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Optimization of cryo-treated EDM variables using TOPSIS-based TLBO algorithm

  • CHINMAYA PRASAD MOHANTY
  • MANTRA PRASAD SATPATHY
  • SIBA SANKAR MAHAPATRA
  • MANAS RANJAN SINGH
Article
  • 38 Downloads

Abstract

In order to machine hard and high-strength-to-weight ratio materials, electrical discharge machining (EDM) process is extensively used in aerospace, automobile and other industrial applications. However, high erosion of tool and improper selection of machining variables have emerged as a major obstruction to achieve productivity in this direction. High erosion of tool not only enhances the cost of machining but also increases the machining time by causing interruption during machining. Therefore, proper selection of machining variables and tool material life are the two vital aspects for the tool engineers working in EDM. In view of this, the present work proposes an extensive experimental investigation and optimization of machining variables of cryogenically treated brass tool materials on machining competences of Inconel 718 workpiece. The study primarily highlights the outcome of cryogenically treated soaking duration of tools along with other important process variables, viz. discharge current, open-circuit voltage, pulse-on time, duty factor and flushing pressure, on the performance measures such as electrode wear ratio (EWR), surface roughness and radial over-cut. The study revealed that soaking duration in deep cryo-treatment of the electrode is a significant variable to achieve improved machining characteristics. The performance measures are converted into equivalent single performance measure by calculating the relative closeness coefficient by the techniques for order preferences by similarity to ideal solution (TOPSIS) approach. Finally, a novel teaching–learning-based optimization (TLBO) algorithm has been proposed to find the optimal level of machining variables for the performance measures. The optimal levels of cutting variables obtained through the algorithm are validated through confirmation test, predicting an error of 2.171 percentages between the computational and experimental results. The predicted result suggests that the proposed model can be used to select the ideal process states to achieve productivity for the cryo-treated EDM.

Keywords

EDM; electrode wear ratio; soaking duration; TOPSIS; TLBO 

Notes

Acknowledgements

The experimental data collected for this study are based on the Doctoral thesis submitted to National Institute of Technology, Rourkela, authored by Dr. Chinmaya Prasad Mohanty, i.e., by the first author of this manuscript. The authors thank National Institute of Technology, Rourkela, India, for providing their facilities and resources to carry out the research work. It is hereby declared that the material present in the manuscript is the original research carried out by the authors. No part of this manuscript or the entire manuscript has been submitted to any conference or journal. The thesis has been also cited in the manuscript. For further clarification, the following link of the thesis submitted to National Institute of Technology, Rourkela, is also provided: http://ethesis.nitrkl.ac.in/6915/1/Chinmaya_511ME123_PhD_2015.pdf

