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Optimization of Tool wear and Surface Roughness in Turning Titanium (Ti-6Al-4V) Alloy; NFMQCF Technique

Sivakoteswararao Katta*, R. Venkatesh**, Suresh Gupta***

* Research Scholar, Department of Mechanical Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India.

** Associate Professor, Department of Mechanical Engineering, RVR&JC College of Engineering, Guntur, Andhra Pradesh, India.

*** Associate Professor, Department of Mechanical Engineering, Bapatla Engineering College, Bapatla, Andhra Pradesh, India.

Periodicity:February - April'2019
DOI : https://doi.org/10.26634/jme.9.2.14812

Abstract

In today's machining applications, nanofluids created a revolution by replacing the various metal cutting fluids used in manufacturing industries, due to its distinct properties such as high thermal conductivity and lubrication. The optimization was done based on the experimentation on surface roughness and tool wear. To get optimized results the technique used was Grey Rational Analysis (GRA), Principle Composite Analysis (PCA), and Response Surface Methodology (RSM) optimization techniques on the turning of Titanium (Ti-Al-4V) alloy with the Nanofluid based Minimum Quantity Cutting Fluid (NFMQCFT) Technique. Here, Graphene nanoparticles are used to mix with the vegetable oil based (Soya Bean) cutting fluid. The experiment has been done by using several machining parameters such as feed rate, cutting speed, depth of cut, etc. An analysis has been made to evaluate the machining parameters for surface roughness values (Ra) and Tool wear based on the actual series of experiments with uncoated carbide tool. The outcomes state that the feed rate has a greater influence on the values of surface roughness as compared to cutting speed. The predicted results are identical to the experimental values. Since this research has multi-objective, these developed models using response surface methodology, grey rational analysis, and principle composite analysis can be used for evaluation of surface roughness and tool wear.

Keywords

Surface Roughness, Tool Wear, NFMQCF Technique, Response Surface Methodology, Grey Relational Analysis, Principle Composite Analysis

How to Cite this Article?

Katta, S., Chaitanya, G., and Shankar, B. R. (2018). Optimization of Tool wear and Surface Roughness in Turning Titanium (Ti-6Al-4V) Alloy; NFMQCF Technique. i-manager’s Journal on Mechanical Engineering, 9(2), 21-34. https://doi.org/10.26634/jme.9.2.14812

References

[1]. Ahmed, S L., & Kumar, M. P. (2016). Optimization of reaming process parameters for titanium Ti-6Al-4V alloy using grey relational analysis. International Mechanical Engineering Congress and Exposition: Advance Manufacturing (pp. V002T02A008-V002T02A008).

[2]. Ananth, V. P., & Vasudevan, (2013). Recent trends in cutting parameters and surface quality in turning operation. Journal of Manufacturing and Industrial Engineering, 4, 56-59.

[3]. Asiltürk, I., Neşeli, S., & Ince, M. A. (2016). Optimisation of parameters affecting sur face roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement, 78, 120-128.

[4]. Chauhan, S. R., & Dass, K. (2012). Optimization of machining parameters in turning of titanium (grade-5) alloy using response surface methodology. Materials and Manufacturing Processes, 27(5), 531-537.

[5]. Debnath, S., Reddy, M. M., & Yi, Q. S. (2016). Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method. Measurement, 78, 111-119.

[6]. D'Mello, G., Pai, P. S., & Puneet, N. P. (2017). Optimization studies in high speed turning of Ti-6Al-4V. Applied Soft Computing, 51, 105-115.

[7]. Dureja, J. S., Gupta, V. K., Sharma, V. S., & Dogra, M. (2009). Design optimization of cutting conditions and analysis of their effect on tool wear and surface roughness during hard turning of AISI-H11 steel with a coated-mixed ceramic tool. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(11), 1441-1453.

[8]. Eriki, A. K., Rao, K. P., & Babu, A. R. (2014). Effects of various parameters on CNMG turning insert in machining Ti-6Al-4V. International Journal of Engineering and Technology Innovations, 1(2), 1-5.

[9]. Garcia, U., & Ribeiro, M. V. (2016). Ti-6Al-4V titanium alloy end milling with minimum quantity of fluid technique use. Materials and Manufacturing Processes, 31(7), 905- 918.

[10]. Gupta, M. K., & Sood, P. K. (2017). Surface roughness measurements in NFMQL assisted turning of titanium alloys: An optimization approach. Friction, 5(2), 155-170.

[11]. Khanna, N., & Davim, J. P. (2015). Design-ofexperiments application in machining titanium alloys for aerospace structural components. Measurement, 61, 280-290.

[12]. Khidhir, B. A., Al-Oqaiel, W., & Kareem, P. (2015). Prediction models by response surface methodology for turning operation. American Journal of Modeling and Optimization, 3(1), 1-6.

