6995(Print), ISSN (Online) Volume 4, Issue 1, January- April (2013), IAEME TECHNOLOGY (IJDMT)

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1 International INTERNATIONAL Journal of Design JOURNAL and Manufacturing OF DESIGN Technology AND MANUFACTURING (IJDMT), ISSN 0976 TECHNOLOGY (IJDMT) ISSN (Print) ISSN (Online) Volume 4, Issue 1, January- April (2013), pp IAEME: Journal Impact Factor (2013): (Calculated by GISI) IJDMT I A E M E INVESTIGATION OF TURNING PROCESS TO IMPROVE PRODUCTIVITY (MRR) FOR BETTER SURFACE FINISH OF AL T6 USING DOE U. D. Gulhane*, S. P. Ayare, V.S.Chandorkar, M.M. Jadhav Department of Mechanical Engineering, Finolex Academy of Management and Technology, Ratnagiri, Maharashtra , India *Corresponding author- Associate Professor, Dept. of Mechanical Engineering, Finolex Academy of Management and Technology, P-60/61, MIDC, Mirjole Block, RATNAGIRI- (M.S.) , India ABSTRACT Higher material removal rate with better surface finish is one of the prime requirements of today s industry. The present paper investigate the effects of cutting parameters like spindle speed, feed and depth of cut on surface finish and material removal rate of Aluminium 7075-T6. Taguchi methodology has been applied to optimize cutting parameters. Feed rate is the most significant factor influencing surface finish whereas material removal rate is significantly affected by cutting speed. For highest MRR with better surface finish. Cutting speed ( m/min),feed rate ( mm/rev.) and depth of cut (0.5 mm) are cutting parameters for higher MRR and optimum surface roughness. Keywords: Surface roughness, MRR, DOE, ANOVA, Al-7075 T6 INTRODUCTION Surface finish is the method of measuring the quality of product and is an important parameter in machining process. It is one of the prime requirements of customers for machined parts. Productivity is also necessary to fulfill the customers demand. For this purpose quality of product and productivity should be high. In addition to surface finish quality, the material removal rate (MRR) is also an important characteristic in turning operation and high MRR is always desirable. Taguchi has proposed off line for quality improvement in place of an attempt to inspect quality in the product on the product line. He observed that no amount of an inspection can put quality back into the product but it merely treats a symptom. Taguchi has 59

2 recommended three stages to achieve the desirable product quality by design Viz. System design, Parameter design and Tolerate design system which help to identify the working levels of the parameter. The optimal condition is selected so that influence of noise factors causes minimum variation to study performance. The orthogonal arrays, variance and signal to noise analysis are essential tool of parameter design. LITERATURE REVIEW John et al. (2001) demonstrated a systematic procedure of using Taguchi parameter design to optimize surface roughness performance with particular combination of cutting parameters in end milling operation. Kopac et al. (2002) described the machining parameters influence and levels that provide sufficient robustness of machining process towards the achievement of the desired surface roughness for cold pre-formed steel workpieces in fine turning. Ihsan Korkut et al. (2004) carried turning tests to determine optimum machining parameters for machining of austenitic stainless steel. Ciftci (2006) investigated the machining characteristics of austenitic stainless steel (AISI 304 and AISI 316) using coated carbide tools. Zhang et al. (2007) have used Taguchi method for surface finish optimization in end milling of Aluminum blocks. G. Akhyar et al. (2008) optimized cutting parameters in turning Ti-6% Al-4% V extra low interstitial with coated and uncoated cemented carbide tools under dry cutting condition. Anirban Bhattacharya et al. (2009) estimated the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel. Saeed Zare Chayoshi & Mehdi Taidari (2009) developed a surface roughness model in hard turning operation of AISI 4140 using CBN cutting tool.adeel H.Suhail et al.(2010) conducted experimental study to optimize the cutting parameters using two performance measures,work piece surface temperature and surface roughness.d.philip Selvaraj and P.Chandramohan (2010) concentrated with dry turning of AISI 304 Austenitic Stainless Steel Nikolaoset al.(2010) developed a surface roughness model for turning of femoral heads from AISI 316L stainless steel. MATERIALS AND METHODS The experimental investigation presented here was carried out on Crown lathe machine. The work piece material used for present work was AL7075-T6. The specification used for experimentation was of Al 7075-T6. Table 1 shows Chemical composition of Al 7075-T6 used for study. Chemical composition Limits Weight % Al Si Fe Cu Mn Mg Cr Zn Ti Each Tota l Minimum Maximum Rem Table1 Chemical composition of AL7075-T6 60

