Experimental Investigation of Machining Parameters in Turning Operation Using Taguchi Analysis

Transcription

1 Anshul Sen et al. 2016, Volume 4 Issue 6 ISSN (Online): ISSN (Print): International Journal of Science, Engineering and Technology An Open Access Journal Experimental Investigation of Machining Parameters in Turning Operation Using Taguchi Analysis 1 Anshul Sen, 2 Dr. Shailesh Dewangan Abstract The objective was to seek out the optimum machining parameters thus on minimize the input of the resources and to maximize the output of the process. The present work involved an experimental study of turning on alloy steel of AISI 4320 grade. The aim of this thesis is to review the effect of speed, feed, and depth of cut on material removal rate, metal surface and tool wear in machining AISI 4320 alloy steel using tungsten carbide tipped cutter. Experimental were conducted on CNC machine and also the influence of cutting parameter was studied via analysis of variance (ANOVA) base on acquainted approach, supported the most effects plots get through Taguchi Analysis, a total of eighteen tests were administrated. Optimum level for MRR, Surface roughness and depth of cut were chosen from the 3 levels of cutting parameters thought of the selection of every parameter is place at 3 completely different levels, namely low, medium and high. Mathematical models were deduce by software system design specialist so as to precise the influence degree of the most cutting variables like cutting speed, feed rate and depth of cut on MRR, SR and Flank wear. The results indicate that the cutting speed is that the dominant issue affecting all 3 investigation parameters. Keywords: ANOVA, Optimization, MRR, SR, FW Introduction Metal cutting process forms the basis of the engineering industry and is involved either directly or indirectly in the manufacture of nearly every product of our modern civilization. The cutting tool is one of the important elements in realizing the full potential out of any metal cutting operation. Over the years the demands of economic competition have motivated a lot of research in the field of metal cutting leading to the evolution of new tool materials of remarkable performance and vast potential for an impressive increase in productivity. 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. 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. Gautam Kumar [1] confirmed that after the experiment and analysis of the turning operation of 304 SS with carbide insert tool shows t the optimization of multi-objective responses can be done efficiently and in simplified way by using Taguchi and grey relational analysis approach. From the analysis it can be shown that feed rate and cutting speed are the main important input parameters which affect the cutting force and the surface roughness most. By this technique the turning operation performance characteristic like cutting force and surface roughness can be improved together. Yang W.H et al [2] confirmed that the Taguchi method provides a systematic and efficient methodology for the design optimization of the cutting parameters with far less effect than would be required for most optimization techniques. It has been shown that tool life and surface roughness can be improved significantly for turning operations. The confirmation experiments were conducted to verify the optimal cutting parameters. The improvement of tool life and surface roughness from the initial cutting parameters to the optimal cutting parameters is about 250%. Satyanarayana Kosaraju et al [5] investigation has been shown that cutting force and temperature were reduced significantly for turning operation by conducting experiments at the optimal 2016 Anshul Sen et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 863

2 parameter combination and also by analyzing S/N ratio. The conformation experiments were also conducted to verify the optimal combination of parameters obtained. Good agreement between the predicted and actual values for force and temperature has been observed. Sunil Dambhare et al [7] Study has revealed that surface roughness by large is influenced by cutting environment and the kind of tool. It was also found that material removal rate is influenced by tool type, cutting velocity, feed and depth of cut while power required for machining depends on cutting environment, tool type cutting velocity and depth of cut. Jagadesh T et al [8] done investigations into cutting forces and surface roughness in micro turning of titanium alloy using coated carbide tool. In their paper, development of micro turning setup and experimental investigations on cutting forces and surface roughness during micro turning of titanium alloy using coated carbide tool has been discussed. When the cutting speed increase there is decrease in cutting forces while uncut chip thickness is greater than the cutting edge radius due to the increase in tool chip interface temperature. The cyclic variation of the cutting force is due to segmented chip formation during micro turning of titanium alloy. Surface roughness is mainly influenced by the edge radius of the cutting insert and uncut chip thickness. M. Nalbant et al [16] The experimental results demonstrate that the insert radius and feed rate are the main parameters among the three controllable factors (insert radius, feed rate and depth of cut) that influence the surface roughness in turning AISI 1030 carbon steel. Taguchi s robust orthogonal array design method is suitable to analyze the surface roughness (metal cutting) problem. Material and Method In this study, a work piece made of AISI 4320 grade (Ni Cr Mo Steel) steel was used. Its sizes were ϕ 40 x 100 mm of 6 specimens was used for the experimental work. The experimental studies were carried out on an Ace designer LT-16 CNC lathe. The experiments were conducted under dry cutting conditions. Tungsten carbide tipped tool model K20 were used as a cutting tool material. The surface roughness was measured using a Mitutoyo Surfest SJ-410 portable device within the sampling length of 25 mm. Fig.1 shows the experimental arrangement. The level of cutting parameter ranges and the initial parameter values were chosen from the manufacturer s handbook recommended for the tested material. These cutting parameters are shown in Table 1. Parameters Cutting Speed (m/min) Depth of Cut (mm) Figure 1: Experimental scheme Table 1: Cutting Parameters Feed 1 Feed mm/rev 0.16 mm/rev The Taguchi method was used to reduce number of experiments. The design of experiments (DOE) and measured values of MRR, SR and FW are shown in Table 2. The experiments were conducted with 6 specimens. The Experiment Deign Using the Taguchi Method The traditional experimental design methods are too Complex and difficult to use. Additionally, large numbers of experiments have to be carried out when the number of machining parameters increase. Therefore, the factors causing variations should be determined and checked under Laboratory conditions. These studies are considered under the scope of off-line quality improvement [20 22].The Taguchi method is an experimental design technique, which is useful in reducing the number of experiments dramatically by using orthogonal arrays and also tries to minimize effects of the factors out of control. The basic philosophy of the Taguchi method is to ensure quality in the design phase. The 864

