Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined Nimonic 80A using abrasive-mixed electro-discharge diamond surface grinding

Abstract

In this paper, a fuzzy logic artificial intelligence technique is delineate to predict the material removal rate (MRR) and average surface roughness (Ra) during abrasive-mixed electro-discharge diamond surface grinding (AMEDDSG) of Nimonic 80A. Though, Nimonic 80A superalloy is extensively used in aerospace and automotive industries due to its high corrosion, fracture toughness, oxidation, and temperature resistance characteristics, being a difficult-to-cut material, its machining is a challenging job. The hybrid machining processes like AMEDDSG can be competently used for machining of Nimonic 80A. The face-centered central composite design is used consummate the experiments and then experimental data are used to establish fuzzy logic Mamdani model to predict the MRR and Ra with respect to changes in the input process parameters viz. wheel RPM, abrasive concentration, pulse current and pulse-on-time. The results of confirmation experiments reveal an agreement between the fuzzy model and experimental results with 93.89 % accuracy implying that the established fuzzy logic model can be precisely used for predicting the performance of the AMEDDSG process. An increase in wheel RPM, pulse current, and pulse-on-time from their low level to high level contributes to increased MRR by 83.89, 71.01, 17.02 %, respectively. Also, an increase in wheel RPM contributes to reduced Ra values by 5.96 %. Abrasive concentration increase from 0 to 4 g/L improves MRR by 24.03 %. The 17.10 % improvement in surface finish is achieved by increasing abrasive concentration from 0 to 8 g/L.