Multi-response optimisation of machining parameters to minimise the overcut and circularity error during micro-EDM of nickel-titanium shape memory alloy

References

• Bengisu M, Ferrara M. Materials that move: smart materials, intelligent design. 1st. Cham: Springer; 2018. Chapter 2, Materials that Move.
   Google Scholar
• Chopra I, Sirohi J. Smart structures theory (Cambridge Aerospace Series). 1st. Cambridge: Cambridge University Press; 2013. Chapter 3, Shape Memory Alloys (SMAs).
   Google Scholar
• Stoeckel D, Tinschert F. Temperature compensation with thermovariable rate springs in automatic transmissions. SAE Tech Paper. 1991;30:35–42.
   Google Scholar
• Kumar PK, Lagoudas DC. Shape memory alloys: modeling and engineering applications. 1st. Boston (MA) US: Springer; 2008. Chapter 1, Introduction to shape memory alloys.
   Google Scholar
• Sharma N, Raj T, Jangra KK. Applications of nickel-titanium alloy. J Eng Technol. 2015;5(1):1–7.
   Google Scholar
• Petrini L, Migliavacca F. Biomedical applications of shape memory alloys. J Metall. 2011;2011:1–15.
   Google Scholar
• Ay M, Çaydaş U, Hasçalık A. Optimisation of micro-EDM drilling of Inconel 718 superalloy. Int J Adv Manuf Technol. 2013;66(5):1015–1023.
   Web of Science ®Google Scholar
• Jahan MP, Wong YS, Rahman M. A study on the fine-finish die-sinking micro-EDM of tungsten carbide using different electrode materials. J Mater Process Technol. 2009;209(8):3956–3967.
   Web of Science ®Google Scholar
• Jahan MP, Rahman M, Wong YS. A review on the conventional and micro-electrodischarge machining of tungsten carbide. Int J Mach Tools Manuf. 2011;51(12):837–858.
   Web of Science ®Google Scholar
• Sanchez JA, López de Lacalle LN, Lamikiz A. A computer-aided system for the optimisation of the accuracy of the wire electro-discharge machining process. Int J Comput Integr Manuf. 2004;17:413–420.
   Web of Science ®Google Scholar
• Masuzawa T. State of the art of micromachining. CIRP Ann - Manuf Technol. 2000;49(2):473–488.
   Google Scholar
• Suresh P, Venkatesan R, Sekar T, et al. Optimisation of intervening variables in micro-EDM of SS 316L using a genetic algorithm and response surface methodology. Strojniški vestnik – J Mech Eng. 2014;60(10):656–664.
   Web of Science ®Google Scholar
• Pradhan BB, Bhattacharyya B. Modeling of micro-electrodischarge machining during machining of titanium alloy Ti—6Al—4V using response surface methodology and artificial neural network algorithm. Proc Inst Mech Eng, Part B: J Eng Manuf. 2009;223(6):683–693.
   Web of Science ®Google Scholar
• Rajbongshi SK, Sarma DK. A comparative study in prediction of surface roughness and flank wear using artificial neural network and response surface methodology method during hard turning in dry and forced air-cooling condition. Int J Mach Mach Mater. 2019;21(5/6):390–436.
   Google Scholar
• Kuriachen B, Mathew J. Experimental investigations into the effects of microelectric- discharge milling process parameters on processing Ti–6Al–4V. Mater Manuf Processes. 2015;30(8):983–990.
   Web of Science ®Google Scholar
• Jahan MP, Kakavand P, Kwang ELM, et al. An experimental investigation into the micro-electro-discharge machining behaviour of aluminium alloy (AA 2024). Int J Adv Manuf Technol. 2015;78(5):1127–1139.
   Web of Science ®Google Scholar
• Rasheed MS, Al-Ahmari AM, El-Tamimi AM, et al. Analysis of influence of micro-EDM parameters on MRR, TWR and Ra in machining Ni-Ti shape memory alloy. Int J Recent Technol Eng. 2012;1(4):32–37.
