Carbon emission in manufacturing processes: modeling and evaluation

Abstract

Sustainable production depends on the optimization of manufacturing processes. The assessment of carbon emissions in manufacturing is crucial for achieving sustainability. However, a comprehensive systematic framework to reflect the carbon emission regularity of manufacturing processes is currently lacking. This study focuses on the modeling and evaluation of carbon emissions by considering machining processes and multiple factors. First, carbon emission models for machining processes, such as turning, milling, and drilling, are systematically summarized by considering power consumption. Second, the influence of system parameters on carbon emissions is analyzed. Results show that cutting depth exerts a substantial effect on carbon emissions, and material removal rate has minimal influence. Last, the emission reduction mechanism and performance of novel sustainable machining processes are examined to contribute to carbon emission reduction. This study helps in systematically understanding carbon emissions in manufacturing processes, providing support for the further development of sustainable manufacturing.

Graphical abstract

Keywords

sustainable production / carbon emission / manufacturing process / power consumption / novel machining processes

1 Introduction

Since the Industrial Revolution, production activities have been causing severe environmental pollution and the greenhouse effect. Sustainable production and carbon-neutral strategies have been proposed to improve the environment [1]. Carbon reduction is one of the key components of sustainable production. Carbon emission reduction can minimize the negative influence on the environment and reduce the rate of global warming, thereby creating a favorable environment for future sustainable production and development. Carbon emission is an umbrella term and synonymous with greenhouse gas emission. Greenhouse gases are constituents in the atmosphere that facilitate the greenhouse effect. As shown in Fig.1, greenhouse gases primarily include carbon dioxide (CO₂), methane, and nitrous oxide [2]. Among these gases, CO₂ is the most prevalent, so the total amount of carbon emissions can be measured in terms of CO₂ equivalents [3].

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) indicates that the primary cause of global warming is human activity, particularly industry activity [4]. The total global CO₂ emission from industry activities decreased from 10.02 billion tons in 2019 to 9.77 billion tons in 2020; then, it rose to 10.33 billion tons in 2021 and to 10.44 billion tons in 2022 [5]. As shown in Fig.1, the industrial sector accounts for 17% of global CO₂ emissions and 37% of the total energy consumption [6,7]. The manufacturing sector accounts for approximately 29% of the total direct CO₂ emissions from the industrial sector and 90% of the total industrial energy consumption [8,9]. As concerns about climate change intensify, manufacturing, a major part of the industrial sector, is under increasing pressure to reduce its carbon emissions [10]. Against the backdrop of addressing global warming, countries worldwide have reached a consensus regarding carbon emission reduction [11]. Policy interventions, carbon emission trading mechanisms, and carbon taxation mechanisms are the main approaches adopted by countries and regions to achieve emission reduction goals [12].

As shown in Fig.2, various countries have introduced numerous policies to reduce carbon emissions. For instance, in 1992, nations signed the United Nations Framework Convention on Climate Change to reduce global CO₂ emissions [13]. The Kyoto Protocol, which sets emission reduction targets for parties to the agreement, was signed in Kyoto, Japan, in 1997 [14]. The 2003 UK Energy White Paper titled Our Energy Future: Creating a Low-Carbon Economy introduced the concept of a low-carbon economy. In 2004, China approved the Medium and Long-term Special Plan for Energy Conservation, which promotes energy conservation and efficiency across society, thus accelerating the creation of an energy-saving society. The 2009 United Nations Copenhagen Accord imposed considerable pressure on countries to undertake emission reduction obligations, making energy conservation and emission reduction more than just slogans [15]. In the same year, the United States House of Representatives passed the American Clean Energy and Security Act of 2009, which primarily aims to limit carbon emissions. The 2015 United Nations Paris Climate Conference resulted in the Paris Agreement, which seeks to balance climate change mitigation and economic development [16]. In 2020, the European Union implemented the European Climate Law that sets emission reduction targets for 2030. In 2021, China released the Action Plan for Carbon Dioxide Peaking Before 2030, which emphasizes that economic and social development should be based on the efficient use of resources and green, low-carbon development. On May 16, 2023, the European Union officially announced the Regulation Establishing a Carbon Border Adjustment Mechanism (CBAM) and clarified in detail the scope of CBAM, the reporting requirements, and the carbon emission calculations. These policies illustrate the increasing global attention to the issue of carbon emissions [17].

As one of the most important processes in manufacturing, mechanical machining plays a crucial role in emission reduction and energy saving in the manufacturing sector [18,19]. Reducing energy consumption and carbon emissions during the manufacturing process is essential for societal sustainability [20,21]. Carbon emissions from machining include direct carbon emissions, such as fuel combustion and chemical reaction, as well as indirect carbon emissions, such as material consumption, energy consumption, and waste disposal. The proportion of direct carbon emissions is small and can be ignored. Therefore, this study mainly synthesizes the models and mechanisms of indirect carbon emissions.

The first step in reducing carbon emissions in mechanical machining is to analyze the factors influencing carbon emissions during the machining process [22]. The establishment of carbon emission models for mechanical machining can provide a comprehensive understanding of the sources and influencing factors of carbon emissions during the machining process. These models can map the relationship between carbon emissions and factors, such as workpiece material, relevant processes, and cutting parameters, thereby helping implement strategies to reduce carbon emissions during machining. Establishing a low-carbon processing model; simulating and analyzing the dynamic behavior of resources, energy consumption, and environmental emissions in the production process of parts; quantitatively evaluating and identifying high-emission processing units; and performing targeted low-carbon optimization research can help machinery manufacturers improve the utilization rate of energy and materials and reduce CO₂ emissions [23]. However, at present, machining carbon emissions involve complex coupled interactions of multiple variables and parameters, making the mapping between them and related influencing factors challenging.

This study provides a detailed overview of the machining carbon emission model from three aspects: energy consumption, material consumption, and waste disposal. On the basis of the model, the effects of cutting parameters, tool parameters, workpiece parameters, and other factors on carbon emissions are summarized. In addition, in consideration of the shortcomings of traditional machining that uses a large amount of cutting fluid, this study comparatively analyzes the carbon emissions of novel processes, such as dry machining, cryogenic machining, and minimum quantity lubrication (MQL).

2 Carbon emissions in manufacturing

As shown in Fig.3, the number of publications related to energy and carbon emissions has gradually increased in recent years, particularly after 2013 when a rapid surge in relevant research occurred. Furthermore, carbon emissions from machining processes have garnered considerable attention in developed and developing countries, particularly in China. Manufacturing, as a crucial component of the modern industry, is essential in minimizing resource consumption and carbon emissions during the manufacturing process to alleviate the greenhouse effect. Low-carbon manufacturing has been proposed by scholars to reduce carbon emissions from the manufacturing industry.

As shown in Fig.4(a), Ball et al. [24] introduced the concept of zero-carbon manufacturing. Cao et al. [25] summarized the definition of low-carbon manufacturing as follows: “low-carbon manufacturing is a sustainable manufacturing model that comprehensively considers energy consumption and carbon emissions throughout the entire lifecycle of a product; its goal is to achieve low energy consumption, low emissions, and low pollution during the production, manufacturing, and usage processes of products”. Li et al. [26] systematically discussed the connotation of low-carbon manufacturing and the theoretical system of manufacturing and proposed a low-carbon manufacturing technology path based on new-generation information technologies, such as industrial Internet and big data, for China’s demand for carbon peaking and carbon neutrality. Low-carbon manufacturing integrates the basic principles of lifecycle analysis and comprehensive consideration of resource and environmental efficiency, and it falls within the realm of green, sustainable manufacturing. The main focus of low-carbon manufacturing is the characteristic of carbon emission reduction. Low-carbon manufacturing can be described as the process of minimizing carbon emissions from the system source and during the manufacturing process [27].

Carbon emission models for machining processes must be established to achieve low-carbon manufacturing. As shown in Fig.4(b), scholars have developed corresponding carbon emission models for various machining processes. Additionally, energy efficiency, being a crucial aspect of carbon emissions in machining, has been extensively studied by researchers, as indicated in Fig.4(c). For example, Huang et al. [35] constructed a parametric model that links carbon emissions with laser welding parameters and elucidated the trade-off relationship between carbon emissions and manufacturing value added. Lin et al. [36] proposed a method to quantify carbon emissions throughout the entire turning process in a dry cutting environment and developed a carbon emission model based on the generalized boundary of the machining system. Jiang et al. [37] built a carbon emission model for the laser rapid prototyping manufacturing process. Building on this foundation, they formulated a multi-objective parameter optimization model that targets carbon emissions, powder utilization efficiency, and cladding quality. Zhang et al. [38] studied a carbon footprint optimization control method based on dynamic programming to minimize carbon emissions during the machining process. Meanwhile, Yin et al. [39] introduced a process planning method that integrates economic and environmental factors through a carbon emission function model. This method generates green and economical process plans, and its feasibility was validated through examples. Cao et al. [40] established a machine tool lifecycle carbon emission model and proposed a quantification method to characterize the carbon emissions of machine tool lifecycles. Sihag and Sangwan [41] proposed an improved micro-level analysis of energy and carbon emissions in value stream mapping for each activity during the machining process. Using milling processes as an example, they demonstrated that the proposed method could optimize the energy efficiency, time efficiency, and carbon emissions of the machining process. In addition, Cao and Li [42] introduced a dynamic simulation method for carbon emissions that considers production disturbances as one of the key factors affecting the carbon emission performance of production systems. Song et al. [43] integrated the principles of life cycle assessment into the carbon emission assessment of machine tool products. They conducted an inventory analysis and calculated carbon emissions during all lifecycle stages, including raw material preparation, product manufacturing, use and maintenance, and transportation.

