Intelligent Systems to Optimize and Predict Machining Performance of Inconel 825 Alloy

Abstract

Intelligent models are showing an uprise in industry and academia to optimize the system’s outcome and adaptability to predict challenges. In machining, there is difficulty of unpredictability to the part performance especially in super alloys. The aim of this research is to propose an intelligent machining model using contemporary techniques, namely, combinative distance-based assessment (CODAS), artificial neural network (ANN), adaptive neuro-fuzzy inference systems, and particle swarm optimization (ANFIS-PSO) approach for minimizing resultant force, specific cutting energy, and maximizing metal removal rate. Resultant force response has shown to be affected by feed rate and cutting speed with a contribution of 54.72% and 41.67%, respectively. Feed rate and depth of cut were statistically significant on metal removal rate contributing with the same value of 38.88%. Specific cutting energy response resulted to be statistically significant toward feed rate with 43.04% contribution and 47.81% contribution by depth of cut. For the CODAS approach, the optimum parameters are cutting speed of 70 m/min, feed of 0.33 mm/rev, and depth of cut of 0.6 mm for the seventh experiment. The estimated values predicted by the ANN and ANFIS method were close to the measured values compared to the regression model. The ANFIS model performed better than the ANN model for predicting turning of the Inconel 825 alloy. As per quantitative analysis, these two models are reliable and robust, and their potential as better forecasting tools can be used for hard-to-machine materials. For hybrid ANFIS-PSO, the optimum parameters for minimizing resulting force were (82, 0.11, 0.15), for minimizing specific cutting energy (45, 0.44 and 0.6) and maximizing metal removal rate (101, 0.43, 0.54). The hybrid model ANFIS-PSO has proven to be a better approach and has good computational efficiency and a lower discrepancy in assessment.