Abstract
In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi’s L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm3/s, and minimum SR of 2.68 µm. The Pareto curve was generated which gives the optimal non-dominant solutions.
Highlights
Shape memory alloys (SMAs) have started to become popular due to their unique ability of memorizing or regaining the original shape from the plastic deformed condition by means of heating or magnetic or mechanical loading
Current, pulse-on time, pulse-off time, and powder concentration has been identified as important machining variables while material removal rate (MRR) and surface roughness (SR) as the output parameters for PMWEDM process of Ni55.8 Ti SMA
The sonication technique creates a stress on the natural graphite flakes, which is transferred over the sp2 hybridized carbons present in the graphene layers and weakens the bond between the graphene layers which binds them as the stacks layers
Summary
Shape memory alloys (SMAs) have started to become popular due to their unique ability of memorizing or regaining the original shape from the plastic deformed condition by means of heating or magnetic or mechanical loading. This increase in inter-electrode gap condition improves the performance by obtaining superior surface finish This realization leads to the initiation studies of Nano powdermixed WEDM (NPMWEDM) of nitinol SMA. Powder-mixed dielectric fluid has turned out to be popular among the researchers to obtain optimum parametric setting for multiple objectives such as MRR and SR. The current study focuses more on the effect of Nano-graphene powder concentration on WEDM process parameters of Nitinol SMA and the parametric optimization of the selected responses. The effect of Nano-graphene powder mixed with dielectric fluid has not been explored properly for multi-objective optimization of machining variables of WEDM process. Current, pulse-on time, pulse-off time, and powder concentration has been identified as important machining variables while MRR and SR as the output parameters for PMWEDM process of Ni55.8 Ti SMA. Investigation of machined surface was carried out using Scanning Electron Microscopy (SEM) to understand the effect of NPMWEDM process
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