Development of Predictive Models for Envisaging the Performance Measures in Electro Chemical Machining of Nickel Alloy for Aerospace applications

The process of electrochemical machining, often known as ECM, is capable of effectively shaping complicated structures in materials that conduct electricity, independent of the materials' level of hardness hence especially used for automobile and aerospace applications. As a result of the demand for high-quality products and the desire for rapid design changes, the manner in which decisions are made in the manufacturing industry has become increasingly contentious. With the assistance of regression analysis, this study proposes the development of predictive models for the purpose of forecasting the performance measures in electrochemical machining of Inconel 800HT. The trials are designed in accordance with Taguchi's principles, and a multiple regression model is utilized in order to derive the mathematical equations. Taguchi's method can be applied as a methodology for single objective optimization in order to attain the most optimal combination of process parameters for the purpose of optimizing the rate at which material is removed. For the purpose of determining the importance of process factors that have an effect on the performance measures, analysis of variance (ANOVA) is utilized. When compared to the models that are now in use, the technique that has been provided for predicting the intended performance measures is not only more flexible, proficient, and exact, but it also provides enhanced monitoring capabilities. In the end, the revised models are then checked for accuracy. There is a good correlation between the experimental data and the expected outcomes, which are formed from the models that have been formulated.