3 Modeling of carbon emissions in the machining process

3.1 Model frame and content

Mechanical machining is a material removal process during which a large amount of resources is consumed; it generates various waste and emissions that substantially affect the environment [44]. As shown in Fig.5, the machining boundary employs a blank, machine tool, cutting fluid, and other materials. Electrical energy is inputted into the machine tool, and after the cutting process, chips, waste liquid, waste tools, and other materials are outputted. The cutting process also emits a large amount of heat. On this basis, carbon emissions from machining processes can be grouped into three major categories: carbon emissions from energy consumption, carbon emissions from material consumption, and carbon emissions from waste disposal [45].

The machining carbon emission categories are shown in Fig.6. Carbon emissions from material consumption include emissions associated with cutting fluid consumption, lubricating oil consumption, tool wear, and fixture wear. Carbon emissions from waste disposal primarily consist of emissions related to the disposal of used tools and cutting fluids, and emissions from chip recycling processes. Carbon emissions from energy consumption encompass dynamic energy usage for cutting, feeding, and other operations, along with the energy consumption inherent in each component of the machine tool. Fig.6 also illustrates that total carbon emissions are influenced by a combination of cutting parameters, tools, lubrication methods, and workpieces. As presented in Fig.6, carbon emissions from energy consumption are primarily influenced by cutting parameters, such as spindle speed, cutting depth, and feed rate, as well as lubrication methods. Carbon emissions from material consumption are mainly related to workpiece materials, gross size, cutting fluid-related parameters, and lubrication methods. Carbon emissions from waste disposal are primarily associated with the workpiece material and gross size. Notably, lubrication methods can substantially reduce carbon emissions during the machining process by minimizing or eliminating the use of cutting fluids. On this basis, researchers have developed innovative processes, such as cryogenic machining, minimal quantity lubrication, and cryogenic quantity minimal lubrication, which are discussed in Section 5. For ease of calculation, the consumption and recycling processing of tools and cutting fluids are calculated together, and carbon emissions from waste disposal only consider carbon emissions from chip recycling. The total carbon emission model is as follows:

(1)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=& {\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}+{\mathrm{C}\mathrm{E}}_{\mathrm{w}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{e}}\\ =& {\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{o}\mathrm{i}\mathrm{l}}+{\mathrm{C}\mathrm{E}}_k+{\mathrm{C}\mathrm{E}}_j+{\mathrm{C}\mathrm{E}}_f.\end{array}

3.2 Electricity consumption component

The overall energy consumption of computer numerical control (CNC) machine tools is a highly complex system, as depicted in Fig.7. The total energy consumption of machine tools can be viewed as the sum of the energy consumed by various subsystems [46]. Modern machine tools primarily use electricity as the main power source. The most power-consuming activities of machine tools are spindle rotation and servo-driven axis movements [47], which depend on the cutting load. Other energy requirements come from hydraulic devices, oil pumps, cooling systems, and other auxiliary devices, such as controller units and fans [48]. The multi-source nature of energy consumption and the energy loss characteristics of each device contribute to the complexity of energy consumption in machine tools [33].

The total carbon emissions from electricity consumption are calculated as Eq. (2), where F_e represents the carbon emission factor for electricity. Electricity consumption in machine tool machining primarily includes the following components: idle power consumption of the machine tool, cutting power consumption, additional load power consumption, feed power consumption, power consumption of the cooling lubrication system, power consumption of the hydraulic system, and power consumption of other auxiliary systems (e.g., tool change, lighting, display, and chip removal systems). Electricity consumption is determined by the power of the moving parts and the duration of motion [49]. Power is determined by the machining parameters, and understanding the key machining parameters will help improve energy efficiency [50]:

(2){\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}=(E_u+E_{\mathrm{c}} +E_{\mathrm{a}}+E_{\mathrm{f}}+E_{\mathrm{c}\mathrm{l}}+E_{\mathrm{h}} +E_i)\cdot F_{\mathrm{e}}.

(1) Idle

Idle power consumption of a machine tool, calculated in Eq. (3), refers to the electricity consumed by the motor when it operates without a load; it primarily comprises standby power consumption and air cutting power consumption [51]. It is the energy consumed during machine positioning, tool change, spindle acceleration and deceleration, feed and withdrawal, and other procedures that do not directly relate to workpiece cutting.

(3)E_u= P_u\cdot t_u= (P_{u0}+A_{1}n+A_2{n}^2)\cdot t_u,

where P_u (kW) refers to idle power, P_u0 (kW) is the minimum idle power of the machine tool (standby power), A₁ and A₂ are coefficients related to the spindle speed of the machine tool, t_u is the idle time (min), and n is the spindle speed (r/min).

(2) Cutting

During various machining processes, excess material from the workpiece is removed by the cutting tool, and the electrical energy consumed during this process is the cutting power consumption. As indicated in Eq. (4), the magnitude of cutting energy consumption is related to the machine power during the cutting process and the corresponding cutting time [52].

(4)E_{\mathrm{c}}=P_{\mathrm{c}}\cdot t_{\mathrm{c}}=F_{\mathrm{c}}\cdot v_{\mathrm{c}}\cdot t_{\mathrm{c}},

where P_c (kW) is the cutting power, t_c (min) is the cutting time, F_c (N) is the main cutting force, and v_c (m/min) is the cutting speed.

On the basis of the material removal rate (MRR) cutting power model in Eq. (5), the specific energy consumption (SEC) for machining is defined as the energy consumption required to remove one unit volume of material per unit time (kW/mm³) [32]. The introduction of SEC simplifies the assessment of energy consumption, and it can be modeled as the inverse function of the MRR, as shown in Eq. (6).

(5)P_{\mathrm{c}}=\mathrm{S}\mathrm{E}\mathrm{C}\cdot \mathrm{M}\mathrm{R}\mathrm{R}=C_0+C_1\cdot \mathrm{M}\mathrm{R}\mathrm{R}+C_2\cdot n,

(6)\mathrm{S}\mathrm{E}\mathrm{C}=C_0+\frac{C_1}{\mathrm{M}\mathrm{R}\mathrm{R}},

where C₀, C₁, and C₂ are inherent coefficients of the machine tool.

The methods for the calculation of cutting time, milling force F_c1, and turning force F_c2 in Eq. (4) are given as Eqs. (7)–(9), respectively [53,54]:

(7)t_{\mathrm{c}}=\frac{V}{a_{\mathrm{p}}{a}_{\mathrm{w}}{f}_{z}nz},

(8)F_{\mathrm{c}1}=C_{\mathit{F}\mathit{Z}}\cdot a_{\mathrm{p}}^{x_F}\cdot f_z^{y_F}\cdot d_0^{z_F}\cdot Z\cdot a_{\mathrm{w}}^{{\alpha }_F} \cdot K_F,

(9)F_{\mathrm{c}2}=C_{\mathit{F}\mathit{Z}}\cdot a_{\mathrm{p}}^{x_F}\cdot f_{\mathrm{c}}^{y_F} \cdot v_{\mathrm{c}}^{n_F}\cdot K_F,

where V (mm³) represents the material removal volume; a_p (mm) is the cutting depth; a_w (mm) is the cutting width; f_z (mm) is the feed per tooth; z is the number of teeth of the milling cutter; d₀ (mm) is the diameter of the milling cutter; C_FZ is the corresponding coefficient; x_F, y_F, z_F, and α_F are constants determined by machining conditions, workpiece material, and tool material; f_c is the turning feed rate; and K_F is the correction coefficient.

Grinding power and grinding force (F_t) are given by Eqs. (10)–(12) [55]:

(10)P_{\mathrm{c}}=F_{\mathrm{t}} v_{\mathrm{s}},

(11)F_{\mathrm{t}}=F_{\mathrm{p}} a_{\mathrm{g}}^x{f}_v^y{v}_{\mathrm{s}}^z(\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}),

(12)F_{\mathrm{t}}=F_{\mathrm{p}} a_{\mathrm{g}}^x{f}_{\mathrm{g}}^y{f}_v^z(\mathrm{c}\mathrm{y}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}),

where F_p, x, y, and z are coefficients and indices related to the grinding process; a_g (mm) is the grinding depth; f_v (mm/min) is the feed rate; v_s (mm/s) is the grinding speed; and f_g (mm) is the grinding feed rate.

The drilling power model is given by Eq. (13) [56]:

(13)P_{\mathrm{c}}=f_v\cdot \int \mathrm{d}{F}_z(z)+\int v(z)\cdot \mathrm{d}{F}_{\mathrm{t}}(z) ,

where F_z(z) (N) is the cutting force in the Z-axis direction, F_t(z) (N) is the cutting force in the X-axis direction, and v(z) (m/min) is the drilling speed.

(3) Additional load

Additional load power consumption refers to the power of mechanical frictional losses that increase because of the load during cutting operations on the machine tool, as shown in Eq. (14).

(14)E_{\mathrm{a}}=P_{\mathrm{a}} t_{\mathrm{c}}=b_{\mathrm{m}} P_{\mathrm{c}} t_{\mathrm{c}},

where P_a (kW) is the additional load power and b_m is the coefficient of additional load losses.

(4) Feed power

Feed power consumption refers to the total energy consumption of the spindle movement in the X, Y, and Z directions. Feed power is approximately linearly related to the feed rate, as shown in Eq. (16) [57].

(15)E_{\mathrm{f}}={\int }_{t_{1X}}^{t_{2X}}{p}_X\mathrm{d}t +{\int }_{t_{1Y}}^{t_{2Y}}{p}_Y\mathrm{d}t +{\int }_{t_{1Z}}^{t_{2Z}}{p}_Z\mathrm{d}t ,

(16)P_F=C_{\epsilon }+ K_{\epsilon }{f}_v,

where p_X, p_Y, and p_Z (kW) are the feed power in the X, Y, and Z axes, respectively; t_1X and t_2X are the start and end times of the X-axis feed motion, respectively; t_1Y and t_2Y are the start and end times of the Y-axis feed motion, respectively; t_1Z and t_2Z are the start and end times of the Z-axis feed motion, respectively; and K_ε and C_ε are specific coefficients of the motor.

