Shape memory alloys (SMAs) are unique class of smart materials with excellent physical, mechanical and biomedical properties, which have wide applications in several fields such as aerospace, robotics, biomedical, and dental etc. These alloys are well known for exhibiting shape memory effect (SME) and pseudoelasticity (PE), it is a well-established fact that they are required to be processed into functioning parts. The conventional machining affects the internal properties of shape memory alloys and hence, it is reported that nonconventional machining techniques are more suitable. Wire electro discharge machining (WEDM) is one of the nonconventional machining processes for machining complicated shapes without hampering the internal properties of such type of materials. In the present experimental investigation, wire electro discharge machining of Ti 50 Ni 40 Co 10 shape memory alloy (SMA) has been carried out and machining performances such as surface roughness (SR), and material removal rate (MRR) have been evaluated. Experimental results exposed that pulse on time, pulse off time and servo voltages are most influential process parameters on the responses. The machined surface has been characterised with respect to microstructure, microhardness, and phases formed.

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The Altemp HX is a nickel-based superalloy having many applications in chemical, nuclear, aerospace, and marine industries. Machining such superalloys is challenging as it may cause both tool and surface damage. WEDM, a non-contact machining technique, can be employed in the machining of such alloys. In the present study, different input parameters which include pulse on time, wire span, and servo gap voltage were investigated. The cutting velocity, surface roughness, recast layer, and microhardness variations were examined on the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. The highest recast layer values were recorded as 25.8 µm, and the lowest microhardness was 170 HV. Response surface methodology and artificial neural network were employed for the prediction of cutting velocity and surface roughness. Artificial neural network prediction technique was the most efficient method for the prediction of response parameters as it predicted an error percentage lesser than 6%.

In this research, austenitic stainless steel SS316 material has been machined using textured carbide cutting tools under dry conditions. Micro-textures were made on tool rake face using wire spark erosion machining technology. Effects of three important machining process parameters i.e. cutting speed, depth of cut and feed rate on machinability (MRR, average roughness, and tool wear) of SS316 have been investigated. Taguchi L 27 orthogonal array based twenty seven experiments have been carried out by varying machining parameters at three levels. Feed rate has been identified as the most important parameter. Machining parameters have been optimized by grey entropy method to enhance the machinability. Optimal combination of machining parameters i.e. 170 m min −1 cutting speed, 0.5 mm/rev feed rate and 1.5 mm depth of cut produced the best machinability with 3.436 μm average roughness, 105187 mm 3 min −1 . MRR, and tool wear 234.63 μm. Lastly, a tool wear and chip morphology study have been done where textured tools have been found outperformed plain (Non-textured) tools.