In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

In electrical discharge machining (EDM), the machined surface quality can be affected by the excessive temperature generation during the machining process. To achieve a longer life of the finished part, the machined surface quality plays a key role in maintaining its overall integrity. Surface roughness is an important quality evaluation of a material’s surface that has considerable influence on mechanical performance of the material. Herein, a sintered cermet tooltip with 75% copper and 25% titanium carbide was used as tool electrode for processing H13 steel. The experiments have been performed to investigate the effects of EDM parameters on the machined surface roughness. The findings show that, as the pulse current, pulse length, and pulse interval are increased, the surface roughness tends to rise. The most significant determinant for surface roughness was found to be pulse current. A semi-empirical surface roughness model was created using the characteristics of the EDM technique. Buckingham’s theorem was used to develop a semi-empirical surface roughness prediction model. The semi-empirical model’s predictions were in good agreement with the experimental studies, and the built empirical model based on physical features of the cermet tooltip was tested using dimensional analysis.

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses.

In Electrical discharge machining operations, tool cost has the maximum contribution in the final cost. Copper is chiefly used tool material for electrical discharge machining process. Although, copper has very high thermal and electrical conductivity but it disintegrates more, especially during the machining of hardened materials due to its lower hardness. Therefore, an effort has been made to fabricate a sintered tool for electrical discharge machining operations and further evaluated it for wear characteristics. This article describes a study on electrical discharge machining using a sintered tool tip to improve wear resistance. In this work, three batches of tool tips using copper, titanium carbide, silicon carbide and graphite are fabricated by powder metallurgy. Initial estimation of the electrodes has been done by evaluating electrical and mechanical properties. Depending upon the desirable properties for the electrode, a best tool tip has been suggested and utilized for electrical discharge machining on hardened EN31 steel. Electrode wear rate, change in tool shape, volume of material removed, surface roughness and surface integrity has been evaluated in this study. The proficiency of the cermet tool tip has been compared with copper tool tip. It has been found that during machining with the cermet tool tip, the electrode wear rate and change in roundness of tool tip decreased up to 14.82% and 20% respectively as compared to copper tool tip. The decrease in change in roundness during machining also signifies the better shape retention of cermet tool tip. The volume of material removal is higher when cermet tool tip is used for the same set of process parameters. From the study of SEM micrographs, it can be observed that the recast layer is less and there are fewer surface cracks on the surface machined by cermet tooltip. The formation of micropores and pock marks are more predominant on workpieces machined by cermet tooltip. The width of cracks is lesser when cermet tool tip is used in comparison to copper tool tip.

Detonation gun spray coatings are known for their high density, high bond strength and better wear/corrosion resistance. In this study, detonation gun spray technique was used to deposit WC-12Co and Cr3C2-25NiCr coating on boiler steel ASTM SAE213 T22 steel. The specimens with and without coating were subjected to cyclic oxidation at 500oC for 50 cycles in air environment to ascertain usefulness of the coating at the said temperature. The weight change technique was used to establish the kinetics of oxidation/corrosion. X-ray diffraction (XRD) and FE-SEM/EDS techniques were used to analyze the exposed samples. The behavior of the uncoated and coated samples supported by various characterization techniques in detail have been reported in the present study.