(5) Cooling lubrication

As indicated in Eq. (17), when the machine tool’s cooling and lubrication system is in operation, the power consumption of the cooling motor and lubrication pump remains constant. Their electricity consumption depends on the operating time. Here, the switching function s_i(t) is introduced to indicate whether the motor or system is in operation.

(17)E_{\mathrm{c}\mathrm{l}}=\sum _{i=1}^n{s}_i(t )\cdot p_i\cdot t_i,

where n is the number of motors in the cooling and lubrication system, P_i (kW) is the motor output power, and t_i (h) is the motor operating time.

(6) Hydraulic system

The power consumption of the hydraulic system mainly comes from the electrical energy consumed by the movement of tools and worktables, as shown in Eq. (18):

(18)E_{\mathrm{h}}=\sum _{q=1}^m{s}_q(t)\cdot p_q\cdot t_q,

where m is the number of motion points in the hydraulic system, p_q (kW) is the power consumed at the qth point of the system, and t_q is the working time at the qth point of the system.

(7) Other auxiliary systems

The power consumption of other auxiliary systems mainly includes the energy consumption of lighting, tool changing, control, and other systems. The calculation method is given as Eq. (19).

(19)E_i=\sum _{j=1}^k{s}_j(t )\cdot p_j\cdot t_j,

where k is the number of energy-consuming subsystems, p_j (kW) is the power of the jth energy-consuming subsystem, and t_j (h) is the operating time of the jth energy-consuming subsystem.

3.3 Material consumption component

(1) Cutting fluids

In mechanical machining processes, the high temperatures generated in the cutting zone can lead to rapid tool wear, decreased surface integrity, and reduced dimensional accuracy [58]. Therefore, cutting fluids are necessary in most machining processes. Cutting fluids come in various types but are mainly classified into two categories: water soluble (aqueous-based) and non-water soluble (oil-based) [59]. Carbon emissions from cutting fluids mainly include emissions caused by the preparation of pure mineral oil and the disposal of cutting fluid waste [60]. During the cutting process, cutting fluids are typically replaced at fixed intervals [61,62]. Given that the quantity of cutting fluid used cannot be determined, and quantitative analysis cannot be conducted during use, quantitative analysis is performed based on the ratio of usage time to the replacement interval [63]. The calculation method is given as Eq. (20).

(20){\mathrm{C}\mathrm{E}}_{\mathrm{c}}=\frac{1}{T_{\mathrm{g}}}\cdot t_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}\cdot B_{\mathrm{c}} \cdot (\rho \cdot F_{q1}+F_{q2}),

where T_g is the replacement interval, B_c (L) is the volume of cutting fluid used during the machining process, ρ (kg/cm³) is the concentration of the cutting fluid, and F_q1,F_q2 (kgCO₂/L) are the carbon emission factors during the machine tool processing stage and the waste fluid treatment stage, respectively.

Gear hobbing is also important in the mechanical manufacturing industry, and carbon reduction is essential for the sustainable development of gear processing [64]. For the carbon emissions from cutting fluids in gear hobbing, an apportionment calculation method is used; this method distributes the carbon emissions of the cutting fluid evenly to each gear processed. It calculates the carbon emissions of the cutting fluid for machining one gear [65], as shown in Eq. (21).

(21){\mathrm{C}\mathrm{E}}_{\mathrm{c}}=\frac{1}{n-x}\sum _{i\in n}\frac{t_i}{T_{\mathrm{c}}}\left(\frac{11}{3}{L}_{\mathrm{c}}{E}_{\delta }{E}_{\gamma }+L_{\mathrm{w}}{F}_{q2}\right),

where T_c is the effective recycling period of the coolant in gear hobbing, L_c is the amount of coolant recycled in the gear hobbing machine, L_w is the volume of cutting fluid wasted during the gear hobbing process, E_δ is the energy content of mineral oil, and E_γ is the default carbon content of mineral oil.

(2) Lubricant

Many parts of the machine tool require regular maintenance and lubrication with lubricating oil to reduce wear on the machine tool components. The use of lubricating oil also results in carbon emissions. The calculation of carbon emissions from lubricating oil is given as Eq. (22) [29].

(22){\mathrm{C}\mathrm{E}}_{\mathrm{o}\mathrm{i}\mathrm{l}}=\frac{T_{\mathrm{s}}}{T_{\mathrm{o}\mathrm{i}\mathrm{l}}}\cdot F_{\mathrm{o}\mathrm{i}\mathrm{l}}\cdot Q_{\mathrm{o}\mathrm{i}\mathrm{l}},

where T_s (min) is the spindle rotation time, T_oil (min) is the lubricating oil replacement interval, F_oil (kgCO₂/L) is the carbon emission factor of lubricating oil, and Q_oil (L) is the volume of lubricating oil used.

(3) Tools

Tool wear substantially affects a tool’s lifespan. It helps estimate the tool’s efficiency, total manufacturing costs, and effect on carbon emissions [66]. From an environmental perspective, disposing the worn-out portion of the tool after its service life results in high carbon emissions [67,68]. Tool wear depends on factors such as cutting conditions, workpiece material, and cutting geometry [69,70]. Carbon emissions from tools are calculated using a time-standard allocation method for the machining process over the tools’ life cycle. The specific calculation method is given as Eq. (23).

(23){\mathrm{C}\mathrm{E}}_k=\frac{t_{\mathrm{c}}}{(N+1)T}{F}_k{m}_k,

where F_k (kgCO₂/kg) is the carbon emission factor of the tool material, m_k (kg) is the mass of the tool, N is the number of times the tool is sharpened, T (min) is the tool life, and t_c (min) is the machining time.

For grinding processes, as shown in Eq. (24), the tool is a grinding wheel, and the carbon emissions from the tool are the carbon emissions from grinding wheel wear.

(24){\mathrm{C}\mathrm{E}}_k=\frac{V_s}{V_u}{m}_k\cdot F_k,

where V_u is the available volume of the grinding wheel and V_s is the volume of grinding wheel wear.

Tool life calculation is influenced by the tool and workpiece materials as well as the cutting parameters and cutting environment. Tool life calculation for turning tools is given as Eq. (25) [71], and for milling and drilling tools, it is given as Eq. (26) [72].

(25)T=\frac{A^{\alpha }}{v_{\mathrm{c}}^{\alpha }{f}^{\beta }{a}_{\mathrm{p}}^{\chi }}\cdot p_{\mathrm{c}},

(26)T=\frac{C_r}{v_{\mathrm{c}}^m{f}^n{a}_{\mathrm{p}}^p{a}_{\mathrm{w}}^s}\cdot p_{\mathrm{c}},

where α, β, and χ are coefficients related to the tool and workpiece materials. A is a coefficient related to cutting conditions. f is the feed per revolution. C_r, m, n, p, and s are coefficients related to tool life and depend on the machining and tool materials.

For gear hobbing, during the mass production of gears, gear hob cutters wear out continuously during processing. The carbon emissions caused by tool usage during the gear hobbing process are apportioned to each gear. The carbon emissions attributed to the tool during the machining process for the ith gear (i.e., CE_i,k) are calculated in Eq. (27) [65].

(27){\mathrm{C}\mathrm{E}}_{i,k}=\frac{1}{n-x}\sum _{i\in n}\frac{N{t}_i}{T_t}{F}_k{m}_k,

where N is the total number of gear hob cutters, t_i (s) is the time consumed to cut gear i, T_t (min) is the total life of a gear hob cutter, n is the total number of gears machined from the start to the stop of the gear hobbing machine, and x is the number of defective products during the gear hobbing machining.

(4) Fixture

During machining processes on machine tools, fixtures need to be used to secure the workpieces being machined. As shown in Eq. (28), the carbon emissions generated from the use of fixtures during the machining process are mainly due to the wear of the fixtures.

(28){\mathrm{C}\mathrm{E}}_j=\frac{t_j}{T_j}(f_j-f_{\mathrm{j}\mathrm{c}})\cdot G_j,

where T_j (h) is the life of the fixture used, f_j (g/kg) refers to the carbon emissions generated from producing the fixture, f_jc (g/kg) denotes the carbon emissions from recycling discarded fixtures, and G_j (kg) is the mass of the fixture material.

3.4 Waste disposal component

Carbon emissions from waste disposal mainly come from the cutting recycling process. During actual machining processes, chips can be recycled for secondary use. Their carbon emissions are divided equally between the original system and the recycling system, as shown in Eq. (29). This carbon emission includes carbon emissions from the production of raw materials and the recycling process [73].

(29){\mathrm{C}\mathrm{E}}_f=\frac{1}{2}\left[\delta (F^f+F^r)\cdot M_m\right]+(1- \delta )F^f\cdot M_m,

where δ is the metal chip recovery rate, F^f (kgCO₂/kg) is the carbon emission factor of the raw material for the part, F^r (kgCO₂/kg) is the carbon emission factor of the chip recycling process, and M_m (kg) is the mass of chips generated in the process.

For carbon emissions from chips in gear hobbing, the calculation method is similar to that for cutting fluid carbon emissions, where the emissions are apportioned to each gear. The carbon emissions from chip disposal for producing one gear are calculated in Eq. (30):

(30){\mathrm{C}\mathrm{E}}_{i,f}=\frac{M_m{F}^r}{n-x}.

This section provides a detailed summary of the carbon emission model for machining processes, which involve turning, milling, drilling, grinding, and gear hobbing. In terms of energy consumption, these processes are roughly the same, with feed energy consumption differing only because of different feed methods. In terms of tooling, cutting fluids, and waste disposal, gear hobbing is modeled using a batch-calculated apportioned modeling approach because gear hobbing is suitable for mass production.