3 It was subjected to turning operation, which was carried out on Lathe machine (Crown lathe machine). As Al 7075-T6 is soft material HSS tool was selected. HSS leaves a better finish on the part and allows faster machining. HSS tool can withstand moderate temperature. Cylindrical specimen of 15mm diameter was safely turned in three jaw chuck by supporting the free end of work. As the work piece was quite long it was needed to face and centre drill the free end supported by the tail stock. Without such support, the force of the tool on the work piece would cause it to bend away from the tool, producing a strangely shaped result In this experiment,in order to investigate the surface finish of the machined workpiece and material removal rate,during cutting of the AL 7075-T6,HSS tool was used.a view of the cutting zone arrangement is shown in Fig.1 The surface roughness of the finished work surface was measured with the help of a surface roughness tester. The material,characteristics of tool and detail of experimental design set-up are listed in Table 2 and conditions are given in Tables 3 Fig. 1 View of cutting zone (Actual arrangement and schematic arrangement) For MRR machining time for each sample has been calculated accordingly. After machining, weight of each machined parts have been again measured precisely with the help of digital balance meter. RESULTS AND DISCUSSION Table 2: Machining parameters and level Machining Parameters Level 1 Level 2 Level 3 Cutting speed (m/min) of cut (mm) Feed rate (mm/rev) Table 3 shows experimental design matrix and surface roughness value (Ra) and MRR for Al 7075-T6. S/N ratio for surface roughness is calculated using lower the better characteristics and S/N ratio for MRR is calculated using larger the better characteristic shown in Table 3.The S/N ratio is calculated using equation (1) and equation (2) 61

4 10 log (1) 10 log (2) Table 3-Experimental design matrix and response variable Expt. No. Turning Parameters Surface Roughness Ra S/N MRR S/N of cut Feed rate Cutting speed Ra1 Ra2 Ra avg ratio Wt base ratio Responses for Signal to Noise Ratios of Smaller is better characteristics for surface roughness is shown in Table 4. and Responses for Signal to Noise Ratios of larger is better characteristics for MRR is shown in Table 5. Significance of machining parameters (difference between max. and min. values) indicates that feed is significantly contributing towards the machining performance as difference gives higher values. Plot for S/N ratio shown in Figure 1 explains that there is less variation for change in depth of cut where as there is significant variation for change in feed rate. Significance of machining parameters (difference between max. and min. values) indicates that cutting speed is significantly contributing towards the MRR as difference gives higher values. Plot for S/N ratio shown in Figure2 explains that there is less variation for change in feed where as there is significant variation for change in cutting speed. 62

5 Table 4- Response Table for Signal to Noise Ratios Smaller is better Level of cut Feed Speed Delta Rank Table 5- Response Table for Signal to Noise ratios larger is better Level of cut Feed Speed Delta Rank Main Effects Plot for SN ratios Data Means feed Mean of SN ratios speed S ignal-to-noise: S maller is better Fig. 2 Effect of of cut, Feed rate and speed on surface finish Table 6-Analysis of Variance for S/N ratios for surface roughness Source DF Seq SS Adj SS Adj MS F P of cut Feed Speed Residual error Total

6 Main Effects Plot for SN ratios Data Means 69 Feed Mean of SN ratios Speed S ignal-to-noise: Larger is better Fig. 3 Effect of of cut, Feed rate and speed on MRR Taguchi method cannot judge and determine effect of individual parameters on entire process while percentage contribution of individual parameters can be well determined using ANOVA. MINITAB software of ANOVA module was employed to investigate effect of process parameters ( of cut, Feed rate and speed) Table 7-Analysis of Variance for S/N ratios for MRR Source DF Seq SS Adj SS Adj MS F P of cut Feed Speed Residual error Total Theory suggests that surface roughness is function of feed rate. In practice it is more like directly related to feed rate. This can be due to flattening of ridges due to side flow or tool work relative vibrations when feed rate is lower the roughness becomes independent of feed rate. 64

7 Table 6 shows Analysis of variance for S/N ratio. F value (3.03) for S/N ratio parameter indicates that feed rate is significantly contributing towards machining performance. F value (0.03) for S/N ratio of parameter indicates that depth of cut is contributing less towards surface finish. It can be observed rough surface from surface texture for the specimen No.5 (cutting speed m/min; depth of cut 1 mm; feed mm/rev.) and smooth surface for the specimen No.4 (cutting speed m/min; depth of cut 1 mm; feed mm/rev.) MRR S/F ROUGHNESS MRR Power (MRR) S/F ROUGHNESS Power (S/F ROUGHNESS) Power (S/F ROUGHNESS) Fig 4 Graph of surface roughness and MRR vs Expt. No. Fig. 5 Specimen with higher MRR and optimum surface roughness Table 7 shows analysis of variance S/N ratio for MRR. F value ( ) for S/N ratio parameter indicates that cutting speed is significantly contributing towards MRR. F value (828.31) for S/N ratio of parameter indicates that depth of cut is contributing less towards MRR. It was observed that maximum MRR is obtained at the cutting speed (15.102m/min), feed rate (0.3345mm/rev) and depth of cut (1.5mm) 65

