Magnetic field assisted powder mixed electrical discharge machining is a hybrid machining process with suitable modification in electrical discharge machining combining the use of magnetic field and fine powder in the dielectric fluid. Aluminum 6061 alloy has found highly significance for the advanced industries like automotive, aerospace, electrical, marine, food processing and chemical due to good corrosion resistance, high strength-to-weight ratio, ease of weldability. In this present work, magnetic field assisted powder mixed electrical discharge machining setup was fabricated and experiments were performed using one factor at a time approach for aluminum 6061 alloy. The individual effect of machining parameters namely, peak current, pulse on time, pulse off time, powder concentration and magnetic field on material removal rate and tool wear rate was investigated. The effect of peak current was found to be dominant on material removal rate and tool wear rate followed by pulse on time, powder concentration and magnetic field. Increase in material removal rate and tool wear rate was observed with increase in peak current, pulse on time and a decrease in pulse off time, whereas, for material removal rate increases and tool wear rate decreases up to the certain value and follow the reverse trend with an increase in powder concentration. Material removal rate was increased and tool wear rate was decreased with increase in magnetic field.

Powder mixed electrical discharge machining (PM-EDM) is a technological advancement in electrical discharge machining (EDM) processes where fine powder is added to dielectric to improve the machining rate and surface quality. In this paper, machining of Nimonic-90 was carried out using fabricated PM-EDM, setup by adding silicon powder to kerosene oil. The influence of four input process parameters viz. powder concentration (P_C), discharge current (I_P), spark on duration (S_ON), and spark off duration (S_OFF) has been investigated on surface roughness and recast layer thickness. L9 Taguchi orthogonal and grey relational analysis have been employed for experimental design and multi-response optimization, respectively. With the addition of silicon powder to kerosene oil, a significant decrease in surface roughness and recast layer thickness was noticed, as compared to pure kerosene. Spark on duration was the most significant parameter for both surface roughness and the recast layer thickness. The minimum surface roughness (3.107 µm) and the thinnest recast layer (14.926 μm) were obtained at optimum process parameters i.e., P_C = 12 g/L, I_P = 3 A, S_ON = 35 μs, and S_OFF = 49 μs using grey relational analysis.

Magnetic field assisted powder mixed electrical discharge machining (MFAPM-EDM) is a variant of EDM process where magnetic field coupled with electric field is used with addition of fine powder in dielectric to improve the surface quality, machining rate and stability of the process. Aluminium 6061 alloy as workpiece was selected due to growing use in aviation, automotive, naval industries. In this present work, parametric study and optimization was carried out on MFAPM-EDM machined Aluminium 6061 alloy. In this study, process parameters such as discharge current (I_P), spark duration (P_ON), pause duration (P_OFF), concentration of powder (CP) and magnetic field (MF) were considered to analyze the effect on material erosion rate (MER) and electrode wear rate (EWR). Box Behnken design approach based on response surface methodology (RSM) was utilized for performing the experiments. Quadratic model to predict the MER and EWR were developed using response surface methodology. Discharge current has most significant effect of 50.176% and 36.36% on MER and EWR, respectively among all others process parameters. Teacher-learning-based optimization (TLBO) was employed for determining the optimal process parameters for maximum MER and minimal EWR. The results obtained with TLBO was compared with well-known optimization methods such as genetic algorithm (GA) and desirability function of RSM. Minimum EWR (0.1021 mm³/min) and maximum MER (30.4687 mm³/min) obtained using TLBO algorithm for optimized process parameters was found to better as compared to GA and desirability function.

Wire electrical discharge machining is a thermal energy-based non-conventional machining process which can machine conductive materials with high precision. In this present work, machining of Inconel-825 was performed using wire electrical discharge machining. Multi-objective parametric optimization was performed for maximum cutting rate and minimum surface roughness using teaching–learning-based optimization, grey relational analysis, and genetic algorithm. Four wire electrical discharge machining parameters, namely spark off time (S_OFF), spark on time (S_ON), peak current (I_P), and angle of cutting (A) were considered. Comparison of optimum wire electrical discharge machining parameters through teaching–learning-based optimization, grey relational analysis, and genetic algorithm was performed. The better optimum solution for wire electrical discharge machining parameters was obtained using teaching–learning-based optimization and optimum values were at I_P (1 A), S_ON (30 µs), S_OFF (12.5 µs), and A (44.8°) with cutting rate as 19.744 mm/min and surface roughness as 1.331 µm. The optimum results obtained using optimization techniques were validated with the experimental results and error was observed to be within 5%. Moreover, response surface models were developed to predict the cutting rate and surface roughness in terms of wire electrical discharge machining parameters using analysis of variance.

Magnetic field assisted powder mixed electrical discharge machining (MFAPM-EDM) process is a hybrid process machining process which improves the machining characteristics and stability of process using assistive magnetic field and dielectric admixed powder. In this article, study on overcut has been performed on MFAPM-EDM machined Aluminium 6061 alloy. Discharge current, powder concentration, pulse on duration, pulse off duration, and magnetic field strength as process parameters have been varied during experimentation. Box Behnken design approach was employed for experimental design to carry out the experiments. Suitable Semi-empirical model was formulated using dimensional analysis for predicting the overcut. The empirical model developed was also compared with RSM model and was found better in predicting the response. Optimum process parameters for minimal overcut was conducted desirability function approach of RSM. Experimental results divulged discharge current as the most important parameters for overcut as compared to other process parameters on account of higher F-value. Confirmatory experiments revealed good correlation between optimum and experimental results.