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AA2014 is a copper-based alloy and is typically used for production of complex machined components, given its better machinability. The purpose of this paper was to study the effects of variation in weight percentage of ceramic Al₂O₃ particulates during electrical discharge machining (EDM) of stir cast AA2014 composites. Scanning electron microscopy (SEM) examination was carried out to study characteristics of EDMed surface of Al₂O₃/AA2014 composites.

The effect of machining parameters on performance measures during sinker EDM of stir cast Al₂O₃/AA2014 composites was examined by “one factor at a time” (OFAT) method. The stir cast samples were obtained by using three levels of weight percentage of Al₂O₃ particulates, i.e. 0 Wt.%, 10 Wt.% and 20 Wt.% with density 1.87 g/cc, 2.35 g/cc and 2.98 g/cc respectively. Machining parameters varied were peak current (1-30 amp), discharge voltage (30-100 V), pulse on time (15-300 µs) and pulse off time (15-450 µs) to study their influence on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR).

MRR and SR decreased with an increase in weight percentage of ceramic Al₂O₃ particulates at the expense of TWR. This was attributed to increased microhardness for reinforced stir cast composites. However, microhardness of EDMed samples at fixed values of machining parameters, i.e. 9 amp current, 60 V voltage, 90 µs pulse off time and 90 µs pulse on time reduced by 58.34, 52.25 and 46.85 per cent for stir cast AA2014, 10 Wt.% Al₂O₃/AA2014 and 20 Wt.% Al₂O₃/AA2014, respectively. SEM and quantitative energy dispersive spectroscopy (EDS) analysis revealed ceramic Al₂O₃ particulate thermal spalling in 20 Wt.% Al₂O₃/AA2014 composite. This was because of increased particulate weight percentage leading to steep temperature gradients in between layers of base material and heat affected zone.

This work was an essential step to assess the machinability for material design of Al₂O₃ reinforced aluminium metal matrix composites (AMMCs). Experimental investigation on sinker EDM of high weight fraction of particulates in AA2014, i.e. 10 Wt.% Al₂O₃ and 20 Wt.% Al₂O₃, has not been reported in archival literature. The AMMCs were EDMed at variable peak currents, voltages, pulse on and pulse off times. The effects of process parameters on MRR, TWR and SR were analysed with comparisons made to show the effect of Al₂O₃ particulate contents.

Electrical discharge machining (EDM) is well-known for its credibility in the processing of advanced materials, which are electrically conductive. The strenuous effort associated with machining of Ti6Al4V (Ti64) using conventional methods, and its low tribological behavior, present an immediate need to develop solutions to monitor and improve the compatible techniques such as EDM.

The present work includes following: monitoring the ED process parameters, namely, current (I) and pulse on time (T_on), in controlling the material removal rate and surface roughness (R_a and S_a) for development of tribo-adaptive surfaces; and investigation on the role of oxides pertinent to the tribo-behavior of Ti64 (bare and EDMed) surfaces.

The tribological behavior of Ti6Al4V surfaces got remarkably improved through ED machining, which points to the credibility of the process to establish itself as a surface alloying technique. The recast layer (RL, alloyed matrix) acted as a protective coating; stable enough to assist the developed tribo-oxides such as TiO and Ti₈O₁₅ in rendering improved sliding performance at load = 50 N and speed = 0.838 ms⁻¹.

The surface modification through ED machining was experimentally proven to improve the wear behavior of Ti6Al4V surfaces.

The purpose of this paper is to investigate the process parameters and optimise the machining input parameter of powder mixed electric discharge machining for high carbon high chromium alloy steel (D2 steel) for the industrial application. Grey relational analysis approach has been used to obtain the multiple performance output response.

In this experimental work, input parameters, namely, pulse on-time, discharge current, tool material and grit size, are selected. The design of the experiment has been constructed with the help of MINITAB 7 Software, in which L16 orthogonal array has been preferred for the experimentation. The effect of input parameters, namely, material removal rate, tool wear rate and surface roughness, is investigated. Grey relational analysis and analysis of variance are performed to optimise the input parameters and better output results.

In this experimentation, there is an increment of tool wear rate by 64.49 per cent, material removal rate by 47.14 per cent and surface roughness by 35.82 per cent.

A lot of practical applications have been found in many different material processing industries like metallurgy, machinery, electronics, transportation, military science, agricultural machinery, etc. These practical applications have brought forward definite and noticeable economic benefits.

The reader is given a general overview on the machining investigation and optimisation of processes parameters through the grey theory approach. It gives a new framework to investigate the problems where multiple input machining variable and various output responses are obtained in single optimised parameters.

The electric discharge machining (EDM) involves electrons discharged from the electrode and machining progresses due to the removal of the material from the component. This a thermal-based machining process primarily used for hard to machine components with conventional methods. This process is used to make intricate cavities and contours. The fabricated part is the replica of the tool material with high surface finish and good dimensional accuracy. This study aims to evaluate the comprehensive effect of process parameters on electric discharge machining of maraging steel.

