Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Rakesh Chaudhari; Aniket Kevalramani; Jay Vora; Sakshum Khanna; Vivek K Patel; Danil Yurievich Pimenov; Khaled Giasin

doi:10.3390/mi13071026

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Micromachines (Basel). 2022 Jun 28;13(7):1026. doi: 10.3390/mi13071026.

Authors

Rakesh Chaudhari¹, Aniket Kevalramani¹, Jay Vora¹, Sakshum Khanna², Vivek K Patel¹, Danil Yurievich Pimenov³, Khaled Giasin⁴

Affiliations

¹ Department of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Raysan, Gandhinagar 382007, India.
² Journal of Visualized Experiments, Delhi 110016, India.
³ Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080 Chelyabinsk, Russia.
⁴ School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK.

Abstract

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (T_on), and pulse-off-time (T_off) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and T_on were found to be major contributors on the response value of MRR, and SR, respectively. A teaching-learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm³/s at T_on of 95 µs, T_off of 9 µs, current of 6 A. Least SR was obtained at T_on of 35 µs, T_off of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

Keywords: near-dry wire electric discharge machining (WEDM); nitinol; optimization; shape memory alloys; teaching–learning based optimization (TLBO) algorithm.

Grants and funding

This research received no external funding.