References

  1. 1.
    Lee S and Li X 2001 Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide. J. Mater. Process. Technol. 115(3): 344–358CrossRefGoogle Scholar
  2. 2.
    Kuppan P, Narayanan S and Rajadurai A 2011 Effect of process parameters on material removal rate and surface roughness in electric discharge drilling of inconel 718 using graphite electrode. Int. J. Manuf. Technol. Manag. 23(3-4): 214–233CrossRefGoogle Scholar
  3. 3.
    Kumar S, Singh R, Singh T and Sethi B 2009 Surface modification by electrical discharge machining: a review. J. Mater. Process. Technol. 209(8): 3675–3687CrossRefGoogle Scholar
  4. 4.
    Kumar N, Kumar L, Tewatia H and Yadav R 2012 Comparative study for mrr on die-sinking edm using electrode of copper & graphite. Int. J. Adv. Technol. Eng. Res. 2(2): 170–174Google Scholar
  5. 5.
    Åhman L 1984 Microstructure and its effect on toughness and wear resistance of laser surface melted and post heat treated high speed steel. Metall. Trans. A 15(10): 1829–1835CrossRefGoogle Scholar
  6. 6.
    Apachitei I, Tichelaar F, Duszczyk J and Katgerman L 2002 The effect of heat treatment on the structure and abrasive wear resistance of autocatalytic nip and nip–sic coatings. Surf. Coat. Technol. 149(2): 263–278CrossRefGoogle Scholar
  7. 7.
    Kim H, Miyaji F, Kokubo T and Nakamura T 1997 Effect of heat treatment on apatite-forming ability of ti metal induced by alkali treatment. J. Mater. Sci. Mater. Med. 8(6): 341–347Google Scholar
  8. 8.
    Arockia Jaswin M and Mohan Lal D 2010 Optimization of the cryogenic treatment process for en 52 valve steel using the Grey–Taguchi method. Mater. Manuf. Process. 25(8): 842–850CrossRefGoogle Scholar
  9. 9.
    Kumar A, Maheshwari S, Sharma C and Beri N 2012 Machining efficiency evaluation of cryogenically treated copper electrode in additive mixed edm. Mater. Manuf. Process. 27(10): 1051–1058CrossRefGoogle Scholar
  10. 10.
    Jafferson J M and Hariharan P 2013 Machining performance of cryogenically treated electrodes in microelectric discharge machining: a comparative experimental study. Mater. Manuf. Process. 28(4): 397–402CrossRefGoogle Scholar
  11. 11.
    Kapoor J, Singh S and Khamba J S 2012 Effect of cryogenic treated brass wire electrode on material removal rate in wire electrical discharge machining. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 226(11):2750–2758Google Scholar
  12. 12.
    Gill S S, Singh H, Singh R and Singh J 2010 Cryoprocessing of cutting tool materialsa review. Int. J. Adv. Manuf. Technol. 48(1): 175–192CrossRefGoogle Scholar
  13. 13.
    Gill S S and Singh J 2010 Effect of deep cryogenic treatment on machinability of titanium alloy (ti-6246) in electric discharge drilling. Mater. Manuf. Process. 25(6): 378–385MathSciNetCrossRefGoogle Scholar
  14. 14.
    Xu J, Liu Y, Wang J, Kui X, Gao Y and Xu Z 2007 A study on double glow plasma surface metallurgy mo–cr high speed steel of carbon steel. Surf. Coat. Technol. 201(9): 5093–5096CrossRefGoogle Scholar
  15. 15.
    Lal D M, Renganarayanan S and Kalanidhi A 2001 Cryogenic treatment to augment wear resistance of tool and die steels. Cryogenics 41(3): 149–155CrossRefGoogle Scholar
  16. 16.
    Collins D and Dormer J 1997 Deep cryogenic treatment of a d 2 cold-work tool steel. Heat Treat. Metals (UK) 24(3): 71–74Google Scholar
  17. 17.
    Mohanty C P, Mahapatra S S and Singh M R 2016 A particle swarm approach for multi-objective optimization of electrical discharge machining process. J. Intell. Manuf. 27(6): 1171–1190CrossRefGoogle Scholar
  18. 18.
    Padhee S, Nayak N, Panda S, Dhal P and Mahapatra S 2012 Multi-objective parametric optimization of powder mixed electro-discharge machining using response surface methodology and non-dominated sorting genetic algorithm. Sadhana, pp. 1–18Google Scholar
  19. 19.
    Prabhu S and Vinayagan B K 2013 Multi objective optimisation of swcnt-based electrical discharge machining process using grey relational and fuzzy logic analysis. Int. J. Mach. Mach. Mater. 13(4): 439–463CrossRefGoogle Scholar
  20. 20.
    Dewangan S and Biswas C K 2013 Optimisation of machining parameters using grey relation analysis for edm with impulse flushing. Int. J. Mechatron. Manuf. Syst. 6(2): 144–158CrossRefGoogle Scholar
  21. 21.
    El-Taweel T A and Hewidy M S 2009 Enhancing the performance of electrical-discharge machining via various planetary modes. Int. J. Mach. Mach. Mater. 5(2–3):308–320CrossRefGoogle Scholar
  22. 22.
    Dewangan S, Gangopadhyay S and Biswas C 2015 Study of surface integrity and dimensional accuracy in edm using fuzzy topsis and sensitivity analysis. Measurement 63: 364–376CrossRefGoogle Scholar
  23. 23.
    Puhan D, Mahapatra S S, Sahu J and Das L 2013 A hybrid approach for multi-response optimization of non-conventional machining on alsic p mmc. Measurement 46(9): 3581–3592CrossRefGoogle Scholar
  24. 24.
    Selvarajan L, Narayanan C S, Jeyapaul R and Manohar M 2016 Optimization of edm process parameters in machining si 3 n 4–tin conductive ceramic composites to improve form and orientation tolerances. Measurement 92: 114–129CrossRefGoogle Scholar
  25. 25.