8 The power regression type is used to calculate the trend of each graph. By observing the graph of surface roughness vs experiment found that the optimum values of surface roughness lies in between 0.6 to 1.2 µm and for MRR lies in between 1000 to 2000 mm 3 /min. CONCLUSION Following are the conclusions drawn based on the test conducted on Al 7075-T6 alloy during Turning operation with HSS 1. From response Table for Signal to Noise ratios for surface roughness, based on the ranking it can be concluded that Feed has a greater influence on the Surface Roughness followed by Speed. of Cut had least influence on Surface Roughness. 2. From response Table for Signal to Noise ratios for MRR, based on the ranking it can be concluded that cutting speed has a greater influence on the Surface MRR followed by feed rate. of Cut had least influence on MRR 3. Cutting speed ( m/min), feed rate ( mm/rev.) and depth of cut (0.5 mm) are cutting parameters for higher MRR with better surface finish. ACKNOWLEDGEMENT Quality control department of Adler Mediequip PVT.LTD, Ratnagiri are gratefully acknowledged. REFERENCES 1 Anirban Bhattacharya, Santanu Das, P. Majumdar (2009), AjayBatish, Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel during Taguchi design and ANOVA, Prod. Eng. Res. Devel, vol 3, pp Adeel H. Suhail, N. Ismail, S.V.Wong and N. A. Abdul Jalil (2010), Optimisation of cutting parameters based on surface roughness and assistance of workpiece surface temperature in turning process, American journal of engineering and applied sciences, vol.3 (1), pp Ciftci I. (2006), Machining of austenitic stainless steels using cvd multilayer coated cementet carbide tools, Tribogy, Internation, vol 39 (6), pp D. Philip Selvaraj and P. Chandermohan (2010), Optimisation of surface roughnes AISI 304 Austenti stainless steel in dry operation using Taguchi method, Journal of engineering science and technology, vol 5, pp G. Akhyar, C. H. Cheharon and J. A. Ghani (2008), Application of Taguchi method in the optimization of turning parameters for surface roughness, International Journal of Science Engineering And Technology, vol. 1 (3), pp J. 6 Kopac J.,M. Bahor and M. Sokovic (2002), Optical machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel workpiecs, Machine Tool Manufacturing, vol 42, pp Korkut I., Kasap M. Ciftci I. and Seker U (2004), Determination of optimum cutting parameters during machining of AISI 304, Austenitic stainless steel, Materials and Design, vol. 25(4), pp

9 8 Gulhane U. D. A. B. Dixit, P. V. Bane and G. S. Salvi, Optimization of process parameters for 316L stainless steel by using Taguchi method and ANOVA, International Journal of Mechanical Engineering and Technology (IJMET), Volume 3, Issue 2, PP , ISSN Print : , ISSN Online: M. Kaladhar, K. V. Subbaiah, Ch. Srinivasa Rao and K. Narayana Rao (2011), Application of Taguchi approach and utility concept in solving the multi-objective problem when turning AISI 202 Austenitic stainless, Journal of Engineering Science And Technology, vol 4 (1), pp Nikolaos I, Galanis and Dimitrios E. Manolakos (2010), Surface roughness prediction in turning of femoral head, Intenational journal of advmanufacturing technology, doi 1007/s 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, p Zhang J. Z. Chen J.C and Kirby E.D (2007), Surface roughness optimization in an endmilling operation using the Taguchi design method, Journal Of Material Processing Technology, 184(1-3), PP Gulhane U. D., Mishra S. B. and Mishra P. K., Enhancement of surface roughness of 316 L Stainless Steel and Ti-6Al-4V using Low Plasticity Burnishing: DOE Approach International Journal of Mechanical Engineering and Technology (IJMET), Volume 3, Issue 1, pp , ISSN Print : , ISSN Online: P.B.Wagh, R.R.Deshmukh And S.D.Deshmukh, Process Parameters Optimization for Surface Roughness in EDM for AISI D2 Steel by Response Surface Methodology, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 1, 2013, pp , ISSN Print : , ISSN Online: U.D.Gulhane, M.P.Bhagwat, M.S.Chavan, S.A.Dhatkar and S.U.Mayekar, Investigating the Effect of Machining Parameters on Surface Roughness of 6061 Aluminium Alloy in End Milling, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2013, pp , ISSN Print : , ISSN Online: