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With the development of the equipment manufacturing industry towards the direction of intelligence, the demand for the use of intelligent materials is increasing, and high-performance shape memory alloy (SMA) products are getting more and more attention. However, the current machining quality of shape memory alloy products can not meet the existing quality requirements. The mechanism of surface integrity of NiTi SMA needs to be revealed. The influence of martensitic transformation on microstructure, roughness, hardness, and superelasticity was explored. The occurrence of martensitic transformation is determined by controlling the cutting speed (lower and higher cutting speeds) and machining conditions (dry and liquid nitrogen conditions). The superelasticity was expressed quantitatively by the remnant depth ratio. The occurrence of martensitic transformation promoted the formation of the affected layer and work hardening. Meanwhile, it is an important reason for the increase in roughness and the decrease in superelasticity.

Electric discharge drilling (EDD) is a variant of spark erosion machining and is successfully used for the drilling of quality microholes. In present research, an attempt was made to drill microholes on Al/SiC composite. The drilling on aluminum composite was made at different machining indicators to analyze its effect on drilling rate (DR), electrode wear ratio (EWR), hole overcut (HO) and circularity (C). The input parameters selected in this work are pulse on-time (Ton), peak current (Ip) and pulse off-time (Toff). The experiments were planned as per Taguchi's L9 orthogonal array (OA). It was found that with the increase in Ton value, HO and EWR increases. However, larger values of Ip and Toff decreases the HO and EWR. Due to the combination of different quality characteristics Grey theory along with desirability has been implemented for collective response of output parameters. The optimized setting for multiple performance characteristics is Ton: 40; Ip: 3; Toff: 50. An improvement of 0.0199 is observed in grade value during optimization as compared to the maximum calculated grade value. After the implementation of proposed optimization technique, an improvement of 42.88% in DR, a reduction of 33.59% in EWR, a reduction of 4.54% in HO and 0.68% in C is observed.

Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy.

The present research used the stir-casting method to develop an Al-based composite. The developed composite exhibited challenges while being processed on conventional machining. Thus, a non-traditional machining process was opted to process the composite. The machining variables selected for the current research were the pulse off time (Toff), pulse on time (Ton), servo voltage (SV), current (I), and tool electrode. Three tool electrodes (SS-304, copper, and brass) were used to process the developed composite (Al/SiC/Gr). The experimental plan was designed using response surface methodology (RSM). The output responses recorded for the analysis were the material removal rate (MRR) and tool wear rate (TWR). The obtained data was optimized using complex proportional assessment (COPRAS) and machine learning methods. The optimized settings predicted by the RSM–COPRAS method were Ton: 60 µs; Toff: 60 µs; SV: 7 V; I: 12 A; and tool: brass. The maximum MRR and TWR at the suggested settings were 1.11 g/s and 0.0114 g/s, respectively. A morphological investigation of the machined surface and tool surface was conducted with scanning electron microscopy. The morphological examination of the surface (machined) presented the presence of cracks, lumps, etc.