Electrochemical machining is a modern machining technique that plays an important role in the applications of aerospace, die and electronic industries. Electrochemical machining is used to machine difficult-to-machine materials and complex shapes. Machining of alloy steels especially 20MnCr5 is very important for its wide variety of applications such as piston bolts, spindles, camshafts, gears, shafts and other mechanical controlling parts. Machining of the above components with conventional machine tools is a burdensome task. Hence, in this work, an investigation was made to study the electrochemical machining characteristics of 20MnCr5 alloy steel. Two electrolytes, namely, aqueous sodium chloride (NaCl) and potassium dichromate (K2Cr2O7) mixed aqueous NaCl, were used to investigate the machining performance. K2Cr2O7 was selected for its oxidizing characteristics and was included in small proportions in aqueous NaCl bath. The influence of predominant electrochemical machining process parameters such as applied voltage (V), inter electrode gap and electrolyte concentration was studied on the material removal rate and surface roughness (Ra). Scanning electron microscope photography of surface of the 20MnCr5 specimen machined with electrochemical machining was studied to understand the effect of electrolytes during the machining. The contour plots were generated to study the effect of process parameters as well as their interactions. It was noted in the study that the presence of K2Cr2O7 in aqueous NaCl electrolyte increases the material removal rate significantly. The process parameters are optimized through genetic algorithm-desirability function. Optimized operating conditions were found to be quite close with experimental results.

This paper investigates the performance enhancement of electrochemical machining (ECM) process by oxygenated aqueous sodium chloride (NaCl) electrolyte. It is experimentally found that the performance parameters such as material removal rate (MRR) and surface roughness (R a ) are greatly improved with this new mix of electrolyte. The oxygen gas is suitable to electrochemically react with aqueous NaCl solution to enhance the oxidation of metal oxides on the machined surface to increase the MRR. In this work, experimental investigations are conducted with both aqueous NaCl electrolyte and oxygenated aqueous NaCl electrolyte on the alloy steel specimen (20MnCr5). Largest MRR of 7.33 g/min with R a of 1.90 μm is obtained in oxygenated NaCl environment compared to MRR of 1.98 g/min and R a of 3.13 μm in aqueous NaCl environment with machining conditions of voltage (V) of 17 V, tool feed rate (F) of 0.5 mm/min, and electrolyte concentration (EC) of 142.5 g/l of water. Microstructure of surface of the specimen machined with oxygenated aqueous NaCl electrolyte is more homogeneous and promises a good surface quality.

Electrochemical machining (ECM) of metals, particularly steels, plays an important role in numerous industrial applications. The alloy steels especially 20MnCr5 are used as good wear resistant in the applications of boxes, piston bolts, spindles, camshafts, gears, shafts and other mechanical controlling parts. Machining of above complicated shapes with conventional machine tools is cumbersome task. Hence, investigation was made to study the electrochemical machining characteristics of 20MnCr5 alloy steel. Hydrogen peroxide (H 2 O 2 ) mixed aqueous NaCl was used as electrolyte in order to enhance the machining performance of ECM. The predominant ECM process parameters such as applied voltage (V), inter-electrode gap (IEG) and electrolyte concentration (EC) were considered to study its influence on the material removal rate (MRR) and surface roughness (R a ). Microstructure of surface of the 20MnCr5 steel specimen machined with ECM was studied to understand the effect of electrolyte during the machining. The contour plots were generated to study the effect of process parameters as well as their interactions. The optimized machined conditions found with particle swarm optimization (PSO)-desirability function (DF) optimizer were quite close with experimental results. Additional advantage of H 2 O 2 presence was noted that scales formed on flow path of electrolyte were completely removed.

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.

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