30120140505011

175 views

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
175
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

30120140505011

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 108 EXPERIMENTAL INVESTIGATION AND PREDICTION OF SURFACE ROUGHNESS IN SURFACE GRINDING OPERATION USING FACTORIAL METHOD AND REGRESSION ANALYSIS Vishal Francis1 , AbhishekKhalkho2 , JagdeepTirkey3 , Rohit Silas Tigga4 , NeelamAnmolTirkey5 1 Assistant professor, Dept of Mechanical Engineering, SSET SHIATS, Allahabad-211007, Uttar Pradesh, INDIA 2-5 U.G. students, Dept of Mechanical Engineering, SSET SHIATS, Allahabad-211007, Uttar Pradesh, INDIA ABSTRACT This study deals with the investigation and prediction of the surface roughness in surface grinding operation. The experiments have been conducted using factorial design on a surface grinding machine with silicon oxide as an abrasive wheel. Feed and depth of cut were varied to observe their effect on surface roughness. Analysis of variance was used to find the significant factor and regression equation was developed to predict the surface roughness. Three different materials were used in this investigation (cast iron, mild steel and stainless steel). Feed rate was found to be the most significant factor in case of cast iron and for mild steel and stainless steel none of the factor was found be significant. Keyword: Surface Roughness, Surface Grinding, Regression Analysis, Factorial Method. INTRODUCTION Surface roughness often shortened to roughness, is a measure of the texture of a surface. It is quantified by the vertical deviations of a real surface from its ideal form. If these deviations are large, the surface is rough; if they are small the surface is smooth. Surface grinding is the most common of the grinding operations. It is a finishing process that uses a rotating abrasive wheel to smooth the flat surface of metallic or non-metallic materials to give them a more refined look or to INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2014): 7.5377 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 109 attain a desired surface for a functional purpose. It is an expendable wheel that is composed of an abrasive compound used for various grinding (abrasive cutting) and abrasive machining operations. In the experiment, common manufacturing metals i.e. Cast Iron, Mild Steel and Stainless Steel were used. Cast Iron is used in track wheel, automotive crankshaft, bearing surface etc. Mild Steel is used in Armour pipes, magnets etc. Stainless Steel is used in architecture, bridges etc. therefore it is worthwhile to study surface roughness property of these materials for surface grinding operation. METHODOLOGY Factorial design is an important method to determine the effects of multiple variables on a response. Factorial design can reduce the number of experiments one has to perform by studying multiple factors simultaneously. Additionally, it can be used to find both main effects (from each independent factor) and interaction effects (when both factors must be used to explain the outcome). Factorial design is best used for a small number of variables with few states (1 to 3). Regression analysis is a statistical process for estimating the relationships among variables. Regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. MATERIALS AND METHOD The material used for the experiments were cast iron, mild steel and stainless steel. Dimensions and chemical composition for each material are given below:- Cast iron: Length-80mm and Width-55mm and thickness-50mm. And its chemical composition iron- 3%, carbon-5.55, silicon alloy-1-3%, sulphur-0.5, magnesium-0.5%, and potassium-0.55. Mild steel: Length-65mm and Width-37mm and thickness-5mm. And its chemical composition Carbon- 0.16-0.18%, Silicon- 0.40%max, Manganese- 0.70-0.90%. Sulphur-0.040%max, Phosphorus- 0.040%max. Stainless steel: Length-67mm and Width-50mm and thickness-10mm. And its chemical composition carbon- 0.12%, Manganese- 7.5-10%, Silicone-0.9%, Chromium-14-16%, Nickel-0.5 -2.0%, Molybdenum-0.2%, Phosphorus-0.06%. Nine experimental runs were carried out for all the three material in student’s workshop of SHIATS Allahabad (U.P.) INDIA. Correspondingly surface roughness was measured for each experiment. The two process parameters, viz. depth of cut and feed rate were varied and spindle speed was kept constant. Table 1: Process parameters and their level Process parameters Level 1 Level 2 Level 3 Depth of cut(mm) 0.1 0.3 0.5 Feed(mm/rev) 30 40 50
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 110 RESULTS AND DISCUSSION Table 2 shows the values of the responses obtained from the experimental runs for all the three materials. Table 2: Experimental runs EXP NO FEED(mm/mi n) DOC(mm) Surface roughness (µm) (cast iron) Surface roughness (µm) (mild steel) Surface roughness (µm) (stainless steel) 1 30 0 .1 12.5 10 10 2 30 0.3 12.5 2.5 2.5 3 30 0.5 7.5 1.25 2.5 4 40 0.1 7.5 7.5 2.5 5 40 0.3 2.5 1.25 5 6 40 0.5 2.5 1.25 1.25 7 50 0.1 5 5 2.5 8 50 0.3 5 10 2.5 9 50 0.5 1.25 2.5 2.5 (1) Effect of variation of process parameters on response Figure1 (a, b &c) shows Pareto charts for all the three materials demonstrating the effect of parameters on surface roughness. It is clear from the chart that feed rate crosses the reference line in case of cast iron (fig.1 a) i.e. it is potentially important factor. Contour plots for surface roughness vs. feed rate and depth of cut are shown in figure 2. Fig.1 (a): Pareto chart for Cast iron Fig.1 (b): Pareto chart for Mild steel
