Excellent characteristics of Nitinol shape memory alloys (SMAs) makes them favourable for use in industrial applications. Precision machining of such advanced alloys becomes a key requirement for industrial applications. Conventional machining processes imposes many difficulties for nitinol SMAs. Electrical discharge machining (EDM) process is appropriate for fabricating intricate and complex profile geometries and also provides a better alternative for difficult-to-cut materials. Addition of nano-particles in an appropriate amount in the dielectric fluid improves the machining by producing good dimensional accuracy, higher productivity, and good surface finish for machining of newly developed advanced alloys. The current study investigated the performance of powder-mixed EDM of nitinol SMA with the considerations of design variables of current, pulse-on-time (Ton), nano-graphene powder concentration (PC), and pulse-off-time (Toff) on surface roughness, dimensional deviation (DD), and material removal rate (MRR). Taguchi's L9 (3ˆ4) design was employed to perform the experiments and Minitab 17 software was used for statistical analysis of design variables using ANOVA, residual plots, and main effect plots. ANOVA results depicted that PC, Ton, and Toff were identified to be the highest contributing parameters with 75.18%, 29.37%, and 45.72% to affect MRR, SR, and DD, respectively. Obtained results has depicted a preferred combined positive trend of increase in MRR with a simultaneous drop in SR and DD after the addition of nano-graphene PC. HST algorithm was used to optimize single and multiple responses. Validation trials were also conducted to reveal the ability and suitability of the HTS technique. Field emission scanning electron microscopy revealed the minor occurrence of resolidified debris particles, globules, micro-pores, and micro-cracks after the addition of nano-graphene PC at 2 g/L.
- Shape memory alloys
- Heat transfer search algorithm