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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.

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.