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Vol.4, No.3, 2025: pp.134-144
EMPIRICAL MODELLING AND GREY RELATIONAL OPTIMIZATION OF WIRE ELECTRIC DISCHARGE MACHINING OF Ni51.59Ti SHAPE MEMORY ALLOY
Authors:
Thakur Singh1
, Pradeep Kumar Karsh2
, Prabhakar Zainith1
1Department of Mechanical Engineering, Shivalik College of Engineering, Dehradun, India
2Department of Mechanical Engineering, Parul Institute of Engineering & Technology, Parul University,
Vadodara, India
Received: 28 June 2025
Revised: 22 August 2025
Accepted: 4 September 2025
Published: 30 September 2025
Abstract:
In this study, Ni51.59Ti shape memory alloy (SMA) was machined using Wire Electric Discharge Machining (WEDM) to investigate and optimize the influence of key process parameters like spark on-time (SON), spark off-time (SOFF), servo voltage (SV), and wire feed (WF) on material removal rate (MRR) and surface roughness (SR). A total of 29 experiments were conducted using the Box-Behnken design (BBD) under the Response Surface Methodology (RSM) framework. Quadratic regression models were developed for MRR and SR, and Analysis of Variance (ANOVA) revealed that SON had the most significant effect on both responses, followed by SOFF, SV, and WF. The predictive models demonstrated high accuracy with R2 values of 0.9609 for MRR and 0.9668 for SR, confirming their reliability for parametric predictions. To address the multi-objective nature of the problem, Grey Relational Analysis (GRA) was also employed to determine the optimal parameter settings, achieving SON = 115 μs, SOFF = 60 μs, WF = 9 mm/min, and SV = 70 V. Under these optimized conditions, a favourable trade-off between maximum MRR and minimum SR was achieved, as verified by confirmation experiments with minimal error. The study highlights that increasing spark on-time and reducing spark off-time significantly enhance MRR while maintaining acceptable surface quality. This work demonstrates the effectiveness of integrating RSM with GRA for WTEM parameter optimization, offering a robust framework for machining complex and hard-to-cut shape memory alloys. The findings provide a valuable reference for extending hybrid optimization techniques such as machine learning, genetic algorithms, or ANFIS to further improve process efficiency and surface integrity in advanced manufacturing of NiTi-based components.
Keywords:
Wire EDM, Shape memory, Optimization, Titanium alloys, GRA, RSM
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© 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 4
Number 4
December 2025.
How to Cite
T. Singh, P.K. Karsh, P. Zainith, Empirical Modelling and Grey Relational Optimization of Wire Electric Discharge Machining of Ni51.59Ti Shape Memory Alloy. Advanced Engineering Letters, 4(3), 2025: 134-144.
https://doi.org/10.46793/adeletters.2025.4.3.3
More Citation Formats
Singh, T., Karsh, P.K., & Zainith, P. (2025). Empirical Modelling and Grey Relational Optimization of Wire Electric Discharge Machining of Ni51.59Ti Shape Memory Alloy. Advanced Engineering Letters, 4(3), 134-144.
https://doi.org/10.46793/adeletters.2025.4.3.3
Singh, Thakur, et al. “Empirical Modelling and Grey Relational Optimization of Wire Electric Discharge Machining of Ni51.59Ti Shape Memory Alloy.“ Advanced Engineering Letters, vol. 4, no. 3, 2025, pp. 134-144. https://doi.org/10.46793/adeletters.2025.4.3.3
Singh, Thakur, Pradeep Kumar Karsh, and Prabhakar Zainith. 2025. “Empirical Modelling and Grey Relational Optimization of Wire Electric Discharge Machining of Ni51.59Ti Shape Memory Alloy.“ Advanced Engineering Letters, 4 (3): 134-144.
https://doi.org/10.46793/adeletters.2025.4.3.3
Singh, T., Karsh, P.K. and Zainith, P. (2025). Empirical Modelling and Grey Relational Optimization of Wire Electric Discharge Machining of Ni51.59Ti Shape Memory Alloy. Advanced Engineering Letters, 4(3), pp. 134-144.
doi: 10.46793/adeletters.2025.4.3.3.