3 greatest advantages of the Taguchi method are to decrease the experimental time, to reduce the cost and to find out significant factors in a shorter time period [23]. Analyzing and Evaluating Results of the Experiment Table 2: Result obtained from experiment S. Feed (mm/rev) Cutting Speed Depth of MRR(c ) SR (μm) Flank No. (m/min) cut (mm) W.(mm)

4 Multi- Objective Optimization Using Taguchi Quality Loss Function The Taguchi quality loss function is to calculate the deviance between the experimental value and desired value [33]. The calculation of Taguchi quality loss function is following steps, 1. All experimental value converted to loss function (Lij). 2. Normalized loss function (Nij). 3. Calculated the total loss function (TLij). 4. Finally calculated the Taguchi quality loss function (ηj). In the first steps for all experimental observed value of MRR and OC are converted into loss function (Lij) that is shown in Table 3. MRR is Higher-The-Better (HTB) quality characteristics and OC is the Lower- The- Better (LTB) quality characteristics. The loss function of HTB and LTB quality characteristics are show in Equation 1 and 2 respectively. LTB response variable = (1) HTB response variable = (2) Where, and is the loss function and experimental value, respectively, for experiment using response. The second step for normalized the loss function of MRR and SR & Flank Wear, with the help of Equation 3. = (3) Where, = is the average loss function of experimental value and n is the trial number. Then calculate the total loss function by using Equation 4. Where, is the weighted function and the Σ = 1. p is the performance characteristics. Then the final steps to calculate the Taguchi loss function with the help of Equation 5. (4) = -10log (T ) (5) Where, the is the Taguchi loss function and the Taguchi loss function is shown in Table Table 3: Taguchi Quality loss function S N. MRR SR Flank Wear MRR Lij SR Lij FW Lij Nij MRR Nij SR Nij FW Tij ηij Rank

5 ANOVA for Taguchi Quality loss function Taguchi Analysis: MPCI versus F (mm/rev), S (m/min), Linear Model Analysis: Means versus F (mm/rev), S (m/min), d (mm) d (mm) Table 4: Estimated Model Coefficients for Taguchi Quality loss function Term Coef SE Coef T-value P-value Constant F (mm/rev S (m/min S (m/min d (mm) d (mm) S = R-Sq = 76.8% R-Sq(adj) = 67.1% Table 5: Analysis of Variance for Taguchi Quality loss function Source DF Seq SS Adj SS Adj MS F-value P-value F (mm/rev) S (m/min)

6 d (mm) Residual Error Total Table 6: Response Table for Taguchi Quality loss function Level F S (m/min) d (mm) (mm/rev) Delta Rank I would like to acknowledge the Central Tool Room & Training Centre, Bhubaneswar for providing machines for conducting the research. I would also like to thanks to Dr. Shaliesh Dewangan for their guidance and support to this work. References Fig 2: Mean Effect Plot for MPCI Conclusion Taguchi quality loss function was adopted to optimize the Turning process with multiple performance characteristics i.e. material removal rate, surface roughness and flank wear. The optimal turning parameter setting were found to be setting cutting speed 230 m/min, feed 0.08 rev/min. and depth of cut 16 mm for maximum material removal rate and for minimum surface roughness and flank wear. Speed is the dominant factor here followed by feed rate then depth of cut. Acknowledgement 1) Kumar, G., (2013), Multi Objective Optimization of Cutting and Geometric parameters in turning operation to Reduce Cutting forces and Surface Roughness, B.Tech. thesis, Department of Mechanical Engineering, National Institute of Technology, Rourkela. 2) Yang W.H. and Tarng Y.S., (1998), Design optimization of cutting parameters for turning operations based on Taguchi method, Journal of Materials Processing Technology, 84(1) pp ) Nithyanandam J., Das S.L. and Palanikumar K. Influence of cutting parameters in machining of titanium alloy (April 2015) (Indian Journal of Science and Technology, Vol 8(S8)) pp ) Asilturk I.,Akkus H. Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method on AISI 4140 Grade Steel (2011) pp ) Kosaraju S., Anne V.G., Popuri B.B. Taguchi Analysis on cutting forces and temperature in turning Titanium Ti- 868