[13]. Kim, J. S., Kim, J. W., & Lee, S. W. (2017). Experimental characterization on micro-end milling of titanium alloy using nanofluid minimum quantity lubrication with chilly gas. The International Journal of Advanced Manufacturing Technology, 91(5-8), 2741-2749.

[14]. Kumar, U., & Narang, D. (2013). Optimization of Cutting Parameters in High-Speed Turning by Grey Relational Analysis. International Journal of Engineering Research and Applications, 3(1), 832-839.

[15]. Luo, M., Wang, J., Wu, B., & Zhang, D. (2017). Effects of cutting parameters on tool insert wear in end milling of titanium alloy Ti-6Al-4V. Chinese Journal of Mechanical Engineering, 30(1), 53-59.

[16]. Makadia, A. J., & Nanavati, J. I. (2013). Optimisation of machining parameters for turning operations based on response surface methodology. Measurement, 46(4), 1521-1529.

[17]. Mia, M., Khan, M. A., & Dhar, N. R. (2017). Highpressure coolant on flank and rake surfaces of tool in turning of Ti-6Al-4V: Investigations on surface roughness and tool wear. The International Journal of Advanced Manufacturing Technology, 90(5-8), 1825-1834.

[18]. Narayan, V., & Aswathy, V. G. (2015). Multi-response optimization in turning of titanium alloy using grey relational analysis. International Journal of Innovative Research in Science, Engineering, and Technology, 4(12), 11841-11847. https://doi.org/10.15680/IJIRSET. 2015.0412025

[19]. Ramesh, S., Karunamoorthy, L., & Palanikumar, K. (2008). Surface roughness analysis in machining of titanium alloy. Materials and Manufacturing Processes, 23(2), 174-181.

[20]. Rao, C. J., Rao, D. N., & Srihari, P. (2013). Influence of cutting parameters on cutting force and surface finish in turning operation. Procedia Engineering, 64, 1405-1415.

[21]. Routara, B. C., Bandyopadhyay, A., & Sahoo, P. (2009). Roughness modeling and optimization in CNC end milling using response surface method: Effect of workpiece material variation. The International Journal of Advanced Manufacturing Technology, 40(11-12), 1166-1180.

[22]. Sahu, N. K., Andhare, A. B., & Raju, R. A. (2018). Evaluation of performance of nanofluid using multiwalled carbon nanotubes for machining of Ti–6AL–4V. Machining Science and Technology, 22(3), 476-492.

[23]. Sahu, S., & Choudhury, B. B. (2015). Optimization of surface roughness using taguchi methodology & prediction of tool wear in hard turning tools. Materials Today: Proceedings, 2(4-5), 2615-2623.

[24]. Sargade, V., Nipanikar, S., & Meshram, S. (2016). Analysis of surface roughness and cutting force during turning of Ti6Al4V ELI in dry environment. International Journal of Industrial Engineering Computations, 7(2), 257-266.

[25]. Satyanarayana, K., Gopal, A. V., & Babu, P. B. (2014). Analysis for optimal decisions on turning Ti–6Al–4V with Taguchi–grey method. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 228(1), 152-157.

[26]. Setti, D., Ghosh, S., & Rao, P. V. (2012, October). Application of nano cutting fluid under minimum quantity lubrication (MQL) technique to improve grinding of Ti–6Al–4V alloy. In Proceedings of World Academy of Science, Engineering and Technology (Vol. 70, pp. 512- 516). World Academy of Science, Engineering and Technology.

[27]. Sharif, S., & Rahim, E. A. (2007). Performance of coated-and uncoated-carbide tools when drilling titanium alloy—Ti–6Al4V. Journal of Materials Processing Technology, 185(1-3), 72-76.

[28]. Sharma, A. K., Tiwari, A. K., & Dixit, A. R. (2016). Effects of minimum quantity lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: A comprehensive review. Journal of Cleaner Production, 127, 1-18.

[29]. Shyha, I., Gariani, S., & Bhatti, M. (2015). Investigation of cutting tools and working conditions effects when cutting Ti-6Al-4V using vegetable oil-based cutting fluids. Procedia Engineering, 132, 577-584.

[30]. Songmei, Y., Xuebo, H., Guangyuan, Z., & Amin, M. (2017). A novel approach of applying copper nanoparticles in minimum quantity lubrication for milling of Ti-6Al-4V. Advances in Production Engineering & Management, 12(2), 139.

[31]. Srithar, A., Palanikumar, K., & Durgaprasad, B. (2014). Experimental investigation and analysis on hard turning of AISI D2 steel using coated carbide insert. In Advanced Materials Research ,984-985, 154-158.

[32]. Vinayagamoorthy, R., & Xavior, M. A. (2014). Parametric optimization on multi-objective precision turning using grey relational analysis. Procedia Engineering, 97, 299-307. https://doi.org/10.1016/j. proeng.2014.12.253

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Optimization of Tool wear and Surface Roughness in Turning Titanium (Ti-6Al-4V) Alloy; NFMQCF Technique

Abstract

Keywords

How to Cite this Article?

References

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