3.5 Carbon emissions from other typical machining processes

(1) Laser welding

Laser welding consists of multiple systems whose carbon emissions have multi-source, multi-stage composite characteristics [74,75]. The sources of carbon emissions from the laser welding process can be traced back to various systems, including the laser generator (CE_laser), the chiller (CE_cool), the robot (and its control cabinet) (CE_robot), the shielding gas (argon) system (CE_gas), and auxiliary systems (CE_aux) [76,77]. Laser welding’s carbon emissions are calculated as Eqs. (31)–(36).

(31){\mathrm{C}\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}={\mathrm{C}\mathrm{E}}_{\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{r}}+{\mathrm{C}\mathrm{E}}_{\mathrm{c}\mathrm{o}\mathrm{o}\mathrm{l}}+{\mathrm{C}\mathrm{E}}_{\mathrm{r}\mathrm{o}\mathrm{b}\mathrm{o}\mathrm{t}}+{\mathrm{C}\mathrm{E}}_{\mathrm{g}\mathrm{a}\mathrm{s}}+{\mathrm{C}\mathrm{E}}_{\mathrm{a}\mathrm{u}\mathrm{x}}.

(32)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{r}}=& F_{\mathrm{e}}\cdot (E_{\mathrm{l}\mathrm{s}}+E_{\mathrm{l}\mathrm{s}\mathrm{t}}+E_{\mathrm{l}\mathrm{w}}+E_{\mathrm{l}\mathrm{r}})\\ =& F_{\mathrm{e}}\cdot ({\displaystyle {\int }_0^{t_{\mathrm{k}}}{P}_{\mathrm{l}\mathrm{s}}(t)\mathrm{d}t}+P_{\mathrm{l}\mathrm{s}\mathrm{t}}\cdot t_{\mathrm{l}\mathrm{s}\mathrm{t}}\\ & +{\displaystyle {\int }_0^{t_{\mathrm{l}\mathrm{w}}}{P}_{\mathrm{l}\mathrm{w}}(t)\mathrm{d}t}+P_{\mathrm{l}\mathrm{r}}\cdot t_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}).\end{array}

In Eq. (32), E_ls, E_lst, E_lw, and E_lr represent the energy consumption during the laser startup process, standby state, working state, and small peak state, respectively. P_ls, P_lst, P_lw, and P_lr correspond to the instantaneous power during the laser startup process, average power in the standby state, instantaneous power during the working state, and average power during the small peak state, respectively. t_ls, t_lst, t_lw, and t_total represent the duration of the laser startup process, the standby state time, the welding duration, and the total time of the welding process, respectively.

(33)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{c}\mathrm{o}\mathrm{o}\mathrm{l}}=& F_{\mathrm{e}}\cdot E_{\mathrm{c}\mathrm{o}\mathrm{o}\mathrm{l}}=F_{\mathrm{e}}\cdot P_{\mathrm{c}\mathrm{o}\mathrm{o}\mathrm{l}}\cdot t_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}\\ =& F_{\mathrm{e}}\cdot \frac{{\int }_0^{t_{\mathrm{c}\mathrm{l}}}P(t)\mathrm{d}t}{t_{\mathrm{c}\mathrm{l}}}{t}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}.\end{array}

(34)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{r}\mathrm{o}\mathrm{b}\mathrm{o}\mathrm{t}}=& {\mathrm{E}\mathrm{F}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}(E_{\mathrm{r}\mathrm{s}\mathrm{t}}+E_{\mathrm{r}\mathrm{w}})\\ =& {\mathrm{E}\mathrm{F}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}\left[P_{\mathrm{r}\mathrm{s}\mathrm{t}}\cdot t_{\mathrm{r}\mathrm{s}\mathrm{t}}+{\int }_0^{t_{\mathrm{r}\mathrm{w}}}{P}_{\mathrm{r}\mathrm{w}}(t)\mathrm{d}t\right].\end{array}

In Eqs. (33) and (34), P_cool represents the average power of the chiller during operation; E_rst and E_rw denote the energy consumption of the robot in standby and working states, respectively; P_rst and P_rw correspond to the average power in the standby state and the instantaneous power in the working state, respectively; t_rst is the time the robot remains in the standby state; and t_rw represents the time during which the robot is in operation.

(35){\mathrm{C}\mathrm{E}}_{\mathrm{g}\mathrm{a}\mathrm{s}}=F_{\mathrm{g}\mathrm{a}\mathrm{s}}\cdot q \cdot t_{\mathrm{g}\mathrm{a}\mathrm{s}}.

(36){\mathrm{C}\mathrm{E}}_{\mathrm{a}\mathrm{u}\mathrm{x}}=F_{\mathrm{e}}\cdot E_i.

In Eq. (35), q represents the flow rate of the shielding gas, t_gas is the duration for which the shielding gas is activated, and F_gas is the carbon emission factor for the material consumption of the shielding gas.

(2) CO₂ shielded welding

CO₂ shielded welding is a gas shielded welding method that uses CO₂ as the shielding gas. During the welding process, CO₂ gas is directly released into the environment, making it a direct source of carbon emissions at the welding site. Additionally, the welding process consumes various materials and electrical energy, indirectly leading to carbon emissions. Therefore, this portion of carbon emissions is considered indirect carbon emissions [78]. The calculation method is shown in Eq. (37).

(37)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=& {\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}+{\mathrm{C}\mathrm{E}}_{\mathrm{p}}\\ =& a_1\sum _{j=1}^n{E}_{1j}{f}_j+a_2\sum _{k=1}^m\sum _{j=1}^n{M}_{2k}{E}_{2j}{f}_j\\ & +a_3\sum _{k=1}^m\sum _{j=1}^n{M}_{3k}{E}_{3j}{f}_j+C_d,\end{array}

where CE_p represents the process carbon emissions; E_ij (i = 1,2,3; j = 1,2,…,n) denotes the consumption amount of the jth type of energy considered in the ith category of carbon emissions; f_j is the conversion factor for the jth type of energy, with standard coal used as a unified energy consumption measurement basis; α₁, α₂, and α₃ are the carbon emission factors for energy, material, and process, respectively; M_2k and M_3k (k = 1,2,…,m) denote the amount of the kth type of material consumed in material and process carbon emissions, respectively; and C_d is the amount of CO₂ gas consumed during the welding process. The direct release of CO₂ gas into the environment is referred to as direct carbon emission.

(3) Sand casting

Sand casting production is a hybrid manufacturing process where carbon emissions can be categorized into five basic types of process-related carbon sources. The five basic sources can be further divided into two major categories: equipment and non-equipment carbon sources. Equipment carbon sources mainly include idle (standby) carbon sources (C_PC) and load carbon sources (C_LC). Non-equipment carbon sources encompass material consumption carbon sources (C_MC), energy consumption carbon sources (C_EC), and unintended carbon sources (C_UC) [79].

The primary factors influencing carbon emissions from equipment carbon sources include equipment power (idle or load) and time factors. The calculation method is shown in Eqs. (38) and (39).

(38)C_{\mathrm{P}\mathrm{C}}=P_o{t}_n{F}_{\mathrm{e}}.

(39)C_{\mathrm{L}\mathrm{C}}=(P_o+\mu m_e{P}_w)\cdot t_n\cdot F_{\mathrm{e}}.

In Eqs. (38) and (39), P_o represents the equipment’s idle or standby power, t_n is the operating time, P_w denotes the additional power per unit weight of the equipment, μ is the power loss coefficient of the equipment, and m_e is the load mass.

The primary factors affecting carbon emissions from non-equipment carbon sources are the carbon emissions from materials, energy, and waste. The basic calculation method is shown in Eqs. (40)–(42).

(40)C_{\mathrm{M}\mathrm{C}}=\sum _{i=1}^n\sum _{k=1}^l(E_k^s\cdot U_i)\cdot F_{\mathrm{e}}.

(41)C_{\mathrm{U}\mathrm{C}}=\sum _{i=1}^n \sum _{k=1}^i(E_k^s\cdot Q_i\cdot {\phi }_i) \cdot F_{\mathrm{e}}.

(42)C_{\mathrm{E}\mathrm{C}}= \sum _{i=1}^n{V}_i\cdot F_i.

In Eqs. (40)–(42), U_i represents the quantity of the ith type of material consumed, denotes the electricity consumption at the kth processing stage for treating the material or waste, V_i is the quantity of the ith type of energy consumed, F_i represents the carbon emission factor for the ith type of energy, Q_i is the quantity of the ith type of unintended output, and φ_i is the difficulty coefficient for handling the ith type of unintended substance. φ_i can be comprehensively determined based on the waste treatment situation.

(4) Forging

In the forging process, material carbon emissions primarily result from the burn-off of metals, and energy carbon emissions are associated with the electrical energy consumed by equipment during pre-forging treatment, forging, and post-forging heat treatment stages. Given that forging generally does not produce waste, carbon emissions from waste treatment are not calculated in this study [80]. The specific calculation method for forging is given in Eqs. (43)‒(45).

(43){\mathrm{C}\mathrm{E}}_{\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}=F_{\mathrm{m}}\cdot q_{\mathrm{b}}\cdot m_{\mathrm{f}}.

(44){\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}=F_{\mathrm{e}}\cdot (E_{q1}+E_{q2}+E_q).

(45)\begin{array}{rl}{E}_q=& {\int }_0^{x_f}{F}_{\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{e}}\mathrm{d}v\\ \approx & \frac{1}{2}\sum _{i=0}^{N-1}\left[F_{f0}(x_i+1)+F_{\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{e}}{x}_i\right]\cdot (x_{i+1}-x_i).\end{array}

In Eqs. (43)–(45), F_m is the carbon emission factor for the forging material, q_b represents the burn-off rate of forging, and m_f is the mass of forging. E_q1 and E_q2 are the electrical energy consumption of the equipment during pre-forging and post-forging heat treatments, and they can be derived from relevant equipment parameters. E_q is the energy consumption during the forging process. F_forge represents the forging force, x_f is the total displacement of the hammer head, x_i denotes the displacement during the ith strike, and N is the number of strikes.

