Development of intelligent system for predicting MTR in EDC process using powder metallurgical tools

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Citation

Mussada, E.K. and Patowari, P.K. (2020), "Development of intelligent system for predicting MTR in EDC process using powder metallurgical tools", Grey Systems: Theory and Application, Vol. 10 No. 3, pp. 321-337. https://doi.org/10.1108/GS-01-2020-0006