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The current research work presents the application of fuzzy logic modeling for electric discharge coating (EDC) process for predicting the material transfer rate (MTR), which has the capability of producing thick and thin films on the conductive substrate material.

Thirty-two real-time experiments were conducted, and fuzzy rules were framed from the inference made from this experimental data. Validating experiments were carried out to check the feasibility of the developed model in prediction.

A fair agreement has been observed between experimental results and the outcomes of fuzzy model. This was supported by a goodness of fit value of 0.917. The values of adjusted R² and standard error were 0.914 and 19.112, respectively.

Current research deals with the prediction of MTR at various parameter grouping conditions, which majorly influence the response parameters. However, other parameters such as quality of the dielectric fluid, flushing pressure and purity of the electrode and work material and so on that influence the response parameters could be further investigated and stand as a future scope of the current work.

MTR is a response parameter that accounts the actual material transfer to the workpiece during the deposition process. This parameter supports/alters the hardness, adhesion, wear resistance and other mechanical properties of the work material. The current modeling work helps to take an optimum decision without conducting large number of experiments at the industrial scale. Due to the nature of fuzzy logic, this method has a potential advantage in dealing with real-time data for various industrial applications.

Developing a fuzzy model for EDC process is not yet addressed, and to attain the economic objective of the process, optimal deposition conditions must be determined, which help the industries to reduce the operation costs. The current study outcomes substantiate the effectiveness of the fuzzy logic in decision-making and prediction of response parameters.

Many engineering applications in this era require new age materials; however, some classic alloys like spring steel are still used in critical applications such as aerospace, defense and automobile. To machine spring steel material, there exist various difficulties such as rapid tool wear rate, the rough surface formation of a workpiece and higher power consumption. The purpose of this paper is to address these issues, various approaches in addition to electrical discharge machines (EDM) are used such as dry EDM (DEDM) and near dry EDM (NDEDM).

This study focuses on these two approaches and their comparative analysis with respect to tool wear during machining of spring steel material. For this study, current, gap voltage, cycle time and dielectric medium pressure are considered input variables. This study shows that the near dry EDM approach yields better results. Hence, the thermo-electrical model for this approach is developed using ANSYS workbench, which is further validated by comparing with experimental results. This thermo-electrical model covers spark radius variation and formation of temperature profile due to electric discharge. Transient thermal analysis is used to simulate the electric discharge machining.

It is observed from this study that discharge environment parameters such as debris concentration and fluid viscosity largely influences the dielectric fluid pressure value. Experimental results revealed that NDEDM yields better results in comparison with DEDM as it shows a 25% lesser tool wear rate in NDEDM.

The range of predicted results and the experimental results are in close agreement, authenticating the model.

This paper aims to present an experimental investigation of improving the surface corrosion resistance of sintered neodymium-iron-boron (NdFeB) magnets by electrical discharge machining (EDM) in different dielectric fluids.

Scanning electron microscope and X-ray diffraction were used to analyze the surface morphology and chemical structure of recast layers formed by EDM using kerosene and distilled water as the dielectric fluids. Polarization scans and electrochemical impedance spectroscopy were applied to investigate the post-machining corrosion resistance.

The test results indicated that the recast layer produced during EDM had amorphous characteristics, and the newly formed amorphous structure could improve the corrosion resistance of the NdFeB material. The corrosion resistance of the recast layer formed in kerosene was better than that formed in distilled water.

Surface corrosion modification of sintered NdFeB magnets by means of electrical discharge with an ordinary copper electrode is proposed in this paper. The layer formed by EDM exhibits different behavior to that of the interior of the bulk material and improves the anti-corrosion performance of NdFeB magnets.

This tribological investigation aims to identify the effect of WS₂ deposition on the Al 6061 surface and optimize the dry sliding conditions to enhance the friction and abrasion wear behavior.

WS₂-deposited Al 6061-T6 surface was considered for this tribological investigation. The design of the experiment was based on the Box–Behnken design of the response surface methodology approach, which is used to evaluate the interaction effect of input parameters on friction coefficient (COF) and specific wear rate (SWR). The abrasive wear behavior of WS₂ deposition against SiC emery sheet was explored through pin-on-disc experimentation by varying applied load (L), sliding velocity (V) and distance (D). Using analysis of variance and regression model, COF and SWR were predicted.

Based on composite desirability criteria, multi-objective optimization was performed to minimize the COF and SWR. The obtained optimal sliding conditions are L = 10 N, V = 2 m/s and D = 949.49 m. The validation test results indicate that the experimental and predicted data are in good conformance. For optimized conditions, worn surface characterization was done using a scanning electron microscope with energy dispersive spectroscopy, and X-ray diffraction analysis was performed to ensure the formation of WS₂ phases on worn-out surfaces. Furthermore, a counter body surface with collected wear debris has been analyzed.

Almost the industries are now focused on a new surface modification technique, which improves the surface and tribological characteristics. This research work specifically relates the tribological effect of WS₂ deposition on an Al 6061-T6 surface through a novel electrical discharge deposition approach and optimizes the dry sliding conditions to improve the frictional and abrasive wear resistance.

Electric discharge drilling (EDD) is used to drill quality microholes on any conductive materials. EDD process parameters play a crucial role in the drilling. Depending upon the material characteristics, the cost of drilling also changes. Therefore, a suitable method is required to control the process parameters and drill quality microholes.