3.6 Carbon emission factor

The carbon emission factor refers to the amount of carbon emissions produced per unit of energy during the combustion or use of each type of energy. Carbon emission factors are qualitative parameters because they are related to the production, consumption, handling, or transportation of energy, materials, consumables, or tools. The emission factor method provided in the IPCC guidelines is currently the most widely used greenhouse gas accounting method. It combines information on the extent of human activities, known as activity data (AD), with the emission or removal coefficients that quantify the emissions per unit of activity (emission factor, EF). This relationship can be simply expressed by the formula E = AD·EF.

Carbon emissions from electricity are calculated by multiplying the consumed electricity by the carbon emission factor. This carbon emission factor varies by country or region because it is related to the methods of electricity generation [81]. Peng et al. [82] proposed a method for calculating the comprehensive carbon emission factor for regional power grids in Chinese provinces based on a proportional distribution, and they developed a calculation model for the exchange and consumption of electricity between provinces and regions within provinces. Zhang et al. [83] calculated the full lifecycle carbon emission factor of electricity in China on the basis of ISO 14067 and PAS 2050 standards and recent developments in China’s power generation industry, and they made preliminary predictions of the full lifecycle carbon emission factors of electricity in China for 2025 and 2030. Jeswiet and Kara [84] posited that an electrical grid has one or more primary energy sources. The primary energy sources are coal (C), natural gas (NG), petroleum (P), biofuel (B), hydro (H), solar (S), wind (W), geothermal (G), earth (E), wave (Wa), and tidal (T). Each of these can be expressed as a fraction. A grid provides electrical energy and is made up of the sum of fractions of the primary sources multiplied by the conversion efficiency, \eta, for each energy source. Each electrical power grid has a carbon emission factor, F_e. F_e can be derived from Eq. (46). The coefficients, namely, 112, 66, and 49, are an unavoidable outcome of combustion and refer to the kilograms of carbon emitted per gigajoule of heat released in each case.

(46)F_{\mathrm{e}}=\eta \cdot (112\cdot x \mathrm{\%}\cdot \mathrm{C}+49\cdot x\mathrm{\%}\cdot \mathrm{N}\cdot \mathrm{G}+66\cdot x \mathrm{\%}\cdot \mathrm{P}).

Many scholars have also conducted research on the calculation of carbon emission factors related to materials. Yu et al. [85] proposed a new method for calculating the carbon emission factor of prefabricated components, which includes analyzing the production process, defining the scope of carbon emissions, identifying carbon emission sources, and establishing a measurement equation for the carbon emissions of prefabricated components. Panagiotopoulou et al. [86] classified carbon emission sources at the process, machine, and system levels and determined the weights of carbon emission factors through sensitivity analysis to identify which carbon emission factor contributes the most to carbon footprint calculation. Hu et al. [87] conducted a systematic literature review to summarize the carbon emission factors associated with railway construction projects. Additionally, some international organizations and regions have already established carbon emission factor databases and are continually improving them. The prevailing carbon emission factors are derived from various sources, including, but not limited to, IPCC, the European Monitoring and Evaluation Programme, the United States Environmental Protection Agency, the Department for Environment, Food and Rural Affairs of United Kingdom, and the Ministry of Ecology and Environment of China [88].

This section provides a detailed summary of carbon emission models for common machining processes, such as turning, milling, drilling, welding, and forging. For machine tool-based processes, such as turning, milling, and grinding, the calculation methods for cutting energy consumption vary depending on the cutting techniques employed, whereas the methods for calculating the inherent energy consumption of machine tools are generally consistent. Regarding the carbon emissions associated with tool wear, various types of tools are utilized in different processes, leading to differences in calculation methods. In terms of tools, cutting fluids, and waste management, given that gear hobbing is suitable for mass production, a modeling approach that calculates the emissions for the entire production batch and then allocates the results is adopted. Tab.1 summarizes the carbon emission calculation formulas for specific aspects of each process. The carbon emissions of welding, casting, and forging processes are calculated separately because of the unique processing methods of these processes.

4 Assessment of the effect of carbon emissions on multiple factors

In machining processes, carbon emissions are influenced by various factors and primarily reflected in cutting parameters, tools, and workpieces. Cutting parameters mainly include MRR, spindle speed, cutting depth, feed rate, and cutting speed [89]. The factors influencing carbon emissions from tools include tool geometry parameters, tool trajectory, and tool material. The factors affecting carbon emissions from workpieces are primarily the workpiece’s raw size and material [90].

4.1 Cutting parameters

Jiang et al. [28] found that as spindle speed increases, carbon emissions resulting from energy consumption or cutting fluid usage decrease. However, tool life also decreases as a result, thereby increasing carbon emissions caused by the tool. Therefore, as shown in Fig.8(c), the overall pattern of carbon emissions follows a quadratic function, exhibiting a minimum value.

The main calculated parameter for cutting speed is spindle speed, and their patterns of change are roughly similar. The effect of cutting speed on carbon emissions and machining time is greater than that of feed rate [53]. Increasing cutting speed to improve machining efficiency does not guarantee low carbon emissions. However, an optimal cutting speed for achieving a balance between machining efficiency and carbon emissions exists [91]. At the same speed, carbon emissions associated with energy consumption from milling are less than those from turning. The main reason is the substantial differences in energy consumption between different machining methods due to variations in cutting forces and tool wear [92].

As shown in Fig.8(d), the pattern of carbon emissions caused by MRR is similar to that of spindle speed. However, as presented in Eq. (47) [17], the range of MRR variation is limited by small values of feed rate and cutting depth, resulting in a smaller effect on carbon emissions compared with spindle speed.

(47)\mathrm{M}\mathrm{R}\mathrm{R}=f{a}_{\mathrm{p}} v_{\mathrm{c}}.

As shown in Fig.8(b), with an increase in feed rate, energy consumption carbon emissions and carbon emissions from waste cutting fluid treatment decrease, whereas carbon emissions from tool wear increase. Therefore, a set of cutting parameters corresponding to the minimum carbon emissions for each process stage exists [93].

The influence of cutting depth on carbon emissions during the cutting process is markedly different. Compared with spindle speed and feed rate, cutting depth has the most substantial effect on carbon emissions and machining time [94]. As depicted in Fig.8(a), the total carbon emissions decrease monotonically with increasing cutting depth. For a given MRR, tool life decreases with increasing cutting depth, leading to increased carbon emissions from tool wear. However, with an increase in cutting depth, machining time decreases, resulting in a reduction in carbon emissions from electrical energy and cutting fluid. Compared with the increase in carbon emissions due to reduced machining time, the increase in carbon emissions from tool wear is minimal. Therefore, at small cutting depths, the total carbon emissions are high, and at large cutting depths, the total carbon emissions are low [95].

4.2 Tool parameters

The factors that affect carbon emissions from tools include tool material, tool geometry angles, diameter, number of teeth, number of sharpening cycles, and quality. Different tool materials exhibit varying cutting energy consumption and tool life under the same operating conditions, leading to different carbon emissions. With regard to tool geometry angles, the rake angle has the greatest effect on cutting forces, with an increase in rake angle resulting in a gradual reduction in cutting forces [96]. Consequently, the electrical energy carbon emissions during cutting also decrease. The selection of different feasible tools can lead to differences in available ranges of cutting parameters, quality, price, precision, and other factors. Each feasible tool has its own unique range of cutting parameters constrained by specific tool and machine specifications.

In terms of tool materials, Tian et al. [97] studied the effect of different tools on carbon emissions and found that when cutting parameters remain constant, machining time does not vary with changes in the tool. However, carbon emissions and costs exhibit substantial variation with different tools. Different tool performance results in different ranges of feasible cutting parameters during the machining process, leading to varying carbon emissions. Selecting appropriate cutting tools during machining processes is advantageous for reducing carbon emissions and machining costs.

4.3 Tool path

The energy consumption of machine tools during operations, such as tool changes and tool path selection, is influenced by the processing sequence of part features. When the previous feature on the machining sequence differs, changes occur in the tool path and the corresponding tool change plan [98]. This situation results in different values for feed power, feed distance, feed speed, tool change time, and tool change power, leading to variations in total energy consumption during operation [99]. Optimizing tool paths can help reduce energy consumption [100]. Li et al. [101] found that under the same cutting parameter settings, the carbon emissions during conventional parallel milling and streamline milling processes differ. As shown in Fig.9(a), the energy consumption and carbon emissions during the machining process increase linearly with the increase in tool path. Therefore, selecting appropriate tool paths is crucial for energy conservation and emission reduction efforts.

4.4 Workpiece parameters

The influence of the workpiece on carbon emissions is mainly related to the size of the raw material and the material of the workpiece. Raw material size is one of the parameters used to calculate MRR, and its effect on carbon emissions has been previously discussed. The material of the workpiece affects the electrical energy consumption and tool wear during processing, thereby influencing the carbon emissions generated during the machining process. Generally, compared with cutting ductile materials, cutting brittle materials consumes less electrical energy and results in less tool wear, leading to reduced carbon emissions [90]. In material selection, the following principles can be followed: prioritize the use of renewable materials, choose materials with low energy consumption and pollution, and select materials with good environmental compatibility [102].

With regard to the influence of workpiece material on carbon emissions, Liu et al. [53] investigated the carbon emissions of machining structural steel, gray cast iron, and malleable gray cast iron by using high-speed steel and cemented carbide tools. As depicted in Fig.9(b), the carbon emissions and machining time of the three materials decrease sequentially, with malleable gray cast iron exhibiting the best machinability. Additionally, the optimal feed rate for high-speed steel tools is higher than that for cemented carbide tools, but the optimal cutting speed is lower. However, compared with high-speed steel tools, cemented carbide tools yield lower carbon emissions and machining time, indicating that cutting speed exerts a more considerable effect on carbon emissions and machining time than feed rate does.

