARAMETER LEVELS Experiments Parameter A(Speed) B(Cutting Depth) C(Feed Rate) 1 Level 1 Level 1 Level 1 2 Level 1 Level 2 Level 2 3 Level 1 Level 3 Level 3 4 Level 2 Level 1 Level 2 5 Level 2 Level 2 Level 3 6 Level 2 Level 3 Level 1 7 Level 3 Level 1 Level 3 8 Level 3 Level 2 Level 1 9 Level 3 Level 3 Level 2 OBSERVATIONS AND RESULTS: As discussed in section 4.1 & 4.2 three levels of each input parameters Speed, Feed rate and depth of cut are taken and the experimental layout of three parameters using the L9 orthogonal array is formed as shown in Table 5.1. TABLE-5.1 EXPERIMENTAL LAYOUT USING AN L-9 ORTHOGONAL ARRAY Exp.No. PROCESS PARAMETER LEVELS A B C Speed (r.p.m.) Feed rate(mm/rev) Depth of cut (mm) 1 1600 0.04 0.35 2 1600 0.03 0.3 3 1600 0.02 0.25 4 1300 0.04 0.25 5 1300 0.03 0.35 6 1300 0.02 0.3 7 1000 0.04 0.3 8 1000 0.03 0.25 9 1000 0.02 0.35 Nine experiments are conduced for the above mentioned nine sets of parameters (speed, feed rate & depth of cut) and in each experiment 20 numbers of pieces are made and are checked with air gauge for dimensional tolerance and for surface roughness the pieces are tested in spectro-testing lab. The average value of dimensional tolerance and surface roughness in microns are listed in table 5.2.
- 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 194 TABLE 5.2: EXPERIMENTAL RESULTS EXP. NO. Factor Results SPEED (A)(R.P.M) FEED (B) (MM/REV) DEPTH OF CUT (C) (MM) DIMENSIONAL TOLERANCE (X)* (MICRONS) SURFACE ROUGHNESS (Y)** (MICRONS) 1 1600 0.04 0.35 1.192263 1.6 2 1600 0.03 0.3 1.311 1.74 3 1600 0.02 0.25 1.157 1.66 4 1300 0.04 0.25 1.564 1.38 5 1300 0.03 0.35 1.72 1.7 6 1300 0.02 0.3 2.10862 1.33 7 1000 0.04 0.3 1.28565 1.43 8 1000 0.03 0.25 1.3048 1.75 9 1000 0.02 0.35 1.85 1.35 * These Dimensional variations were obtained by Air Gauge in the company premises itself using 20 samples per reading. ** These Surface roughness values were obtained using the RA values obtained from the Lab Reports of Spectro Test Labs which is an ISO-9001:2000 and ISO-14001:2004 certified organisation and works in accordance to Protocol: IS-3073-1967. A sample of 3 pieces per reading was supplied to the aforesaid lab for testing purpose. ANALYSIS OF RESULTS In the Taguchi method the results of the experiments are analyzed to achieve one or more of the following three objectives: a) To establish the best or the optimum condition for a product or a process. b) To estimate the contribution of individual factors. c) To estimate the response under the optimum conditions. Studying the main effects of each of the factors identifies the optimum condition. The process involves minor arithmetic manipulation of the numerical result and usually can be done with the help of a simple calculator. The main effects indicate the general trend of the influence of the factors. Knowing the characteristic i.e. whether a higher or lower value produces the preferred result, the levels of the factors, which are expected to produce the best results, can be predicted. The knowledge of the contribution of individual factors is the key to deciding the nature of the control to be established on a production process. The analysis of variance (ANOVA) is the statistical treatment most commonly applied to the results of the experiment to determine the percent contribution of each factor. Study of the ANOVA table for a given analysis helps to determine which of the factors need control and which do not. In this study, an L9 orthogonal array with four columns and nine rows was used. This array has eight degree of freedom and it can handle three design parameters. Each parameter is assigned to a column, nine parameters combination being available. Therefore only nine parameters are required to study the entire parameter space using the L9 orthogonal array.
