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Experimental Investigation and Analysis of Effect of Process Parameters on Surface Roughness of AISI 4340 during MQL Turning with Nano Fluid


Affiliations
1 Department of Mechanical Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India
2 Department of Mechanical Engineering, Sanjay Ghodawat Group of Institution, Kolhapur, Maharashtra, India
     

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The selection of cooling system and effective optimization of machining cutting parameters affects cost and production time of quality of machined work piece material. This research work represents an investigation on response parameters such as surface roughness and metal removal rate during MQL turning of AISI 4340 with nano fluid along with comparative analysis of different cooling systems. Three values of feed rate and depth of cut respectively were chosen to study the effect on surface roughness. As per Taguchi design L9 orthogonal array design matrix has been selected for conducting of experiments. The optimal conditions are obtained from Grey Relational Analysis (GRA) as Feed (0.04 mm/rev.) and Depth of cut (1.5 mm). The Signal to Noise ratio plot for GRA shows similar optimum condition therefore the results achieved from ANOVA are closely matching to the results of GRA. Improvement in grey relational grade is near about 1.24%. From the comparative result analysis, it was observed that Minimum Quantity Lubrication (MQL1) with nano fluid (MWCNT) showed lowest surface roughness compared to MQL2, dry and flood condition. Also it is found that the percentage error is below ±5%. For MQL1 at optimum condition (Feed rate 0.04 mm/rev. and Depth of cut 1.5 mm) the obtained surface roughness (Ra = 1.01μm). The findings of this study show that MQL with nano fluid can substitute the flood lubrication for better surface finish and performance characteristics can be improved effectively through this approach.

Keywords

MQL, Nano Fluid, GRA, Surface Roughness, Metal Removal Rate.
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  • Narana Rao S., Satyanarayana B. S., (2011), Experimental Estimation of Tool Wear and Cutting Temperatures in MQL using Cutting Fluids with CNT Inclusion, International Journal of Industrial Engineering Science and Technology, ISSN:0975-5462.

  • Sahoo Ashok Kumar, Sahoo Bidyadhar, (2012) Experimental Investigation on Machinability Aspects in Finish Hard Turning of AISI 4340 Steel using Uncoated and Multilayer Coated Carbide Inserts, Measurement 45, pp2153-2165.

  • Young Kug Hwang and Choon Man Lee, (2010), Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments, Journal of Mechanical Science and Technology 24(8), 1669-1677.

  • Suhil Adheil H., Ismail N., (2010), Optimization of Cutting Parameters of Turning Operations by using Geometric Programming, American J. of Engineering and Applied sciences, 3(1) pp102-108.

  • Dhar N. R., Islam S. and Kamruzzaman M., (2007), Effect of Minimum Quantity Lubrication(MQL) on Tool Wear, Surface Roughness and Dimensional Deviation in Turning AISI-4340 Steel, G. U. Journal of Science 20(2), pp23-32.

  • Lohar D. V., Nanavte C. R., (2013), Performance Evaluation of Minimum Quantity Lubrication (MQL) Using CBN Tool during Hard Turning of AISI 4340 and its Comparison with Dry and Wet Turning, International Journal of Industrial Engineering and Management Science, Vol. 3, No. 3.

  • Tasdelen B., Thordenberg H., Olofsson D., (2008), An Experimental Investigation on Contact Length During MQL Machining, Journal of Material Processing Technology, pp221-231.

  • Shen Bin, (2008), Minimum Quantity Lubrication Grinding Using Nano fluids, A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy (Mechanical Engineering) in the University of Michigan.

  • Prabhu S., Vinayagam B. K., (2011), Fractal Dimensional Surface Analysis of AISI D2 Tool Steel Material with Nano fluids in Grinding Process Using Atomic Force Microscopy, 466 / Vol. XXXIII, No. 4.

  • Berger P. D. and R. E. Maurer, (2001), Experimental Design with Applications in Management, Engineering and the Sciences, 1st edition, Duxbury Press, USA, ISBN:10: 0534358225, pp:496

  • Patole P. B. and Kulkarni V. V., (2017), Experimental Investigation and Analysis of Relationship between Surface Roughness and Cutting Force during MQL Turning of AISI 4340 with Nano Fluid, Manufacturing Technology Today (CMTI), Vol. 16 No.1, pp 1-9.