The input process parameters in the present work are peak current (Ip), pulse on-time (Ton) and pulse off-time (Toff). The trials were intended in accordance to central composite face-centered design of response surface methodology (RSM). The output responses, namely drilling rate (DR) and electrode wear ratio (EWR), were converted into a single response, that is, grade using Grey relational analysis (GRA). The grade value is further modeled by regression analysis. The empirical model was figured out using teaching–learning-based optimization (TLBO). The RSM-Grey-TLBO-based multicriteria decision-making (MCDM) is used to investigate the optimized process parameter setting.

The RSM-Grey-TLBO-based MCDM approach suggests that the optimized setting for DR and EWR is Ip: 3A; Ton: 40 µs; Toff: 42 µs. The percentage errors for the predicted and experimental results are 8.1 and 7.5% in DR and EWR, respectively.

The parametric optimization of EDD using RSM-Grey-TLBO-based MCDM approach while machining commercially pure titanium is still underway. Thus, this MCDM approach will give a path to the researchers working in this direction.

The purpose of this study is to prepare a state-of-the-art review on advanced ceramic materials including their fabrication techniques, characteristics, applications and wettability.

This review paper presents the various types of advanced ceramic materials according to their compounding elements, fabrication techniques of advanced ceramic powders as well as their consolidation, their characteristics, applications and wetting properties. Hydrophobic/hydrophilic properties of advanced ceramic materials are described in the paper with their state-of-the-art application areas. Optical properties of fine ceramics with their intrinsic characteristics are also presented within. Special focus is given to the brief description of application-based manipulation of wetting properties of advanced ceramics in the paper.

The study of wetting/hydrophobicity/hydrophilicity of ceramic materials is important by which it can be further modified to achieve the required applications. It also makes some sense that the material should be tested for its wetting properties when it is going to be used in some important applications like biomedical and dental. Also, these advanced ceramics are now often used in the fabrication of filters and membranes to purify liquid/water so the study of wetting characteristics of these materials becomes essential. The optical properties of advanced ceramics are equally making them suitable for many state-of-the-art applications. Dental, medical, imaging and electronics are the few sectors that use advanced ceramics for their optical properties.

This review paper includes various advanced ceramic materials according to their compounding elements, different fabrication techniques of powders and their consolidation, their characteristics, various application area and hydrophobic/hydrophilic properties.

The purpose of this study is to explore the machining characteristics of electrical discharge machining (EDM) when a tool is fabricated using powder metallurgy. Because pure Cu tools obtained using conventional machining pose problems of high tool wear rate, tool oxidation causes loss of characteristics in tool shape.

The research investigation carried out experiments planned through Taguchi’s robust design of experiments and used analysis of variance (ANOVA) to carry out statistical analysis.

It has been found that copper and chromium electrodes give less metal removal rate as compared to the pure Cu tool. Analytical outcomes of ANOVA demonstrated that MRR is notably affected by the variable’s polarity, peak current, pulse on time and electrode type in the machining of EN9 steel with EDM, whereas the variables pulse on time, gap voltage and electrode type have a significant influence on EWR. Furthermore, the process also showed that the use of powder metallurgy tool effectively reduces the value of SR of the machined surface as well as the tool wear rate. The investigation exhibited the possibility of the use of powder metallurgy electrodes to upgrade the machining efficiency of EDM process.

There is no major limitation or implication of this study. However, the composition of the powders used in powder metallurgy for the fabrication of tools needs to be precisely controlled with careful control of process variables during subsequent fabrication of electrodes.

To the best of the authors’ knowledge, this is the first study that investigates the effectiveness of copper and chromium electrodes/tools fabricated by means of powder metallurgy in EDM of EN9 steel. The effectiveness of the tool is assessed in terms of productivity, as well as accuracy measures of MRR and surface roughness of the components in EDM machining.

The purpose of this paper is to find the influence of surface bump texture combination characteristics on friction-wear properties so as to provide a basis for the selection of the bump texture combination scheme on the surface of the roll.

In this paper, six groups of different bump texture combination characteristics and their processing methods are introduced, of which three groups are regular distribution with different spacing and three groups are random distribution with different spacing. Then the effect of bump textures with different spacing, regular and random distribution on friction-wear properties was studied by ring block friction-wear experiments.

The results show that the friction coefficient of random distribution texture surface is lower than that of regular texture surface under the same spacing condition. In the regular distribution, the friction coefficient decreases with the increase of texture spacing. In the random distribution, the friction coefficient increases at first and then decreases with the increase of texture spacing. In addition, the wear resistance of textured surface is significantly higher than that of smooth surface because of the higher microhardness of the textured area. The attenuation ratio of textured surface roughness decreases with the increase of the distance between adjacent textures.

At present, the research on roller surface friction-wear is mainly based on the change of the overall surface roughness. However, there are few reports on the influence of the combination characteristics of laser bump texture on friction-wear from the microscopic scale.

The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

The purpose of this study is to examine the postprocessed wire tool surface using scanning electron microscopy and find out the streamlined conditions of input process variables using multi-objective optimization techniques to get minimum wire wear values.

A federated mode of response surface methodology (RSM) and artificial neural network (ANN) is used to optimize the process variables during the machining of a nickel-based superalloy.

The study explores that with the rise in spark-off time and spark gap voltage, the rate of wire tool consumption also escalates.

Most of the researchers used the RSM technique for the optimization of process variables. The RSM generates a second-order regression model during the modeling and optimization of a manufacturing process whose major limitation is to fit the collected data to a second-order polynomial. The leading edge of ANN on the RSM is that it has comprehensive approximation capability, i.e. it can approximate virtually all types of nonlinear functions, including quadratic functions also.