Compared with tools, cutting parameters have a greater influence on carbon emissions during the mechanical machining process. The determination of cutting parameters substantially influences the machining process, and optimizing these parameters is an effective method to improve resource consumption and reduce greenhouse gas emissions in manufacturing [103]. As shown in Fig.8, among all cutting parameters, cutting depth has the most substantial influence on carbon emissions during the mechanical machining process, and MRR has the least effect. Moreover, the selection of tools, workpiece materials, and machining paths plays a crucial role in carbon emissions during the mechanical machining process. A low carbon footprint should be one of the design constraints when designing products to reduce carbon emissions; this approach is effective for reducing product carbon footprint [104]. During the machining process, the aforementioned factors must be comprehensively considered; appropriate cutting parameters must be set; suitable tools and environmentally friendly materials that are easy to process should be selected; and efficient, low-energy, low-carbon tool paths must be chosen. These measures greatly contribute to reducing carbon emissions during the mechanical machining process.

4.5 Optimization of carbon emissions from machining

Cutting parameters, tools, and workpieces affect machining carbon emissions. As shown in Tab.2, to address these factors, scholars have used various methods to optimize carbon emissions during machining. Tian and Yin [105] employed the NSGA-II algorithm to optimize the grinding carbon emission model by optimizing three parameters: grinding wheel linear speed, radial feed rate, and workpiece rotary feed rate. Ic et al. [106] applied the response surface methodology (RSM) to establish a regression model of carbon emission and surface roughness in the turning process and used the expectation function method to obtain the parameter values that minimize surface roughness and carbon emissions. Li and Liu [107] developed a green and efficient milling parameter optimization model to minimize carbon emissions during the milling process. Jiang et al. [108] adopted NSGA-II to solve Pareto boundaries with carbon emission as the optimization objective and used the technique for order preference by similarity to an ideal solution method to rank the Pareto boundaries and determine the optimal combination of cutting parameters. Meanwhile, Yi et al. [63] proposed a carbon emission and machining time turning optimization model on the basis of the proposed system energy boundaries by using the NSGA-II algorithm with cutting speed and feed rate as the limiting conditions. Nguyen et al. [109] used regression analysis to establish a comprehensive model for carbon emissions, specific cutting energy consumption, and surface roughness in turning processes. They optimized the cutting parameters to reduce carbon emissions and energy consumption. The prediction accuracy and optimization performance of these proposed models are shown in Tab.3.

However, in machining processes, carbon emissions resulting from energy consumption constitute the majority of the total emissions. Carbon emissions resulting from energy consumption account for approximately 65% of global greenhouse gas emissions [111]. In China, manufacturing energy consumption accounts for about 57% of the total energy consumption, with approximately 16% coming from electricity [112]. Inefficient energy use leads to high carbon emission levels [113]. Machining systems are vast, and machine tools are the most basic energy-consuming equipment in manufacturing. The emissions generated by machine tool electricity cannot be ignored [114]. Many studies have shown that machining system energy efficiency is very low, with only about 30% of energy used for cutting [31]. Gutowski et al. [115] described an example of energy measurement in an automated machining production line and found that only 14.8% of energy is actually used for cutting, with most of the energy being used for the operation of cutting fluid and lubrication systems. Therefore, improving machine tool energy efficiency is crucial in energy conservation and carbon emission reduction.

Many scholars have studied the energy efficiency of machine tool machining processes and employed various approaches to optimize machine tool energy efficiency. Liu et al. [31] proposed an energy flow theory of machining systems and established an energy consumption model for the main drive system of ordinary machine tools. They revealed that the no-load power of the main drive system of machine tools approximates a quadratic function of speed. Jeswiet and Kara [84] introduced the concept of carbon emission signature and established the relationship between carbon emissions and energy consumption. Shi et al. [116] developed an energy flow model for the main drive system of variable-frequency speed-regulated CNC machine tools. They analyzed the power transmission characteristics of the main motor of machine tools and the mechanical transmission system and established dynamic power balance equations for the entire main drive system. Hu et al. [117] built a frequency-based parameter model for the no-load energy of the main drive system of CNC machine tools and analyzed the energy parameter characteristics under no-load operation of machine tools. Balogun and Mativenga [34] categorized machine tool states into three major categories and introduced a ready state between the preparation state and the cutting state. During this stage, abundant energy is required to drive spindle movement to position the tool and workpiece correctly and set the necessary cutting speeds.

5 Carbon emissions from novel processes

Various novel processes have been developed and implemented to mitigate pollution and carbon emissions in the manufacturing industry. Green mechanical processing technology refers to the use of environmentally friendly equipment, processes, and materials to reduce environmental effects while achieving efficient and precise processing during the mechanical processing process. Green processes are based on traditional processes but incorporate advanced technologies to form green processes. Examples include rapid prototyping technology, waste recycling technology, dry machining, and cryogenic machining.

5.1 Dry machining

Compared with dry cutting, traditional wet cutting involves the use of large amounts of cutting fluid, with associated costs comprising approximately 7%–17% of the total manufacturing costs [118]. This situation results in high manufacturing costs, which hinder the sustainable development of related processes [119]. The use of cutting fluids can maximize efficiency, enhance process safety, and improve tool performance and part quality [120]. However, these fluids pose numerous challenges in terms of cost, environmental influence, disposal, and recycling. Examples of these challenges include additional energy, infrastructure, and manufacturing costs; environmental pollution; and threats to workers’ health [121].

Innovative technologies have been developed to avoid or minimize the use of cutting fluids, and in some cases, dry machining, that is, machining without the use of cutting fluids, has become feasible [122]. Reducing the use of cutting fluids not only saves costs but also decreases environmental pollution [123]. The environmental performance of dry machining can be broadly reflected in the following aspects: low cost, low resource consumption, low carbon emissions, and absence of toxic substances [124]. However, dry machining still has some drawbacks, such as high cutting temperatures leading to excessive tool wear and short tool life. Additionally, the chips generated during machining cannot be cleared in a timely manner, resulting in surface deterioration [125].

Khanna et al. [66] conducted a comparative analysis of carbon emissions between dry and wet machining processes and found that the total carbon emissions of dry machining are lower than those of wet machining. Wet machining consumes more energy and emits more carbon than dry machining does because of the use of a large amount of cutting fluid, which requires additional power from the motors. Under the same MMR, dry cutting is more energy efficient and environmentally friendly compared with wet cutting [126]. However, dry machining accumulates a large amount of heat in the cutting zone over a certain length of machining, resulting in accelerated tool wear and high carbon emissions from tool wear. Nevertheless, compared with carbon emissions from tool wear in dry machining, the energy consumption carbon emissions caused by the use of cutting fluids in wet machining are higher.

The environmental friendliness of dry machining has been validated in practical applications. Li et al. [127] conducted machining experiments on 192000 automotive transmission gears using a high-speed dry gear hobbing machine and the same batch of gears using a spray coolant in a traditional gear hobbing machine. They found that high-speed dry gear hobbing consumes 62.5% less electricity and 40% less lubricating oil compared with wet cutting, resulting in 59% less carbon emissions. The carbon emissions from dry gear cutting are much lower than those from traditional wet machining, making the former more environmentally friendly than the latter.

However, Lin et al. [36] found that multi-pass wet machining is more low-carbon than dry machining. Under multi-pass machining conditions, wet cutting allows for high cutting speeds and feed rates, thus reducing the machining time and carbon emissions from tool wear and energy consumption. Despite the carbon emissions generated from the use of cutting fluids, the overall carbon emissions from wet machining are lower than those from dry machining.

5.2 Cryogenic machining

Cryogenic machining involves delivering a low-temperature medium to the cutting zone or within the tool to maintain the cutting area and tool at a low-temperature cooling state for machining [128]. Commonly used cryogenic media include liquid nitrogen (LN₂, −186 °C) and liquid CO₂ (LCO₂, −76 °C) [129]. In comparison with traditional coolant lubrication methods, cryogenic machining does not utilize cutting fluids [130,131]. Nitrogen gas and CO₂ can evaporate into the atmosphere without requiring recovery or treatment, posing no harm to the environment or human health, making cryogenic machining an environmentally friendly machining method [132]. As depicted in Fig.10(a), during cryogenic machining, LN₂ is sprayed into the cutting area via a nozzle. A calibrated digital K-type thermocouple is installed at the nozzle’s tip to monitor the temperature. During the cryogenic machining process, the measured temperature is approximately −175 °C and remains constant throughout the machining operation. An FLIR T640 thermal camera with an accuracy of ±1 °C is utilized to monitor the temperature of the machined surface during actual processing [133]. In machining operations, LN₂ is typically applied to the cutting zone via jetting or immersion to reduce wear, prolong tool life [134], and enhance machining quality with smooth chip evacuation. The cryogenic cooling system of LCO₂ enables accurate and stable flow control, temperature measurement within the pipeline, pressure measurement within the pipeline, and flow rate measurement [135]. LN₂ and LCO₂ exhibit some differences in machining. LN₂’s temperature is lower than that of LCO₂, so it enables faster cooling of the tool and workpiece. With regard to the pressure required to maintain a liquid state, LCO₂ requires lower pressure than LN₂ does.

Agrawal et al. [136] conducted turning experiments under conventional wet cooling and N₂ cryogenic cooling conditions and found that deep cold turning at high cutting speeds is a suitable measure to reduce carbon emissions. As shown in Fig.10(c), compared with wet machining, cryogenic machining exhibits higher material carbon emissions because the coolant (LN₂) used in cryogenic machining evaporates immediately after dissipating heat from the cutting zone and cannot be reused or recycled. Moreover, coolant production generates a large amount of carbon emissions. However, the carbon emissions from chip recovery in cryogenic turning are low, and the energy consumption for waste disposal in cryogenic turning is also low, thereby reducing the overall carbon emissions. Given these factors, cryogenic machining results in lower total carbon emissions compared with wet machining, making it more environmentally friendly.