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 111 Fig.1 (c): Pareto chart for Stainless steel Figure 2 (a) shows that best surface roughness is obtained as we increase feed rate from 30 to 50 (mm/min) and on simultaneously increasing depth of cut from 0.1 mm to 0.5 mm in case of cast iron. For mild steel the best surface roughness is obtained as we keep depth of cut between 0.3 mm to 0.5 mm and feed rate should be kept between 30 to 45 mm/min, on further increasing the feed surface roughness deteriorates. In case of stainless steel there are three regions where we can get best surface roughness; first combination is when depth of cut is 0.1 mm and feed rate is kept around 45 mm/min, second when feed rate is 30 mm/min and depth of cut is around 0.3 mm and thirdly when depth of cut is kept 0.5 mm and feed rate is kept between 35 to 45 mm/min. Figure 2 (a): Contour plots for Cast iron Figure 2 (b): Contour plots for Mild steel
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 112 Figure 2 (c): Contour plots for Stainless steel (2) Regression Analysis Equation 1 shows the regression equation obtained for response (surface roughness) in terms of feed and depth of cut for Cast iron, similarly for Mild steel and Stainless steel regression equations are shown in equation 2 and 3 respectively. Surface roughness (microns) = 23.9 - 0.354 feed (mm/min) - 11.5 Depth of cut (mm) [1] Surface roughness (microns) = 6.46 + 0.062 feed (mm/min) - 14.6 depth of cut (mm) [2] Surface roughness (microns) = 10.7 - 0.125 feed (mm/min) - 7.29 depth of cut (mm) [3] Time series graph for all the three materials were plotted showing the response values obtained from experiments and regression equation. Figure 3 (a, b & c) shows times series plot for Cast iron, Mild steel and Stainless steel respectively. The predicted values seems to be in close agreement with the experimental values. Figure 3 (a): Time series plot for Cast iron Figure 3 (b): Time series plot for Mild steel
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 113 Figure 3 (c): Time series plot for Stainless steel CONCLUSION The study deals with the investigation of effect of process parameters on surface roughness, factorial method and regression analysis were used for this study. From the analysis of result, following conclusion can be drawn: • Most important parameter for surface roughness was found to be feed rate in case of cast iron. • Predicted values are in close agreement with the experimental values therefore the developed model can be adequately used for prediction of surface roughness in surface grinding operation. REFERENCE [1]. Rama Rao. S and padmanabhan. G (2012)” Application of taguchi methods and ANOVA for metal removal rate in electrochemical machine of A1/5% sic composite”. International journal of engineering research ad application vol 2, issue 3, pp. 192-197. [2]. J.Pradeep Kumar and P.Packiaraj (2012), “Effect of drilling parameters on surface roughness. Tool wear, Material removal Rate and hole diameter error in drilling of OHNS”, International Journal of Advanced Engineering Research and Studies. Vol. 1/ Issue III/April- June, 2012/150-154. [3]. K. Krishnamurthy and J.Venkatesh (2013), “Assessment of surface roughness and material removal rate on machining of TIB2 reinforced Aluminum 6063 composites. A Taguchi’s approach”. International Journal of Scientific and Research Publications. Vol. 3, Issue 1. [4]. Gunwant D.Shelake, Harshal K. chavan, Prof. R. R. Deshmukh and Dr. S. D. Deshmukh, “Model for Prediction of Temperature Distribution in Workpiece for Surface Grinding using FEA”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp. 207 - 213, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [5]. A. Hemantha Kumar, Krishnaiah.G and V.Diwakar Reddy, “Ann Based Prediction of Surface Roughness in Turning”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 162 - 170, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 108-114 © IAEME 114 [6]. Karin Kandananond (2009), “Characterization of FDB Sleeve Surface Roughness Using the Taguchi Approach”, European Journal of Scientific Research ISSN 1450- 216X Vol.33 No.2, pp.330-337 © EuroJoumals Publishing, Inc. [7]. Tejinder Pal Singh, Jagtar Singh, Jatinder Madan and Gurmeet Kaur, “Effects of Cutting Tool Parameters on Surface Roughness”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 1, Issue 1, 2010, pp. 182 - 189, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [8] Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar, “Application of Taguchi Method and Anova in Optimization of Cutting Parameters for Material Removal Rate and Surface Roughness in Turning Operation”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 47 - 53, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [9]. Puertas. I. Arbizu, C.J. Luis Prez (2003), “Surface roughness prediction by factorial design of experiments in turning processes”, Journal of Materials Processing Technology, 143- 144 390-396. [10]. B. S. Raghuwanshi (2009), A course in Workshop Technology Vol. II (Machine Tools). Dhanpat Rai & Company Pvt. Ltd.

×