    Srivastava V and Pandey P M 2011 Study of the cryogenically cooled electrode shape in electric discharge machining process. In: Conference Proceedings of the World Academy of Science, Engineering and Technology, vol. 60, pp. 1017–1021Google Scholar
  26. 26.
    Abdulkareem S, Khan A A and Konneh M 2009 Reducing electrode wear ratio using cryogenic cooling during electrical discharge machining. Int. J. Adv. Manuf. Technol. 45(11): 1146–1151CrossRefGoogle Scholar
  27. 27.
    Srivastava V and Pandey P M 2012 Performance evaluation of electrical discharge machining (edm) process using cryogenically cooled electrode. Mater. Manuf. Process. 27(6): 683–688CrossRefGoogle Scholar
  28. 28.
    Joshi S and Pande S 2009 Development of an intelligent process model for edm. Int. J. Adv. Manuf. Technol. 45(3): 300–317CrossRefGoogle Scholar
  29. 29.
    Joshi S N and Pande S 2010 Thermo-physical modeling of die-sinking edm process. J. Manuf. Process. 12(1): 45–56CrossRefGoogle Scholar
  30. 30.
    Paramashivan S S, Mathew J and Mahadevan S 2012 Mathematical modeling of aerosol emission from die sinking electrical discharge machining process. Appl. Math. Modell. 36(4): 1493–1503CrossRefGoogle Scholar
  31. 31.
    Chen Y and Mahdivian SM 2000 Analysis of electro-discharge machining process and its comparison with experiments. J. Mater. Process. Technol. 104(1): 150–157CrossRefGoogle Scholar
  32. 32.
    Mohanty C P, Sahu J and Mahapatra S S 2013 Thermal–structural analysis of electrical discharge machining process. Proc. Eng. 51: 508–513CrossRefGoogle Scholar
  33. 33.
    Singh S, Maheshwari S and Pandey P 2004 Some investigations into the electric discharge machining of hardened tool steel using different electrode materials. J. Mater. Process. Technol. 149(1): 272–277CrossRefGoogle Scholar
  34. 34.
    Kumar V and Kumar P 2015 Experimental investigation of the process parameters in cryogenic cooled electrode in edm. J. Mech. Sci. Technol. 29(9): 3865–3871CrossRefGoogle Scholar
  35. 35.
    Tzeng G H and Huang J J 2011 Multiple attribute decision making: methods and applications. Chapman and Hall/CRCGoogle Scholar
  36. 36.
    Jayakumar D N and Venkatesh P 2014 Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem. Appl. Soft Comput. 23: 375–386CrossRefGoogle Scholar
  37. 37.
    Behzadian M, Otaghsara S K, Yazdani M and Ignatius J 2012 A state-of the-art survey of topsis applications. Expert Syst. Appl. 39(17): 13051–13069CrossRefGoogle Scholar
  38. 38.
    Mogale D, Dolgui A, Kandhway R, Kumar S K and Tiwari M K 2017 A multi-period inventory transportation model for tactical planning of food grain supply chain. Comput. Ind. Eng. 110: 379 – 394CrossRefGoogle Scholar
  39. 39.
    Maiyar L M and Thakkar J J 2017 A combined tactical and operational deterministic food grain transportation model: Particle swarm based optimization approach. Comput. Ind. Eng. 110: 30 – 42CrossRefGoogle Scholar
  40. 40.
    Rao R V, Rai D P and Balic J 2017 A multi-objective algorithm for optimization of modern machining processes. Eng. Appl. Artif. Intell. 61: 103 – 125CrossRefGoogle Scholar
  41. 41.
    Mogale D, Kumar S K, Mrquez F P G and Tiwari M K 2017 Bulk wheat transportation and storage problem of public distribution system. Comput. Ind. Eng. 104: 80 – 97CrossRefGoogle Scholar
  42. 42.
    Rao R V and Savsani V J 2012 Mechanical design optimization using advanced optimization techniques. Springer Science & Business MediaGoogle Scholar
  43. 43.
    Rao R V, Savsani V J and Vakharia D P 2012 Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. 183(1): 1–15MathSciNetCrossRefGoogle Scholar
  44. 44.
    Li J-Q, Pan Q-K and Mao K 2015 A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems. Eng. Appl. Artif. Intell. 37:279–292CrossRefGoogle Scholar
  45. 45.
    Ghasemi M, Ghanbarian M M, Ghavidel S, Rahmani S and Moghaddam E M (2014) Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: a comparative study. Inf. Sci. 278: 231–249MathSciNetCrossRefGoogle Scholar
  46. 46.
    Tiwari A and Pradhan M 2017 Applications of tlbo algorithm on various manufacturing processes: a review. Materials Today: Proceedings, vol. 4, no. 2, Part A, pp. 1644 – 1652, 2017, In: 5th International Conference of Materials Processing and Characterization (ICMPC 2016) Google Scholar
  47. 47.
    Rao R V, More K C, Taler J and Ocłoń P 2016 Optimal design of stirling heat engine using an advanced optimization algorithm. Sadhana 41(11): 1321–1331MathSciNetMATHGoogle Scholar
  48. 48.
    Rao R V, Savsani V J and Vakharia D 2011 Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3): 303–315CrossRefGoogle Scholar
  49. 49.
    Mohanty C P 2015 Studies on some aspects of multi-objective optimization: a case study of electrical discharge machining process, Ph.D. dissertationGoogle Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  • CHINMAYA PRASAD MOHANTY
    • 1
  • MANTRA PRASAD SATPATHY
    • 2
  • SIBA SANKAR MAHAPATRA
    • 3
  • MANAS RANJAN SINGH
    • 4
  1. 1.School of Mechanical Engineering and Building SciencesVIT UniversityVelloreIndia
  2. 2.School of Mechanical EngineeringKIIT UniversityBhubaneswarIndia
  3. 3.Department of Mechanical EngineeringNational Institute of TechnologyRourkelaIndia
  4. 4.Silicon Institute of TechnologyBhubaneswarIndia

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