Khanna et al. [66] conducted a comparative analysis of the carbon emissions of cryogenic and dry machining. As shown in Fig.10(b), dry cutting generates substantial cutting forces, resulting in high cutting power consumption. In a cryogenic machining environment, energy consumption is considerably reduced, leading to a decrease in carbon emissions. With regard to carbon emissions from tool wear, during cryogenic machining, LCO₂ is sprayed into the cutting zone, remarkably reducing the cutting zone temperature and greatly extending tool life [137]; notably, the carbon emissions generated from tool wear at low temperatures are reduced. Therefore, the total carbon emissions from cryogenic machining are much lower than those from dry machining.

Jamil et al. [138] conducted a comparative study on the sustainability of LN₂ and LCO₂. They found that compared with dry machining, carbon emissions are reduced by approximately 4.58% and 3.57% on average under LCO₂ and LN₂ cooling conditions, respectively. In terms of production energy consumption, LCO₂ requires much less energy compared with LN₂, resulting in lower carbon emissions from LCO₂ processing [139]. Iqbal et al. [140] also demonstrated that the use of a small amount of CO₂ as a coolant during machining does not increase the carbon footprint. Moreover, the preparation of CO₂ as a coolant involves capturing CO₂ from the atmosphere, and during the machining process, CO₂ evaporates back into the atmosphere without increasing the carbon footprint during this process. Therefore, LCO₂ cryogenic machining is sustainable.

5.3 Minimum quantity lubrication

In certain situations where complete removal of the cutting fluid is not feasible because of insufficient air cooling [141], near-dry machining and its enabling technologies, also known as MQL, have been developed. Compared with dry machining, MQL reduces cutting temperatures, tool wear, and surface roughness to a certain extent [142]. MQL mist or aerosol is generated by mixing compressed air with a cutting fluid. A lubricating layer is formed at the tool–workpiece interface, reducing friction and heat generation. This technology involves mixing small amounts of lubricant with high-pressure gas; the lubricant can penetrate the wheel air barrier and reach the grinding zone after mixing and atomization, and the high-pressure gas cools and removes debris [143,144]. MQL utilizes biodegradable plant oils as cutting fluids and thus exerts nearly zero harm to the environment and human health. The consumption of cutting fluid is typically between 10 and 200 mL/h, which is only 1%–10% of that used in traditional flood cooling methods, substantially reducing the usage and disposal costs of cutting fluids.

MQL is more environmentally friendly than dry cutting but slightly less so than cryogenic machining. This conclusion has been validated in the studies conducted by Uysal et al. [145] and Jamil et al. [138]. Under the same cutting conditions, cryogenic and MQL machining experience much lower cutting forces compared with dry machining, with LCO₂ and LN₂ effectively reducing the cutting zone temperature and extending tool life. Consequently, the energy consumption and carbon emissions of cryogenic and MQL machining are lower than those of dry cutting. However, during MQL machining, the power consumption of hydraulic pumps and compressors increases, whereas during cryogenic machining, only the power consumption of compressors is affected. Thus, compared with MQL machining, cryogenic machining produces lower carbon emissions.

The sustainability of MQL has been validated in practical applications. Zhang et al. [146] developed a micro-droplet MQL device attached to an oil film and conducted data measurements at two user facilities. Under micro-droplet MQL, the oil consumption in the two facilities was reduced by an average of 97.6% relative to the oil consumption under flood cooling, resulting in a yearly CO₂ emission equivalent reduction of 1982 kg. These data indicate that the carbon emissions generated from oil consumption in micro-droplet MQL are much lower than those from traditional flood cooling.

5.4 Electrostatic MQL

MQL’s limited penetration and cooling capacity often lead to high heat generation, which accelerates tool wear and compromises the surface integrity of workpieces. As depicted in Fig.11(a) and Fig.11(b), electrostatic MQL (EMQL) was developed to overcome these limitations. As presented in Fig.11(c), the cutting fluid is atomized into droplets by high-pressure gas and charged at a copper nozzle. The charged oil droplets are then transported to the cutting zone under the driving force of airflow and electric field [147]. This method combines electrostatic spraying with MQL and uses minimal amounts of lubricating oil (5–20 mL/h), and the charged oil particles enhance the penetration and wetting properties of the cutting zone, thereby improving lubrication performance [148], as shown in Fig.11(d). Studies have shown that EMQL substantially improves friction and cutting performance; tool life increases by approximately 73% compared with MQL, and lubricant consumption decreases by 67%. Using EMQL can reduce the concentration of floating droplets in the workspace by about 10% compared with MQL, thereby reducing the amount of mist and resulting in minimal harm to human health, reduced environmental damage, and increased environmental friendliness [149,150].

Kashyap et al. [151] analyzed carbon emissions during milling under LCO₂ and EMQL cooling conditions and found that the total carbon emissions from EMQL are 27.69% higher than those from low-temperature LCO₂ machining. The main difference between the two is in carbon emissions from waste disposal, as shown in Fig.12(c). Similarly, Salvi et al. [152] demonstrated that carbon emissions from waste disposal considerably affect the entire machining process, and dry cutting and LCO₂ cutting environments are more environmentally feasible than EMQL cutting environments. Compared with EMQL machining, low-temperature LCO₂ machining offers longer tool life and lower chip disposal energy consumption, and the CO₂ gas generated during the machining process can be recovered and reused, resulting in negligible carbon emissions [153].

5.5 Nanofluid MQL

Nanofluid MQL (NMQL), as illustrated in Fig.12(a), is an innovative lubrication method that combines nano-lubrication with traditional cooling techniques. Its principle involves the addition of a certain proportion of nanoscale lubricants to the coolant, forming a stable and uniform lubricating film. Simultaneously, it effectively reduces the temperature of the coolant, thereby achieving dual effects of lubrication and cooling [154]. NMQL not only inherits the advantages of conventional MQL but also addresses the critical drawback of insufficient heat transfer capability typical of traditional MQL. Furthermore, the excellent tribological properties of nanoparticles enhance the lubrication performance at the wheel/workpiece interface and at the wheel/chip interface, thereby improving the machining accuracy, surface quality, and surface integrity of the workpiece [155]. As depicted in Fig.12(b), the anti-wear and friction-reducing mechanisms of nanoscale particles at the sliding contact interface can be summarized into four main effects: rolling, film forming, filling and repairing, and polishing. The four effects elucidate the principle of wear reduction by nanoscale particles from different perspectives [156,157].

In the study conducted by Khan et al. [158] that focused on turning Ti-6Al-4V alloy, the carbon emissions during Al-GnP NMQL and low-temperature LN₂ machining processes were investigated. In Fig.12(d), \mathrm{S}\mathrm{\_}\mathrm{C}\mathrm{E}\mathrm{e} represents the carbon emissions from electrical energy consumption, and \mathrm{S}\mathrm{\_}\mathrm{C}\mathrm{E}\mathrm{p} denotes the total CO₂ emissions per product. The carbon emissions resulting from electrical energy consumption in Al-GnP NMQL are relatively higher than those in LN₂ machining. However, LN₂ production entails high CO₂ emissions, and LN₂ cannot be recycled. For every kilogram of LN₂ synthesized, approximately 0.67 kg of CO₂ is emitted. From the perspective of total carbon emissions, Al-GnP nanofluid-assisted machining yields low carbon emissions.

Ross et al. [139] found that among different methods, dry cutting results in the highest carbon emissions, and NMQL generates the least emissions, as illustrated in Fig.12(e). The multi-walled carbon nanotubes in NMQL oil reduce friction in the contact area, improve heat dissipation, and thus lower power consumption, and the concentration of nanomaterials substantially reduces the amount of turning associated with machining time. LCO₂ rapidly decreases the heat in the cutting contact area, resulting in short machining time. However, the preparation of LCO₂ entails additional energy consumption and carbon emissions.

By contrast, the findings of Gupta et al. [159], as depicted in Fig.12(f), indicate that on the average, carbon emissions are reduced by 13.47%, 32.81%, and 49.17% under the cooling and lubrication conditions of MQL, NMQL, and LN₂, respectively, compared with dry turning. The lowest carbon emissions are produced under LN₂ cooling conditions. However, this study did not include the carbon emissions from the preparation process of LN₂.

With regard to the carbon emissions in MQL, EMQL, and NMQL machining processes, with the exception of the calculation methods for carbon emissions from cutting fluid consumption and the electrical energy consumed by the MQL system (E_MQL), the other carbon emission calculation methods are similar to those in Section 2. Given that the cutting fluid in MQL, EMQL, and NMQL processes is directly atomized, its recovery does not need to be considered. The carbon emission calculation method is shown in Eqs. (48) and (49).

(48)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=& {\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{o}\mathrm{i}\mathrm{l}}+{\mathrm{C}\mathrm{E}}_k+{\mathrm{C}\mathrm{E}}_j+{\mathrm{C}\mathrm{E}}_f\\ =& (E_u+E_{\mathrm{c}}+E_{\mathrm{a}}+E_{\mathrm{f}}+E_{\mathrm{c}\mathrm{l}}+E_{\mathrm{h}}+E_i\\ & +E_{\mathrm{M}\mathrm{Q}\mathrm{L}})\cdot F_{\mathrm{e}}+{\mathrm{C}\mathrm{E}}_{\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{o}\mathrm{i}\mathrm{l}}\\ & +{\mathrm{C}\mathrm{E}}_k+{\mathrm{C}\mathrm{E}}_j+{\mathrm{C}\mathrm{E}}_f.\end{array}

(49){\mathrm{C}\mathrm{E}}_{\mathrm{c}}=t_{\mathrm{c}}\cdot Q_{\mathrm{l}}\cdot F_{\mathrm{l}}.

In the above equation, Q_l represents the flow rate of the cutting fluid, and F_l is the carbon emission factor of the cutting fluid.

