Abstract
The uncertainty of the dynamic change process of expenditure of energy from machine tools seriously hinders the development of an intelligent and green manufacturing industry. In response to the multifaceted impact of identification methods in the process of energy consumption change, the difficulty of extracting high-quality features, and the complexity and nonlinearity of energy consumption curves, an identification method for predicting the energy consumption of machine tools based on integrated models was proposed. Taking CNC milling as an example, the model of energy consumption based on different cutting periods was established, which was then trained and predicted by using the preprocessing signal based on Random forest (RF). Meanwhile, the RF was used to identify the cutting stages and realize the energy consumption classification prediction. The advantage and availability of this method have been demonstrated through practical cases, and it has been verified that this recognition method can accurately predict different operating states and energy consumption of each stage on machine tools.
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