Investigation of optimal machining Monel 400 superalloy considering carbon emissions using FEM, regression and ANN methods

Abstract

In this study, the optimal machining parameters for the machining of Monel 400 superalloy considering carbon emission were investigated. Monel 400 stands out among nickel alloys with its resistance to high corrosive environments. First, the Johnson-Cook material model for Monel 400 was developed for FEM simulations. Then, FEM simulations were performed using seven different machining parameters, including cutting and geometric, and validated with experimental results in the literature. The obtained FEM results were trained and predicted by regression and Artificial Neural Network (ANN) analysis. With this study, the Johnson-Cook material model of Monel 400 superalloy and the optimal machining parameters considering carbon emission with different cutting and geometric machining parameters are provided to the literature. As a result, it was determined that the most effective machining parameters in terms of cutting force were 139.04% increase in depth of cut, 74.23% increase in feed and 9.57% decrease in side rake angle. In terms of carbon emission, the most effective machining parameters were found to be −338.9g reduction in depth of cut, −294.3g reduction in feed rate and −171.9g reduction in cutting speed. In terms of training and prediction of FEM results, it was observed that the regression model was 89.19% for cutting force data and 95.39% for carbon emission, while the ANN model was 98.081% for cutting force data and 99.978% for carbon emission. Optimal machining parameters were determined by ANN method. The optimal machining parameters (cutting speed, feed, cutting depth, side rake angle, back rake angle, lead angle and nose radius) of Monel 400 superalloy with the lowest cutting force and carbon emission were determined as 100 m/min, 0.15mm/rev, 0.375 mm, 0°, 5°, 0°, 0°, 0°, 0.4 mm and 100 m/min, 0.2mm/rev, 0.25 mm, −5°, 5°, 0°, 0.8 mm, respectively.