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A programmable logical control based Smart Machine Controller (SMC) has been developed to control the EDM process. SMC extracts the EDM process variables for rough, semi-finish and finish machining operations from the developed database. The experimental database was created by conducting pilot experimentations after conducting the validation experiments, the SMC was tested on six different dies and mold steel materials with original machine controller and the results show that the MRR and surface finish improved significantly when used with SMC machining parameters. The developed SMC is very effective and highly interactive when compared to the original die-sinking EDM machine controller.

The present study focusses to enhance the wear resistance of electroless Ni-P coatings with the co-deposition of selflubricating nano graphite particles synthesized by mechanical milling using a high energy planetary ball mill. The coatings were developed on an aluminum substrate and nano graphite particles were co-deposited (4 g/l) into the Ni-P matrix using alkaline hypophosphite reduced electroless bath. Changes in properties after heat treatment at 220 °C for 4 h in vacuum have been correlated with morphological and microstructural changes shown by field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) respectively. Energy dispersive analysis of X-ray (EDAX) determined the quality and elemental composition of coatings. Wear resistance, coefficient of friction (pin on disc) and microhardness of electroless Ni-P-graphite nanocomposite coatings were calculated and contrasted with Ni-P coatings. The outcomes reveal that the size of synthesized graphite nanoparticles is 20 nm. Microhardness and wear resistance of electroless coatings have improved after heat treatment. Excellent wear resistance has been observed at the cost of some hardness in Ni-P-graphite nanocomposite coatings over Ni-P coatings.

In this paper, a comparative study was conducted to observe the flow patterns of bulk powder materials through a conical shaped hopper. Hoppers were designed based on Jenike charts and further analyzed using discrete element method. The paper focuses on the flow of micro-sized powder materials and how their area flow pattern changes along with velocity distribution variation with a change in hopper angle. Discrete element method was used to predict the flow patterns for different hopper angles and to study the transition of flow from funnel flow to mass flow. A comparison was made among these hoppers based on their average velocity and average mass flow rate to have an optimum design for mass flow. It was observed that a decrease in hopper angle resulted in an increase in both the mass flow rate and average velocity.

This paper aims to optimize the wire electric discharge machining (WEDM) parameters using multi response surface methodology. Experiments were performed on Nimonic 80A alloy to improve the response variables, namely material removal rate (MRR) and Kerf width (Kw). SEM analysis was carried out on the different samples obtained using different levels of input input parameters, namely, peak current, gap voltage, duty cycle and wire speed to evaluate the quality of machined surface. The influence of these parameters on MRR and Kw were determined using ANOVA. The analysis results revealed that the peak current is the main factor affecting MRR and Kw. The optimized WEDM parameters obtained using multi response function of RSM technique for machining of Nimonic 80A alloy are peak current (57A), gap voltage (90 V), duty cycle (75 µs), and wire speed (7 mm/min) of wire electrode. The characterization results show that high discharge energy results in deep craters with micro craters at low energy levels on the machined surface of the specimen. The results obtained using the proposed methodology would provide sufficient data for enhancing the machining of Nimonic 80A alloy on WEDM.

In this work, electromagnetic microwave energy radiations at 2.45 GHz frequency have been utilized to produce 5 mm thick joints of SS304-SS316. Filler powder mixture of fine nickel powder and epoxy blumer 1450XX was taken as interface slurry between the mating faces of the joints. Nano and micro particle sized nickel powder of 70 nm (powder 1), 20 µm (powder 2) and 50 µm (powder 3) were used during three sets of experiments. Activated charcoal powder was utilized as susceptor medium to initiate the microwave heating process. Scanning electron microscopy results showed that the joints were crack free and homogenous. Elemental spectroscopy was carried out to observe and compare the presence of different elemental composition in the joining region with different filler powders.

The ubiquity of artificial intelligence in the manufacturing domain draws inspiration for the present article. The implementation of a neural network technique is still a difficult and time-consuming effort for the industry. Prediction of machining variables is a considerable issue that needs to be explored for preventive maintenance of the machine structure and to optimize the surface quality. This work aims at predicting response parameters of the dry turning process for Inconel 825 alloy using deep-cryogenic treated tungsten-carbide insert through artificial neural network technique. Process parameters considered in this work were cutting speed, feed and depth of cut, whereas, surface-roughness, tool-wear, and material-removal-rate were taken as the three response parameters.14 types of training functions were compared based upon their error indices searching for the training function which best suits this work. Artificial Neural Network (ANN) model was developed by taking Bayesian regularization back propagation based training function. The response values predicted by the ANN were in very close approximation to the actual experimental value with the mean square error of only 0.0011 μm2, 39.0882 μm2 and 0.0520 cm6/min2in the prediction of surface-roughness, tool-wear, and material-removal-rate of dry turning process of Inconel 825 using treated carbide tool.