Multiple criteria Decision making (MCDM) techniques are used to select the best parameters by comparing several responses to achieve the desired goal. There are different MCDM techniques available for optimization of machining parameters. In the current investigation, multi-objective optimization by data envelopment analysis based ranking (DEAR) approach was used for machining Maraging C300 grade steel.

The Taguchi L9 runs were planned with process parameters such as current (Amp), Tool diameter (mm) and Dielectric pressure (MPa). The effect of process parameters on the responses, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were evaluated. High MRR is found at 15 A current, 14 mm tool diameter and dielectric pressure of 0.2 MPa. Optimum process parameters experiment showed reduced crack density.

An effort was made successfully to enhance the responses using the DEAR method and establish the decision making of selecting the optimal parameters by comparing the results obtained by machining maraging steel C300 grade.

The purpose of this paper is to represent the innovative process of powder-mixed electrical discharge machining of high-speed steel T1 grade and to conduct experimental investigation to optimize the machining parameters associated with multiple performance characteristics using grey relational analysis. The machining of high-speed steel T1 grade via conventional machining is a difficult process. However, it can be easily machined by powder-mixed electric discharge machining.

Carefully selected machining parameters give the optimum output results. For experimentation, the following input parameters have been used: pulse on-time, discharge current, tool material and powder concentration. The effects of input parameters, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), have been investigated in this research.

Grey relational analysis and analysis of variance have been performed to optimize the input parameters for better output response. Optimized results show increment of TWR, MRR and SR, which is 63.24, 52.18 and 42.49 per cent, respectively.

This research paper will be beneficial for the industrial application. The GRA result gives the better output response.

Nowadays, the applications of metal matrix composites are tremendously increasing in engineering fields. Consequently, the demand for precise machining of composites has also grown enormously. The purpose of this paper is to reduce production cost and simultaneously improve desired product quality through optimal parameter setting using WASPAS and MOORA.

Metal matrix composites were fabricated using stir casting process, with aluminum 6063 as matrix and titanium carbide as reinforcement. Fabricated composite samples were machined on medium duty lathe using cemented carbide tool. All the experiments were carried out based on Box–Behnken design. Comparison of multi objective optimization based on ratio analysis and weighted aggregated sum product assessment in optimizing four parameters, namely, “cutting speed,” “feed rate,” “depth of cut” and “reinforcement weight percent of composite samples”; evaluating their influence on material removal rate, cutting force and surface roughness were carried out.

The output achieved by both MOORA and WASPAS are in similar MCDM) techniques in the selection of machining parameters.

The results obtained in the present paper will be helpful for decision makers in manufacturing industries, who work in metal cutting area, to select the suitable levels for the parameters by implementing the MCDM techniques.

The novelty of this paper is making an attempt to select better MCDM technique based on the comparison of results obtained for the individual technique.

This study aims to explore the machinability of ZrB₂ using electrical discharge machining (EDM) with different tool materials.

The workpiece for this study was fabricated through powder metallurgy compaction method. The disc is machined using diamond load grinding to have parallel surfaces, then, 2 mm diameter holes are machined on the disc using EDM spark erosion machine with different tool materials (graphite, aluminium, tantalum, niobium, copper, brass, silver, tungsten and titanium). Roundness, geometry of hole, and diameter of the hole at different diametric planes, surface roughness (SR), material removal rate (MRR), tool wear rate (TWR), taper angle and recast layer (RCL) thickness are measured. The photographic analysis of tools, holes in the top view, bottom view and sectional view. SEM analysis was conducted to study the recast layer. Desirability function analysis was employed to rate the performances of tools.

A new theory is developed which relates recast layer thickness with melting point and thermal conductivity of the tool materials. Machining of ZrB₂ by EDM is feasible; graphite is identified as the best tool. Recast layer thickness of the machined surfaces are indirectly proportional to the product of melting point and thermal conductivity of tool. Ablation behaviour of ceramic workpiece lead additional material losses in the tool.

Extremely high strength and hardness of ZrB₂ due to the coexistence of strong covalent and metallic bond make mechanical machining very difficult or even impossible. No machinability studies were conducted previously on ZrB₂ using EDM; this work reveals machinability study of ZrB₂ with different tool materials.

The purpose of this paper is to study the effect of ultrasonic machining process parameters on surface quality while machining titanium alloy Ti-6Al-4V.

Effect of cryogenic treatment (CT) of tool and work material was also explored in the study. Taguchi’s L18 orthogonal array was chosen for design of experiments and average surface roughness was measured.

Different modes of fracture were detected at work surface corresponding to varied input process parameters. Slurry grit size, power rating and tool material along with CT of work material were found to be the significant parameters affecting surface quality.

The results obtained have been modelled using artificial neural network approach.