- 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 195 CONCLUSION It is found that the parameter design of the Taguchi method provides a simple, systematic and efficient methodology for the optimization of process parameters. Based on the results obtained in this study, the following can be concluded: a) The percentage contribution of cutting speed is 57.2%, feed rate is 23.4%, depth of cut is 12.7% and that of error is 6.7% for minimum dimensional tolerance. b) The percentage contribution of the cutting speed is maximum i.e. 57.2 % for obtaining the minimum value of the dimensional variation. c) The optimum combination of the parameters and their levels for obtaining minimum dimensional variation is A1B1C3 (spindle speed = , feed = , depth of cut = ). d) The percentage contribution of cutting speed is 26.5 %, feed rate is 60.6 %, depth of cut is 5.7% and that of error is 7.2% for minimum value of surface roughness. e) The percentage contribution of the feed rate is maximum i.e. 60.6 % for obtaining the minimum value of the surface roughness. f) The optimum combination of the parameters and their levels for obtaining minimum surface roughness is A2B3C2 (spindle speed = , feed = , depth of cut = ) . g) Out of the above two combinations the surface roughness and dimensional tolerance were found to be the minimum at A2B3C2 with the RA value of 0.91 microns and standard deviation of 1.15 microns. h) The initial values of surface roughness and standard deviation for dimensional variation that were obtained by the operator without the application of Taguchi technique were 1.98 microns and 2.10862 microns respectively. The final values of surface roughness and standard deviation for dimensional variation that were obtained using the optimal parameters as suggested in the project work are 0.91 microns and 1.15 microns respectively. Thus, it can be safely concluded that the output quality conditions (Surface Roughness and Dimensional Tolerance or standard deviation for dimensional variation in our case) are greatly advanced by the application of Taguchi technique. Also, the final results are in total conformance with the customer expectations. Hence, one can very well conclude that the project work is successfully completed. REFERENCES 1. Agapiou J S 1992 The optimization of machining operations based on a combined criterion, Part 1: The use of combined objectives in single-pass operations, Part 2: Multi-pass operations. J. Eng. Ind., Trans. ASME 114: 500–513 2. Armarego E J A, Brown R H 1969 The machining of metals (Englewood Cliffs, NJ: Prentice Hall) ASME 1952 Research committee on metal cutting data and bibliography. Manual on cutting of metals with single point tools 2nd edn. 3. A.S. Shouckry(1982) “The effect of cutting conditions on dimensional accuracy” Wear, Volume 80, Issue 2, 16 August 1982, Pages 197-205. 4. Barker T B 1990 Engineering quality by design (New York: Marcel Dekker) 5. Behzad, M. and Chartrand, G. “Introduction to the Theory of Graphs”. Boston, Allyn and Bacon, Boston, 1971) pp-271. 6. Benardos, P.G. and Vosniakos, G.C., “Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments”, (Robotics and computer integrated manufacturing 18 , 2002)), pp. 343-354.
- 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 196 7. Benton W C 1991 Statistical process control and the Taguchi method: A comparative evaluation. Int. J. Prod. Res. 29: 1761–1770 8. Bhattacharya A, Faria-Gonzalez R, Inyong H 1970 Regression analysis for predicting surface finish and its application in the determination of optimum machining conditions. Trans. Am. Soc. Mech. Eng. 92: 711 9. Brewer R C 1966 Parameter Selection Problem in Machining. Ann. CIRP 14: 11 10. Brewer R C, Rueda R 1963 A simplified approach to the optimum selection of machining parameters. Eng. Dig. 24(9): 133–150 11. Chanin M N, Kuei Chu-Hua, Lin C 1990 Using Taguchi design, regression analysis and simulation to study maintenance float systems. Int. J. Prod. Res. 28: 1939–1953 12. Vipin Kumar Sharma, Qasim Murtaza and S.K. Garg, “Response Surface Methodology & Taguchi Techquines to Optimization of C.N.C. Turning Process”, International Journal of Production Technology and Management (IJPTM), Volume 1, Issue 1, 2010, pp. 13 - 31, ISSN Print: 0976- 6383, ISSN Online: 0976 – 6391. 13. Prof. (Dr). Rachayya.R.Arakerimath and Prof (Dr).V.A.Raikar, “Productivity Improvement by Sa and Ga Based Multi-Objective Optimization in CNC Machining” International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 1, 2012, pp. 100 - 109, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 14. Ajeet Kumar Rai, Shalini Yadav, Richa Dubey and Vivek Sachan, “Application of Taguchi Method in the Optimization of Boring Parameters”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 4, 2013, pp. 191 - 199, ISSN Print: 0976-6480, ISSN Online: 0976-6499. 15. Prabhat Kumar Sinha, Vijay Kumar, Piyush Pandey and Manas Tiwari, “Static Analysis of Thin Beams by Interpolation Method Approach To Matlab”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 4, 2013, pp. 254 - 271, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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