INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 6340 (Print) ISSN 0976 6359 (Online) Volume 3, Issue 2, May-August (2012), pp. 67-72 IAEME: www.iaeme.com/ijmet.html Journal Impact Factor (2011): 1.2083 (Calculated by GISI) www.jifactor.com IJMET I A E M E OPTIMIZATION OF PROCESS PARAMETERS FOR 316L STAINLESS STEEL USING TAGUCHI METHOD AND ANOVA U. D. Gulhane*, A. B. Dixit, P. V. Bane, G. S. Salvi Department of Mechanical Engineering, Finolex Academy of Management and Technology, Ratnagiri, Maharashtra 415612, India *Corresponding author- Asst. Professor, Dept. of Mechanical Engineering, Finolex Academy of Management and Technology, P-60/61, MIDC, Mirjole Block, RATNAGIRI- (M.S.) 415639, India, Tel.: +91-9226797252, Fax: +91-02352228436, E-mail ID: umesh_gulhane@yahoo.com ABSTRACT This paper investigates the parameters affecting the roughness of surfaces produced in the turning process for the material 316L Stainless Steel. Design of experiments was conducted for the analysis of the influence of the turning parameters such as cutting speed, feed rate and depth of cut on the surface roughness. The results of the machining experiments for 316LSS were used to characterise the main factors affecting surface roughness by the Analysis of Variance (ANOVA) method. The feed rate was found to be the most significant parameter influencing the surface roughness in the turning process. Keywords: surface roughness, DOE, ANOVA, 316L SS INTRODUCTION Now-a-days, due to the increasing demand of higher precision components for its functional aspect, surface roughness of a machined part plays an important role in the modern manufacturing process. To achieve the desired surface finish, a good predictive model is required for stable machining. Generally, these models have a complex relationship between surface roughness and operational parameters [1]. Typically, selected cutting operations have limited capability of attaining the required surface roughness. However, it is necessary to determine optimal cutting parameters in order to achieve minimal expenses or minimal cost/production time. Researchers have applied different methods for prediction of optimal cutting parameters. Taguchi s parameter design offers a systematic approach for optimization of various parameters with regard to performance, quality and cost. Further, design optimization for finish was carried out and signal-to-noise (S/N) ratio and analysis of 67
variance (ANOVA) were employed using experimental results to confirm effectiveness of this approach. Gopalsami et. al. studied experimentally the surface roughness of machined hardened steels AISI 4140(63HRC) with Al 2 O 3 +TiCN mixed ceramic tool for turning process. By using ANOVA and Taguchi method they concluded that cutting speed is significantly contributing towards the finish[2]. Paulo Davim and Figueira were obtained machinability evaluation of cold work tool steel by hard turning process using S/N ratio and ANOVA by ceramic cutting tools and observed that cutting speed is most influencing parameter[3]. In this paper, L 9 orthogonal array is employed to analyze experimental results of machining obtained from 9 experiments by varying three process parameters viz cutting speed (A), depth of cut (B) and feed rate(c). ANOVA has been employed and compared with Taguchi method. METHODOLOGY DOE techniques enable designers to determine simultaneously the individuals and interactive effects of many factors that could affect the output results in any design. DOE also provides a full insight of interaction between design elements; it helps turn any standard design into robust one. Simply DOE helps to pin point the sensitive parts and sensitive areas in designs that cause problems in response variable. We are then able to fix these problems and produce vigorous results. Taguchi envisaged new method of conducting the design of experiments which are based on well defined guidelines. This method uses a special set of arrays called orthogonal array. This standard array stipulates the way of conducting the minimum number of experiment which could give the full information of all the factors that affect the performance parameter. While there are many standard orthogonal arrays available, each of arrays is meant for a specific number of independent design variables and levels. ANOVA can be useful for determining influence of any given input parameter for a series of experimental results by design of experiments for machining process and it can be used to interpret experimental data. While performing ANOVA degrees of freedom should also be considered together with each sum of squares. In ANOVA studies a certain test error, error variance determination is very important. Obtained data are used to estimate F value of Fisher Test (F-test). Variation observed (total) in an experimental attributed to each significant factor or interaction is reflected in percent contribution (P), which shows relative power of factor or interaction to reduce variation [4]. EXPERIMENTATION The specimens used for experimentation were of 316LSS. Table 1 shows nominal and actual composition of 316L SS used for the study. It was subjected to turning operation which was carried out on Lathe Machine (Kirloskar Turn master 35). As 316 LSS is a hard material, carbide tool was selected.carbide leaves a better finish on the part and allows faster machining. Carbide tools can also withstand higher temperatures than standard high speed steel tools. Cylindrical specimen of 12 mm diameter was safely turned in the four jaw chuck by supporting the free end of the 68
