Reducing the energy consumption of machine tool processing has been a consistent concern and research issue in the international manufacturing industry. To achieve energy conservation and emissions reduction in machine tools, an energy consumption model of the machining process must first be established. However, considering the differences in machining equipment, complex energy flow conditions and time-varying load forces, accurate energy consumption of machining process can be difficult to obtain. Against this backdrop, our research proposes a modelling method for processing energy consumption with an integration mechanism and data, that considers the advantages of mechanism analysis modelling and data modelling. Among them, the mechanism analytical model for characterising energy consumption is determined by the dynamic mechanism of the multi-energy source of the machine tool. The data model is built using a support vector machine (SVM) algorithm based on the deviation between the actual results and the theoretical model. Then, a case study is performed to verify the feasibility and practicability of the proposed method. The results demonstrate accurate prediction and quantitative analysis of energy consumption.

At present, the evaluation index for low carbon manufacturing was mainly focused on the relationship between carbon emission and economic benefits or removal volume. But there was rarely comprehensive evaluation index to evaluate the carbon utilisation level of processing. Based on this, this paper presented the carbon utilisation efficiency as the comprehensive evaluation index in low carbon manufacturing. The carbon utilisation efficiency was defined as the ratio of the carbon emission of materials removal to the whole carbon emission in manufacturing process. A carbon utilisation efficiency model was established in milling process, based on flow characteristics and removal mechanism of carbon emission during milling. Then a multi-objective optimisation model was established based on Particle Swarm Optimisation, and the minimum processing time and high carbon utilisation efficiency were set as the optimisation objectives. And the experiment was performed to confirm the undetermined constant in the optimisation model and verify the effectiveness of the optimisation model. The optimised milling parameters were verified to reduce the processing time and improve the carbon utilisation efficiency.

Machine tool is the basic manufacturing equipment in today’s mechanical manufacturing industry. A considerable amount of energy and carbon emission are consumed in machining processes, the realization of sustainable manufacturing of machine tools have become an urgent problem to be solved in the field of industry and academia. Therefore, five types of machine tools were selected for the typical machining processes (turning, milling, planning, grinding and drilling). Then the model of the energy efficiency, carbon efficiency and green degree model were established in this paper which considers the theory and experiment with the resource, energy and emission modeling method. The head frame spindle and head frame box were selected to verify the feasibility and practicability of the proposed model, based on the orthogonal experiment case of the key machining process. In addition, the influence rules of machining parameters were explored and the energy efficiency and green degree of the machine tools were compared. Finally, the corresponding strategies for energy conservation and emission reduction were proposed.