The increasing demand for energy is leading to global depletion of fossil fuels and growing environmental pressures, which are issues that need to be addressed. Machine tools are basic energy-consuming equipment in manufacturing systems. However, existing theoretical models ignore tool wear as well as workpiece material properties. This makes it difficult to further improve the accuracy of the model. Therefore, this study begins with the point of view of energy dissipation in the metal material removal process. A milling power model for computer numerical control (CNC) machines, considering tool wear and workpiece material properties during machining, is established. At the same time, milling is taken as the research object. A multi-objective cutting parameter optimization model is established to ensure the surface quality of the workpiece. In addition, the cutting energy consumption is taken into account in the developed models. Based on the multi-objective manta ray foraging optimization algorithm (MOMRFO), the Pareto-optimal solution set under multiple cutting conditions is solved. Finally, the experimental results of optimized parameters are compared with empirical parameters. The average prediction accuracy of the proposed energy consumption prediction model is above 91%. The experiments show that machining quality improves by optimizing the cutting parameters, with SEC, MRR, and Ra increasing by more than 44%, 53%, and 38%, respectively. The goals of reducing energy consumption and increasing productivity are achieved.

As an indispensable piece of equipment in the manufacturing industry, the machine tool is low-energy-efficiency and high-energy-consumption in operation. Therefore, it is urgent to establish a cutting energy consumption model to guide production and reduce the energy consumption of the machining process. In this paper, the AlTiCrN-coated cutting tool is taken as the object of study, and the cutting energy consumption model is established. The cutting energy consumption model is composed of a machining time model and a cutting power model. The cutting power model can be divided into the shear deformation power model of the workpiece, the friction power model of the flank surface and the friction power model of the rake surface. The influence of the edge shape is taken into account in the establishment of the friction power model of the flank surface. The machining time model considering the S-type acceleration and deceleration stage is established. The accuracy of the model was verified by experiments. Experimental results show that the model has high accuracy. The Taguchi method was used to carry out the numerical experiment with the cutting energy consumption of the machine tool as the response. The influences of cutting parameters on energy consumption are analyzed. Cutting width is the most important factor, followed by cutting depth, then feed rate and spindle speed. The physical principle of the influence of cutting parameters on cutting energy consumption is revealed.

Accurate predictions of CNC machine tool energy consumption are crucial for sustainable manufacturing but remain challenging due to complex nonlinear dynamics. This paper proposes a mechanism–data hybrid framework combining physical modeling with an Attention–LSTM network. Unlike existing parallel hybrid models, this approach embeds the mechanism model’s output as a strong prior into the neural network, explicitly guiding the learning of nonlinear residuals. First, a hierarchical decoupled mechanism model is constructed to establish the physical baseline of energy consumption. Second, an Attention–LSTM network is designed to compensate for dynamic errors caused by tool wear and thermal variations. Finally, experimental validation on a three-axis CNC milling machine demonstrates that the proposed method significantly outperforms meaningful baselines, achieving a Root Mean Square Error (RMSE) of 0.0610 and an R² of 0.9936. The framework provides a robust, physically interpretable solution for energy monitoring in intelligent manufacturing systems.

Aiming at the problem of non-uniform and non-universal evaluation criteria of machine tools’ carbon emissions in the whole life-cycle analysis, an evaluation method of life-cycle carbon-emission analysis of machine tool based on declared unit was put forward by analyzing and summarizing the existing carbon emission evaluation models. A universal evaluation system for machine-tool life-cycle carbon-emission analysis was first established, and an appropriate declared unit was then selected according to industry characteristics and machine-tool types. Subsequently, an information-flow-based iERWC boundary division method was proposed to support data collection and carbon-emission calculation across five life-cycle stages. To better reflect carbon emissions in the phase of application, the life-cycle inventory incorporates the use of coated cutters, including the associated cutters’ consumption and replacement demands. Two heavy duty floor-milling and boring machine tools produced by Qiqihar No. 2 Machine Tool (Group) Co., Ltd. Were taken as examples to calculate and evaluate life-cycle carbon-emission analysis of machine tools, and the uncertainty analysis was carried out; the possible influencing factors were pointed out to ensure the comprehensive carbon-emission assessment of the whole life cycle.