7 6Al-4V (2012) (International journal of Mechanical Engineering ISSI no , vol-1 issue-4, 2012). pp ) Prete A.D., Primo T., Franchi R. Super-Nickel Orthogonal Turning Operations Optimization (2013) (14th CIRP Conference on Modeling of Machining Operations (CIRP CMMO). pp ) Dambhare S., Deshmukh S., Borade A., Digalwar A. Sustainability issue in turning operation a study in Indian Industry on AISI 1040 Carbon steel {EN-8} (2015) (12th Global Conference on Sustainable Manufacturing). pp ) Jagadesh T, Samuel G L Investigation into cutting forces and surface roughness in micro turning of Titanium Alloy Ti-6Al-4V using coated carbide (2014) (International conference on advances in manufacturing and materials engineering AMME 2014). pp ) Sharma A V N L, P., Raju S., Gopichand A., Subbaiah K.V. Optimization of cutting parameters on Mild steel with HSS and Cemented Carbide tipped tools using ANN {Artificial Neural Network} (Nov 2012) (IJRET: International Journal of Research in Engineering and Technology ISSN: ) pp ) Dipti Kanta Das, Ashok Kumar Sahoo, Ratnakar Das, B. C. Routara Investigations on hard turning using coated carbide insert: Grey based Taguchi and regression methodology on EN 24 steel (3rd International Conference on Materials Processing and Characterization (ICMPC) (2014) pp ) T. Tulsiramarao, Dr. K Srinivas, Dr. P. Ram Reddy, A. Ravindra, Dr. V.R. Ravi Kumar Experimental study on the effect of cutting parameter on surface finish obtained in CNC turning operation on Stainless steel & Aluminum (International Journal of Innovative Research in Science, Engineering and Technology{Vol. 2, Issue 9, September 2013}) pp ) Dash, S.K., (2012), Multi Objective Optimization of Cutting Parameters in Turning Operation to Reduce Surface Roughness and Tool Vibration, B.Tech. thesis, Department of Mechanical Engineering, National Institute of Technology, Rourkela. 13) Halim, M.S.B., (2008), Tool Wear Characterization of Carbide Cutting Tool Insert in a Single Point Turning Operation of AISI D2 Steel, B.Tech. thesis, Department of Manufacturing Engineering, Universiti Teknikal Malaysia Mekala. 14) Khandey, U., (2009), Optimization of Surface Roughness, Material Removal Rate and cutting Tool Flank Wear in Turning Using Extended Taguchi Approach, MTech thesis, National Institute of Technology, Rourkela. 15) Faisal, M.F.B.M., (2008), Tool Wear Characterization of Carbide Cutting Tool Inserts coated with Titanium Nitride (TiN) in a Single Point Turning Operation of AISI D2 Steel, B.Tech. thesis, Department of Manufacturing Engineering, Universiti Teknikal Malaysia Mekala. 16) M. Nalbant, H. Go kkaya, G. Sur Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning on AISI 1030 Carbon Steel Materials and Design 28 (2007) pp ) S. Ramesh, L. Karunamoorthy, K. Palanikuma Measurement and analysis of surface roughness in turning of aerospace titanium alloy Ti-6Al-4V (grade 5) Measurement 45 (2012) pp ) M. Sadilek, J. Dubsk y, Z. Sadílkova, Z. PorubaV Cutting forces during turning with variable depth of cut on cold work tool steel HRC 23 (Dec 2015 ) 19) Hiren Gajera1, Dr. Komal Dave An Investigation of Forces, Surface Roughnes and Chip Morphology of En 19 Material during Turning (International Journal of Innovative Research in Science, Engineering and Technology) (Vol. 4, Issue 10, Oct 2015) pp Author s details Department of Mechanical Engineering, Chouksey Engineering College Bilaspur, ID: 1 senanshul02@gmail.com, 2 shaileshdewangan123@gmail.com Copy for Cite this Article- Anshul Sen, Dr. Shailesh Dewangan Experimental Investigation of Machining Parameters In Turning Operation Using Taguchi Analysis, International Journal of Science, Engineering and Technology, Volume 4 Issue 6: 2016, pp