  • Patole P. B. and Kulkarni V.V., (2017), Experimental investigation and optimization of cutting parameters with multi response characteristics in MQL turning of AISI 4340 using nano fluid, Cogent Engineering (Taylor and Francis group), 4: 1303956.

  • Patole P. B. and Kulkarni V.V., (2016), Optimization of Process Parameters based on Surface Roughness and Cutting Force in MQL Turning of AISI 4340 using Nano Fluid, Materials Today Procedings: PMME 2016.

  • Attanasio Gelfi A., Giardini C. and Remino C., (2006) Minimal quantity lubrication in turning: effect on tool wear, International Journal on the Science and Technology of Friction, Lubrication and Wear, 260, pp333-338.

  • Kumar and Sing, (2016), Multi response optimization in wire electrical discharge machining of Inconel x- 750 using Taguchi technique and grey relational analysis, International Journal of Cogent Engineering, 3: 1266123

  • Chang C.L., Tsai C. H., Chen L. Applying grey relational analysis to the decathlon evaluation model, International journal of Computer Internet Manage 2003;11(3):54-62.

  • Ulas Caydas, Ahmet Hascalik., (2008), Use of the Grey Relational Analysis to determine optimum laser cutting parameters with multi-performance characteristics, Elsevier, Optics and Laser Technology 40 pp 987-994.

  • Sing Dilbag and Venkteshewara Rao P. (2007), A surface roughness prediction model for hard turning process, International Journal of Advanced Manufacturing Technology, 32: 1115-1124.

  • Fung C. P., Manufacturing process optimization for wear property of fiber–reinforced polbutylene terephthalate composites with grey relation analysis. Wear 254: 298-306.

  • Pujari Shrinivasa Rao, Koona Ramji and Beela Satyanarayna, (2010), Prediction of Material removal rate for Aluminium BIS-24345 alloyin wire cut EDM, International Journal of Engineering Science and Technology, Vol. 2(12), 7729-7739.

  • Nalbant M. H., Gokkaya and G. Sur, (2007), Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design, 28, pp. 1379-1385.

  • Patel P., Modi. B. S., Shet S and Patel. T. (2015), Experimental Investigation, Modelling and Comparison of kerf width in laser cutting of GFRP. Bonfring International Journal of Industrial Engineering and Management and Science, 5 (2), pp. 55-62.

  • Douglas C. Montgomery, (2001), Design and Analysis of Experiments, John Wiley and Sons, pp1-17.

  • P. J. (1996), Taguchi Techniques for Quality Engineering, New York, McGraw Hill.


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  • Experimental Investigation and Analysis of Effect of Process Parameters on Surface Roughness of AISI 4340 during MQL Turning with Nano Fluid

Abstract Views: 246  |  PDF Views: 2

Authors

P. B. Patole
Department of Mechanical Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India
V. V. Kulkarni
Department of Mechanical Engineering, Sanjay Ghodawat Group of Institution, Kolhapur, Maharashtra, India

Abstract


The selection of cooling system and effective optimization of machining cutting parameters affects cost and production time of quality of machined work piece material. This research work represents an investigation on response parameters such as surface roughness and metal removal rate during MQL turning of AISI 4340 with nano fluid along with comparative analysis of different cooling systems. Three values of feed rate and depth of cut respectively were chosen to study the effect on surface roughness. As per Taguchi design L9 orthogonal array design matrix has been selected for conducting of experiments. The optimal conditions are obtained from Grey Relational Analysis (GRA) as Feed (0.04 mm/rev.) and Depth of cut (1.5 mm). The Signal to Noise ratio plot for GRA shows similar optimum condition therefore the results achieved from ANOVA are closely matching to the results of GRA. Improvement in grey relational grade is near about 1.24%. From the comparative result analysis, it was observed that Minimum Quantity Lubrication (MQL1) with nano fluid (MWCNT) showed lowest surface roughness compared to MQL2, dry and flood condition. Also it is found that the percentage error is below ±5%. For MQL1 at optimum condition (Feed rate 0.04 mm/rev. and Depth of cut 1.5 mm) the obtained surface roughness (Ra = 1.01μm). The findings of this study show that MQL with nano fluid can substitute the flood lubrication for better surface finish and performance characteristics can be improved effectively through this approach.

Keywords


MQL, Nano Fluid, GRA, Surface Roughness, Metal Removal Rate.

References