Machine Learning-Based Prediction of Specific Energy Consumption for Cut-Off Grinding.

Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC-C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.

Model Based on an Effective Material-Removal Rate to Evaluate Specific Energy Consumption in Grinding.

Grinding energy efficiency depends on the appropriate selection of cutting conditions, grinding wheel, and workpiece material. Additionally, the estimation of specific energy consumption is a good indicator to control the consumed energy during the grinding process. Consequently, this study develops a model of material-removal rate to estimate specific energy consumption based on the measurement of active power consumed in a plane surface grinding of C45K with different thermal treatments and AISI 304. This model identifies and evaluates the dissipated power by sliding, ploughing, and chip formation in an industrial-scale grinding process. Furthermore, the instantaneous positions of abrasive grains during cutting are described to study the material-removal rate. The estimation of specific chip-formation energy is similar to that described by other authors on a laboratory scale, which allows to validate the model and experiments. Finally, the results show that the energy consumed by sliding is the main mechanism of energy dissipation in an industrial-scale grinding process, where it is denoted that sliding energy by volume unity decreases as the depth of cut and the speed of the workpiece increase.

Influence of the Regime of Electropulsing-Assisted Machining on the Plastic Deformation of the Layer Being Cut.

In this article, the influence of electropulsing on the machinability of steel S235 and aluminium 6060 has been studied during conventional and electropulsing-assisted turning processes. The machinability indices such as chip compression ratio ξ , shear plane angle ϕ and specific cutting energy (SCE) are investigated by using different cutting parameters such as cutting speed, cutting feed and depth of cut during electrically-assisted turning process. The results and analysis of this work indicated that the electrically-assisted turning process improves the machinability of steel S235, whereas the machinability of aluminium 6060 gets worse. Finally, due to electropluses (EPs), the chip compression ratio ξ increases with the increase in cutting speed during turning of aluminium 6060 and the SCE decreases during turning of steel S235.