## 1 Introduction  
As the pillar industry supporting the national economy, the manufacturing industry plays an important role in economic globalization and sustainable development. However, while the manufacturing industry is advancing at a rapid pace, excessive energy consumption and large amounts of greenhouse gas emissions are becoming increasingly serious. Determining how to sustainably realize low energy consumption and high efficiency in the manufacturing industry has also become a key issue in the global manufacturing industry. In close relation to this, the improvement of energy management for high-energy-consumption equipment in the manufacturing industry has become the research object of many scholars. As basic pieces of equipment for manufacturing processing systems, CNC lathes have a large number of machine tools, high energy consumption, and an average energy efficiency of less than 30% [1]. Machining industry practitioners are eager to find solutions to reduce the energy consumption of CNC lathe machining. In CNC turning, the thoughtful selection of cutting parameters can not only diminish the energy consumption of the lathe but also significantly enhance the machining surface quality. To optimize energy consumption, the first step is to analyze the functional relationship between the processing energy consumption and cutting parameters of a CNC lathe. [PERSON] et al. [2] established the relationship between the machining input power of a CNC lathe and the material removal rate in the cutting state by analyzing the power change law in the CNC turning process. Subsequently, [PERSON] et al. [3] improved the formula and established a cutting specific energy model that can reflect the relationship between the cutting energy consumption and the material removal rate of a CNC lathe. At present, CNC lathe energy consumption optimization models are established by taking cutting specific energy, the material removal rate, and other factors as the optimization target, and different algorithms are used to select the optimal cutting parameters so as to reduce cutting energy consumption and improve a material's surface quality. For example, [PERSON] et al. [4] examined the impact of energy consumption on non-processing factors such as automatic tool change, common feed, and rapid feed; established a multi-objective mathematical model with cutting energy consumption, processing quality, and processing time as the optimization objectives; and used the non-dominated genetic algorithm (NSGA-II) for multi-objective optimization. [PERSON] et al. [5] established a multi-objective cutting parameter optimization model with 30 CrMnSiNiA steel as the cutting material and surface roughness and material removal rate as the objectives and used the improved multi-objective Harris Hawk algorithm (MOHHO) for optimization. [PERSON] et al. [6] used the response surface method to analyze the effect of cutting parameters on cutting force and cutting temperature to establish mathematical models of cutting force and material removal rate and used a genetic algorithm for multi-objective optimization. [PERSON] et al. [7] used AISI1045 steel as the cutting material to establish a multi-objective mathematical model with cutting specific energy and material surface roughness as the optimization objectives, and the response surface method was used to obtain a regression model of the study variables so as to maximally optimize the energy efficiency and material surface quality. [PERSON] et al. [8] used a blend of grey correlation analysis and response surface methodology to select the optimal machining parameters for turning AISI 304 austenitic stainless steel. This approach led to a notable 66.9% decrease in surface roughness and an 81.46% decrease in specific energy consumption. [PERSON] et al.