5.6 Cryogenic MQL

Cryogenic machining inherently limits lubrication characteristics, prompting the development of cryogenic-MQL (CMQL), which combines cryogenic cooling with MQL [160]. When MQL technology is applied in the efficient cutting of difficult-to-machine materials, issues, such as lubrication film rupture and lubrication failure, may occur because of excessively high temperatures in the cutting zone. This problem can be addressed through CMQL, which utilizes the effective cooling of low-temperature cooling media and the anti-friction lubrication properties of lubricating oil. CMQL exhibits excellent lubrication and cooling properties, positively affecting surface integrity and tool life [161]. A recent study indicated that compared with low-temperature cooling alone, the introduction of mixed CMQL reduces surface roughness by 24% and decreases tool wear by 50% [162]. Dry-ice blasting employs compressed air mixed with dry ice to fracture dry ice into small particles, which are then propelled to impact surfaces, serving as a physical means to remove surface contaminants, oils, and dust particles [163]. The lubrication and cooling properties are preserved by combining MQL with dry ice blasting, and timely removal of chips is achieved, thereby enhancing surface quality.

Jamil et al. [164] studied the processability and sustainability of MQL combined with dry-ice blasting. As shown in Fig.13(a), they found that MQL reduces energy consumption by 13.3%, and carbon emissions are 54% lower on the average compared with MQL combined with dry-ice blasting, indicating that good processability and sustainability can be achieved with MQL alone. Notably, the manufacturing process of dry ice generates a large amount of energy-related carbon emissions, and the cost of dry ice accounts for nearly 30% of the total machining cost. However, MQL combined with dry-ice blasting reduces surface roughness by 15%, showing high potential for development.

The carbon emission calculation method for the CMQL machining process is shown in Eqs. (50) and (51).

(50)\begin{array}{rl}{\mathrm{C}\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=& {\mathrm{C}\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{o}\mathrm{i}\mathrm{l}}+{\mathrm{C}\mathrm{E}}_k+{\mathrm{C}\mathrm{E}}_j+{\mathrm{C}\mathrm{E}}_f\\ =& (E_u+E_{\mathrm{c}}+E_{\mathrm{a}}+E_{\mathrm{f}}+E_{\mathrm{c}\mathrm{l}}+E_{\mathrm{h}}+E_i+E_{\mathrm{M}\mathrm{Q}\mathrm{L}}\\ & +E_{\mathrm{i}\mathrm{c}\mathrm{e}})\cdot F_{\mathrm{e}}+{\mathrm{C}\mathrm{E}}_{\mathrm{c}}+{\mathrm{C}\mathrm{E}}_{\mathrm{o}\mathrm{i}\mathrm{l}}\\ & +{\mathrm{C}\mathrm{E}}_k+{\mathrm{C}\mathrm{E}}_j+{\mathrm{C}\mathrm{E}}_f.\end{array}

(51){\mathrm{C}\mathrm{E}}_{\mathrm{c}}=F_{\mathrm{l}}\cdot V_{\mathrm{M}\mathrm{Q}\mathrm{L}}+F_{\mathrm{C}{\mathrm{O}}_2}\cdot V_{\mathrm{C}{\mathrm{O}}_2}.

In the equations, E_ice represents the electrical energy consumed by the cryogenic system during its operation, V_MQL denotes the volume of cutting fluid consumed by the MQL system, F_{\mathrm{C}{\mathrm{O}}_2} is the carbon emission factor for CO₂, and V_{\mathrm{C}{\mathrm{O}}_2} is the volume of CO₂ consumed.

5.7 Cryogenic ultrasonic-assisted machining

The cryogenic ultrasonic-assisted device, as illustrated in Fig.14(a) and Fig.14(b), introduces ultrasonic vibration to enhance the processability of cryogenic-assisted machining. This intermittent contact between the tool and workpiece induced by ultrasonic vibration positively affects cutting forces [167–169]. Cryogenic ultrasonic vibration assistance retains the benefits of cryogenic assistance while effectively mitigating adverse effects, such as machining hardening [166,170]. This conclusion was validated in the study of Khanna et al. [165]. They analyzed energy consumption and carbon emissions under cryogenic-assisted turning (CAT) and cryogenic ultrasonic-assisted turning (CUAT) conditions, as depicted in Fig.13(b). They found that compared with CAT, CUAT has lower cutting forces, reduced energy consumption, less tool wear, and lower carbon emissions. This result indicates that introducing ultrasonic assistance into cryogenic turning positively influences cutting processes, making them increasingly environmentally friendly and low carbon.

The carbon reduction potential of processes, including dry machining, cryogenic machining, MQL, NMQL, EMQL, CMQL, and cryogenic ultrasonic-assisted machining is evaluated in this section. These novel processes address the drawbacks of traditional flood cooling methods that utilize large amounts of cutting fluids, substantially reducing carbon emissions associated with the cutting fluid and energy consumption of cooling systems. The primary difference among the carbon emission models of novel and traditional processes lies in the cutting fluids and cooling systems used. A comprehensive literature review was conducted regarding the carbon emissions of these novel processes, and it revealed that scholars mainly performed their calculations on the basis of the general model summarized in Section 3. However, specific carbon emission calculation methods for unique aspects of novel processes, such as MQL equipment and the preparation and release of cryogenic media, have not been established yet. Therefore, this section presents the limited carbon emission models of the novel processes identified in the literature review. Additionally, this section conducts a comparative assessment of the carbon emissions of the novel processes, and the results are presented in Tab.4, which shows that the total carbon emissions of most novel processes are higher than those of cryogenic machining. Notably, when the energy consumption associated with the preparation of cryogenic media is excluded, cryogenic machining demonstrates high potential for carbon reduction.

6 Conclusions and prospects

6.1 Conclusions

The following conclusions were obtained after summarizing the carbon emission models in mechanical machining, analyzing the carbon emissions during the machining process, and examining the factors that influence the carbon emissions of machine tool cutting processes.

(1) Carbon emission models for machining processes, such as milling, turning, and grinding, are summarized in this review. Carbon emissions during machining processes comprise three main parts: carbon emissions from electrical energy consumption during cutting, carbon emissions from material consumption, and carbon emissions from waste disposal. Among these, carbon emissions from electrical energy consumption constitute the majority. Furthermore, carbon emissions in machining processes are influenced by various factors, which are categorized into three main groups in this work: cutting, tool, and workpiece parameters. Among them, cutting parameters have the most substantial effect on carbon emissions.

(2) Optimizing cutting parameters is crucial for reducing carbon emissions in machining processes. The effects of five cutting parameters, namely, depth of cut, spindle speed, cutting speed, feed rate, and MRR, on carbon emissions are systematically reviewed in this work, and the optimization methods employed by researchers for these parameters are summarized. Among these parameters, depth of cut exerts the greatest influence on carbon emissions, followed by feed rate and spindle speed; the least effect is associated with MRR. Furthermore, with the exception of depth of cut, the other parameters have an optimal point where carbon emissions are minimized.

(3) Emerging carbon reduction technologies with remarkable development potential, including dry machining, cryogenic machining, MQL, and several green hybrid processes, are also reviewed. The capabilities of these methods to reduce cutting fluid usage, decrease electrical energy consumption during machining, improve the cutting environment for tools, and extend tool life are highlighted. Reductions in carbon emissions related to cutting fluid consumption, energy usage, and tool wear can be achieved through these technologies. In addition, a comparative assessment of the carbon reduction potential of these novel processes is presented. It reveals that cryogenic machining can reduce carbon emissions by up to 49.17%, and cryogenic machining and its hybrid processes demonstrate substantial carbon reduction potential.

6.2 Prospects

Reducing carbon emissions from the manufacturing industry is essential in mitigating the greenhouse effect and improving the global environment. The development of carbon emission modeling and the improvement and promotion of novel process-related technologies can help reduce carbon emissions from the manufacturing industry. As shown in Fig.15, to provide direction for scholars to study carbon emission in the manufacturing industry, this study recommends the development of carbon emission modeling, enhancement of carbon emission assessment, promotion of novel processes, and application of carbon emission modeling in four aspects.

(1) Carbon emission models for machining processes involve a multitude of factors and complex influencing mechanisms. Different machining processes are affected by distinct factors and mechanisms, which make the development of accurate carbon emission models challenging. Moreover, current research on carbon emission models concentrates on traditional processes, such as turning, milling, and grinding, and provides little attention to high-pollution, high-energy consumption processes (e.g., electrical discharge machining and laser machining). Therefore, scholars must further investigate the influencing mechanisms of various factors in machining carbon emission models and prioritize the establishment of carbon emission models for special machining processes.

(2) The introduction of carbon tax policies underscores the importance of assessing carbon emissions in machining processes. However, carbon emissions during the cutting operations of machine tools are complex and dynamic. Existing carbon emission models struggle to accurately capture the dynamic nature of carbon emissions that arise from instantaneous changes in cutting power. Furthermore, machining carbon emissions are influenced by various complex factors and mechanisms. As a result of these limitations, current carbon emission models exhibit low assessment accuracy and fail to meet the precision required for contemporary calculations. Therefore, scholars must engage in in-depth research to enhance the accuracy of these models.

(3) Currently, research on the carbon emissions of novel processes is limited and lacks depth. Carbon emission modeling of novel processes is not adequately considered. For example, carbon emissions related to cryogenic media preparation and release and those resulting from the energy consumption of MQL and EMQL equipment also require consideration. Furthermore, the high costs of these novel processes restrict their application in production. However, the environmental benefits of novel processes far surpass those of traditional methods. Therefore, future work should focus on continuing in-depth research on modeling the carbon emissions of novel processes, reducing the processing costs of novel processes, promoting the application of green processes in production, and strengthening the research on the environmental aspects of novel processes.

(4) Currently, carbon emission models are still at the theoretical stage and have not been integrated into intelligent green machining nor applied in actual factory processes. Therefore, their contribution to intelligent machining in factories is limited. Future research should focus on the practical application of carbon emission models in smart factories to provide guidance for intelligent and environmentally friendly production.

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