Micro-wire-cut electrode discharge machining (EDM) is an emerging manufacturing process in the field of micro-manufacturing to fabricate the complex profiles of micro-components. It is a complex process involving various process parameters such as pulse on time, pulse off time, wire speed, wire tension and current. In addition to micro-fabrication, this process can also be extended in the field of tool design and developments such as dies, moulds, precision manufacturing, contour cutting, etc., where complex shapes need to be generated with high-grade dimensional accuracy and surface finish. In this research work, an attempt is made to investigate the effect of process parameters on the output variables such as material removal rate (MRR), surface finish and the cutting width (kerf) of wire-cut EDM for duplex stainless steel (DSS). Scanning electron microscopy (SEM) has been used to capture the images of the kerf width, and the measurements are taken with the help of the welding expert system and software. An optimization technique (Taguchi method) has been employed to identify the optimum parameters of the micro-wire-cut EDM process for cutting 2205 grade duplex stainless steel. The effect of each control parameter on the performance measure is studied individually using the plots of signal to noise ratio.

Incremental forming is a novel process which proves to be an effective and alternative method for the production of economically low-volume functional products. The process exhibits a potential to manufacture three dimensional parts without the use of dedicated dies. The paper describes the technique of using CNC milling machine and hemispherical shaped tool which aids in the formation of final shape of the component by a series of step deformation. The associated attributes pertinent to the analysis are forming angle, roughness characteristics and thickness distribution. Finite element modeling using ABAQUS were carried out and results were compared and validated.

Aluminum matrix composites (AMCs) are gaining increasing attention from various industries due to their lightweight and more excellent wear resistance than conventional materials. Manufacturers embracing that difficulty in machining MMC due to reinforcing particles abrasive nature shorten the tool life. Electro-discharge machining (EDM) is an enormously used non-conventional process to remove material in die making, aerospace, and automobile industries and machine any material with the highest hardness. Hence in the present study, EDM was performed on an aluminium alloy 8081 (AA8081) with reinforcement of 10% SiC, 5% B4C, and 5% Gr particles utilizing an ultrasonic cavitation assisted stir casting process. The machining investigation was carried out adopting face-centered central composite design (CCD) with three parameters such as current, pulse-on time, and pulse-off time to ascertain the effects of two sustainable measures, viz., Material removal rate (MRR) and tool wear rate (TWR) the data were collected. An Artificial Neural Network (ANN) model was developed based on data obtained from experiments. Finally, experimental values are compared with the predicted values of ANN and found high prediction accuracy. The advanced model results are used to approximate the responses fairly precisely. The version features a mean coefficient of correlation of 0.99072. Effects uncovered that the projected version is employed for the prediction of the complex EDM process.

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