The emerging demands of industry for developing the novel materials with superior mechanical properties have successfully resulted in the development of distinct materials such as Al-matrix composites. Among these composites, newly developed Al6061-7.5% SiC holds promising mechanical characteristics. But, the SiC reinforcement in the Al-matrix makes the machining of this composite challenging, thus posing a serious concern regarding its effective utilization. In this research, high-speed wire electric discharge machining (WEDM) was employed for the precise machining of a squeeze casted Al6061-7.5% SiC composite. The cutting performance of the WEDM was assessed in terms of roughness (S_R), cutting rate (C_s) and kerf width (K_W). Experimentation was performed according to the response surface methodology. The experimental findings were thoroughly investigated using statistical, optical and scanning electron microscopic (SEM) analyses. It has been revealed that the voltage is most influential/contributing parameter (having a percentage contribution of 25%) for controlling theS_Rduring WEDM of Al6061-7.5% SiC composite, whereas for theC_SandK_W, pulse and current are the major contributing control variables with percentage contributions of 90% and 84%, respectively. At low magnitude of both current and voltages, the surface quality is improved up to 33.3%. The SEM and optical microscopic evidences reveal shallow craters, small size melt re-deposits and micro globules on the machined surface at lower settings of both the said variables. Contrarily, for achieving higher cutting speed, high values of current and voltage along with low pulse are deemed essential. In case ofK_W, low magnitude of current and voltage along with smaller pulse yields 20% reduction in the kerf width. The analyses revealed the conflicting nature of the studied output responses (S_R,C_sandK_W). Therefore, multi-objective genetic algorithm (MOGA) was used to find a parametric combination. The best combination of WEDM input parameters found is current = 3 A, voltage = 84.999 V and pulse = 10 mu. This combination gives a minimumS_Rof 5.775 μm with aK_Wof 0.3111 mm at aC_Sof 5.885 mm/min. The suitability of the MOGA-proposed parametric combination was witnessed through confirmation trials. Furthermore, the parametric effects have also been mathematically quantified with respect to the defined machinability parameters.

The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.

The requirement of cost-effective and ecological production systems is crucial in the competitive market. In this regard, the focus is shifted towards sustainable and cleaner machining processes. Besides the clean technologies, effective parametric control is required for machining materials (such as High Strength Low Alloy Steels) specifically designed for high strength applications having superior physio-chemical properties. Therefore, the machinability complexities require optimized solutions to reduce temperature elevation and tooling costs and improve machining of these materials. Complying to the market needs, this research examines the effectiveness of nanofluid on tool life, wear mechanisms, surface roughness (Ra), surface morphology, and material removal rate (MRR) in turning of 30CrMnSiA (HSLA) using minimum quantity lubrication (MQL) and SiO₂-H₂O nanofluids (NF-MQL). A systematic investigation based on physical phenomena involved is carried out considering four process parameters (cutting speed (V_C), feed rate (F_r), depth of cut (D_OC), and mode of lubrication for machining. F_r is found as the vital parameter for surface roughness while MRR is highly influenced by D_OC regardless of lubrication approach. One-step sustainability technique is applied, in which process variables used for roughing conditions are analogous to attain surface comparable to finished machining without compromising process efficiency and demonstrate its feasibility through optimal settings under NF-MQL. Multi-response optimization proved the NF-MQL machining condition as the best alternative which result in 28.34% and 5.09% improvements for surface roughness and MRR, respectively. Moreover, the use of SiO₂ is recommended over MQL due to lower energy consumption, low tool wear, and better surface integrity, sustainable liquid, and related costs.