   Google Scholar
• Rasheed MS, Abidi MH, El-Tamimi AM, et al. Investigation of micro-EDM input parameters on various outputs in machining Ni-Ti shape memory alloy using full factorial design. Adv Mater Res. 2013;816–817:173–179.
   Google Scholar
• Venkatesan K, Devendiran S, Bhupatiraju SCSR, et al. Experimental investigation and optimization of micro-drilling parameters on Inconel 800 superalloy. Mater Manuf Processes. 2020;35(11):1214–1227.
   Web of Science ®Google Scholar
• Pandey AK, Gautam GD. Grey relational analysis-based genetic algorithm optimization of electrical discharge drilling of Nimonic-90 superalloy. J Braz Soc Mech Sci Eng. 2018;40:1–16.
   Web of Science ®Google Scholar
• Singh AK, Patowari PK, Chandrasekaran M. Experimental study on drilling micro-hole through micro-EDM and optimization of multiple performance characteristics. J Braz Soc Mech Sci Eng. 2020;42:1–14.
   Web of Science ®Google Scholar
• Abidi MH, Al-Ahmari AM, Umer U, et al. Multi-objective optimisation of micro-electrical discharge machining of nickel-titanium-based shape memory alloy using Moga-II. Measurement. 2018;125:336–349.
   Web of Science ®Google Scholar
• Aksezer CS. On the sensitivity of desirability functions for multi-response optimisation. J Ind Manage Optim. 2008;4(4):685–696.
   Google Scholar
• Myers RH, Montgomery DC, Anderson-Cook CM. Response Surface methodology: process and product optimisation using designed experiments. 4th ed. Hoboken: John Wiley & Sons; 2016.
   Google Scholar
• Derringer G, Suich R. Simultaneous optimisation of several response variables. J Qual Technol. 1980;12(4):214–218.
   Web of Science ®Google Scholar
• Dureja JS, Gupta VK, Sharma VS, et al. Design optimisation 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. Proc Inst Mech Eng, Part B: J Eng Manuf. 2009;223(11):1441–1453.
   Web of Science ®Google Scholar
• Dureja JS, Gupta VK, Sharma VS, et al. Design optimisation of flank wear and surface roughness for CBN-TiN tools during dry hard turning of hot work die steel. Int J Mach Mach Mater. 2010;7(1):129–147.
   Google Scholar
• Dureja JS. Optimisation of tool wear during hard turning of AISI-H11 steel using TiN coated CBN-L tool. Int J Mach Mach Mater. 2012;12(1–2):37–53.
   Google Scholar
• Chattopadhyay AK, Chattopadhyay AB. Wear and performance of coated carbide and ceramic tools. Wear. 1982;80(2):239–258.
   Web of Science ®Google Scholar
• Liu S, Forrest JYL. Advances in grey systems research. 1st ed. Berlin Heidelberg: Springer; 2010. Chapter 16, Optimisation Method of Grey Relation Analysis Based on the Minimum Sensitivity of Attribute Weights.
   Google Scholar
• Ragupathy K, Velmurugan C, Senthilkumar N. Tribological and heat treatment prediction of stir cast Al 6061/SiC/MoS2 composites using grey relational analysis. J Balkan Tribol Assoc. 2018;24(2):198–217.
   Google Scholar
• Lin CL. Use of the Taguchi method and grey relational analysis to optimise turning operations with multiple performance characteristics. Mater Manuf Processes. 2004;19(2):209–220.
   Web of Science ®Google Scholar
• Manikandan N, Raju R, Palanisamy D, et al. Optimisation of spark erosion machining process parameters using hybrid grey relational analysis and artificial neural network model. Int J Mach Mach Mater. 2020;22(1):1–23.
   Google Scholar
• Antony J. Design of experiments for engineers and scientists. 2nd ed. London: Elsevier; 2014.
   Google Scholar
• Montgomery DC. Design and analysis of experiments. 8th ed. New York: Wiley; 2017.
   Google Scholar