work. As the work piece was quite long it was needed to face and centre drill the free end supported by the tailstock. 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. Table 1 Composition of 316LSS SAE Designation % Cr % Ni % C % Mn % Si % P % S Other %Mo 316L SS (Nominal) 16 18 10 14 0.03 2.0 1.0 0.045 0.03 2.0 3.0 316L SS (Actual) 17.34 10.69 0.024 1.748 0.471 0.034 0.018 2.08 Work piece was inserted in the 4-jaw chuck and was tightened in the jaws until they just started to grip the work piece. The work piece was then rotated to ensure that it is seated evenly and to dislodge any chips or grit on the surface that might keep it from seating evenly. Work piece was kept as parallel as possible with the center line of the lathe. The selected tool was tightly clamped in the tool holder. The angle of the tool holder was properly adjusted so that the tool remained approximately perpendicular to the side of the work piece. The turning was carried out on 9 different sections of the work piece. For each section, all the three parameters, viz. cutting speed, depth of cut and feed rate, were varied as shown in Table 2. The surface roughness of each specimen was tested on the surface roughness tester (Mitutoyo Roughness tester SJ-400). The average values of Ra were obtained from three readings for each specimen. Table 2: Machining parameters and levels: Machining Parameters Cutting speed (m/min) Depth of cut (mm) Feed rate (mm/rev) Level 1 Level 2 Level 3 1.696 6.78 10.55 0.06 0.16 0.3 0.06 0.125 0.3 RESULTS AND DISCUSSION Table 3 shows experimental design matrix and surface roughness value (Ra) for 316L SS. S/N ratio is calculated using Lower the better characteristics and shown in Table 3. 69
Expt. No. Coded Values Table 3 Experimental design matrix and response variable Turning Parameters Actual Values Cutting speed (mm/m in) Depth of cut (mm) Feed rate (mm/ rev) Surface Roughness Ra (µm) 316LSS Ra 1 Ra 2 Ra 3 (Ra)avg S/N ratio A B C A B C 1 1 1 1 45 0.060 0.06 2.370 1.970 2.280 2.207-6.8760 2 1 2 2 45 0.160 0.125 2.890 2.600 2.620 2.703-8.6369 3 1 3 3 45 0.300 0.3 5.360 5.860 5.820 5.680-15.087 4 2 1 2 180 0.060 0.125 2.850 2.720 2.640 2.737-8.7455 5 2 2 3 180 0.160 0.3 7.520 7.870 7.960 7.783-17.822 6 2 3 1 180 0.300 0.06 1.420 1.500 1.500 1.473-3.3641 7 3 1 3 280 0.060 0.3 8.920 9.500 9.460 9.293-19.363 8 3 2 1 280 0.160 0.06 0.990 1.030 0.910 0.977 0.2021 9 3 3 2 280 0.300 0.125 2.260 2.400 2.290 2.317-7.2985 Responses for Signal to Noise Ratios of Smaller is better characteristics is shown in Table 4. 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 cutting speed where as there is significant variation for change in feed rate. Table 4 Response Table for Signal to Noise Ratios Smaller is better Level A B C 1-10.2-11.662-3.346 2-9.977-8.753-8.227 3-8.82-8.583-17.424 Delta 1.38 3.078 14.078 Rank 3 2 1 70
Main Effects Plot for SN ratios Data Means A B -5-10 Mean of SN ratios -15-5 1 2 C 3 1 2 3-10 -15 1 Signal-to-noise: Smaller is better 2 3 Fig. 1 Effect of cutting speed (A), Depth of cut (B) and Feed rate (C) on surface finish 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 (cutting speed(a), Depth of Cut (B), Feed rate (C). Table 5 Analysis of Variance for S/N ratios Source DF Seq SS Adj SS Adj MS F P A 2 3.294 3.294 1.647 0.23 0.815 B 2 17.967 17.967 8.984 1.24 0.446 C 2 306.615 306.615 153.308 21.19 0.045 Resi.error 2 14.469 14.469 7.235 Total 8 342.346 Table 5 shows Analysis of variance for S/N ratio. F value (21.19) of parameter indicates that feed rate is significantly contributing towards machining performance. F value (0.23) of parameter indicates that cutting speed is contributing less towards surface finish. It can be observed rough surface from photographs taken for the specimen No. 7 (cutting speed, 10.55 m/min; depth of cut, 0.06 mm; feed, 0.3 mm/rev.) and smooth surface for the specimen No. 8 (cutting speed, 10.55 m/min; depth of cut, 0.16 mm; feed, 0.06 mm/rev.) as shown in figure 2. 71
CONCLUSION Fig. 2 Specimen photograph for rough and smooth surface Taguchi method of experimental design has been applied for optimizing multi response process parameter for turning 316LSS are optimized with L9 orthogonal array. Results obtained from Taguchi method closely matches with ANOVA. Best parameters found for finish machining are: cutting speed, 10.55 m/min; depth of cut, 0.16 mm; feed, 0.06 mm/rev. The parameters found for rough machining are cutting speed, 10.55 m/min; depth of cut, 0.06 mm; feed, 0.3 mm/rev. Feed is most influencing parameters corresponding to the quality characteristics of surface roughness. REFERENCES 1. C. Natarajan, S. Muthu and P. Karuppuswamy, Investigation of cutting parameters of surface roughness for a non-ferrous material using artificial neural network in CNC turning Journal of Mechanical Engineering Research Vol. 3(1), pp. 1-14, January 2011. 2. Bala Murugan Gopalsamy, Biswanath Mondal and Sukamal Ghosh, taguchi method and Anova : An Approach for process parameters optimisation of hard machining while machining hardened steel Journal of Scientific and Industrial research, vol.68, Aug 2009, pp.686-695. 3. Paulo Davim J and Figueira L, machinability evaluation of hard turning of cold worked tool steel with ceramic tools with statistical techniques, Mater Des.28 (2007) 1186-1191. 4. Julian J. Faraway, Practical Regression and ANOVA using R, July 2002, pp 168-200. 72