A review of energy consumption and minimisation strategies of machine tools in manufacturing process

ABSTRACT

A global rise in demand for energy gives rise to a plethora of challenges like the increase in greenhouse gas emissions with its associated environmental impact. The manufacturing sector is a key wealth-generating sector in any economy and with significant amounts of energy consumed, they play a key role in any energy minimisation strategy and policy. In this paper, a top-to-bottom review of energy consumption and minimisation strategies of machine tools in a manufacturing process is presented. Firstly, we review the state-of-the-art on various energy consumption models for the machining process and the application of these models in production planning and scheduling. Secondly, we review the various energy reduction strategies for different manufacturing process and their limitations. Finally, we examine the implementation level of energy model in production and planning for energy minimisation problem in the literature and conclude with valuable insights on current research directions. Taken together, this review lays a solid foundation to contributing to an understanding of the state-of-the-art on various work on energy minimisation strategies especially at the production and planning level which are particularly relevant to reducing carbon footprint, costs of production and will be particularly valuable to the academia, industry and policy makers.

KEYWORDS:

• energy consumption
• energy minimisation
• machine tools
• machining process
• manufacturing process
• transient energy
• renewable energy

I. Introduction

Rapid global population growth has led to an increased demand for energy. This is especially true in emerging market economies where such growth is accompanied with some measure of economic growth thereby creating new energy demand challenges. As a result, global energy consumption has increased by more than 30% since 1990 with global energy demand projected to increase by an average of 1.6% per year from 2009 to 2035 (O. Publishing 2012). Figure 1 provides an overview of this projected increase. Fossil fuels (coal, oil and natural gas) have been the primary sources of energy accounting for about 80% of global energy source according to (P. IEA, International Energy Agency et al. 2011), and it has been the driving force of rapid globalisation and industrialisation. However, generation of energy via fossil fuels is associated with the release of harmful gases such as carbon dioxide CO₂, solid waste, etc. to the atmosphere, which causes environmental pollution. In response to this environmental challenge, the International Energy Agency (IEA) has encouraged the use of alternative sources of energy, most notably the renewable sources of energy such as solar PV, wind turbines, biomass, etc., which are environmentally friendly.

Figure 1. History and prediction of the world’s energy consumption (Yoon et al. 2015)

With renewable energy accounting for about 20% of global energy consumption, there has been an increased effort in most developed countries to continually invest in these forms of energy sources. However, economic cost, a lack of clear-cut policies and competition from fossil fuels are some of the major challenges associated with the global adoption of renewable energy. As a result, only about 28% of global energy sources (IEA 2020) have been achieved so far through these sources. We have shown in Figures 2 and 3 the penetration of renewable energy sources across the different regions in the world and the percentage of various sources of global renewable energy, respectively.

Figure 2. Global renewable electricity production by region (P. IEA, international energy agency et al. 2011)

Figure 3. Sources of global energy (P. IEA, international energy agency et al. 2011)

It, therefore, implies that the larger percentage of world’s energy is still generated through fossil fuels, and this is why the global CO₂ atmospheric concentration increasing (Yoon et al. 2015), as can be seen in Figure 4. From this perspective, the efficient utilisation of energy is a great alternative or intermediate way of reducing the energy demand, since little power can yield high production value when efficiently used. However, this intermediate approach can be best applied by looking inward to the energy consumption mechanism in all the sectors and providing method(s) of minimising wastage.

Figure 4. Global mean annual concentration of $C{O}_2$ measured in parts per million (ppm) (Yoon et al. 2015)

Among several sectors, the industrial sector has been considered to be the sector that consumes a significant percentage of the total energy produced. 37% of the world’s total energy produced is used up in this sector, and 17% of the world’s total CO₂ emission comes from this sector (Yoon et al. 2015). This percentage is on the rise, as many countries are becoming more and more industrialised to fend for the ever-growing demand of their population. In addition, the energy demand in this sector has been forecasted to follow an incremental trend of 1.3% annually from 2013 to 2025 (Jia et al. 2018). Also, about 90% of the energy consumed in the industrial sector is due to the production process and manufacturing activities (Salahi and Jafari 2016), with different machine tools generating consuming a significant part.. It has been reported (P. IEA, International Energy Agency et al. 2011) that manufacturing industries have an energy saving potential of about 25–37 EJ, which is about 18–26% of the total industrial energy consumption. It is, therefore, necessary to review the existing state-of-the-art methods and highlight the possible future approach of minimising energy in the manufacturing industries.

There has been substantial research undertaken on energy minimisation strategies from different perspectives such as machine tool builder, manufacturing industrial processes, management and government policies, energy audits (Abdelaziz, Saidur, and Mekhilef 2011); May et al. 2017). In this paper, we shall examine the review of energy consumption by machine tools and energy minimisation strategies in the manufacturing process. The aim is to review the existing state-of-the-art methods using a top-to-bottom approach and to highlight possible future approaches for minimising energy consumption in the manufacturing industry.

The rest of the paper is organised as follows. The scope of the review is presented in Section II, followed by the research aim in Section III. The state-of the-art review of preceding research based on a top-to-bottom approach is presented at appropriate levels in Section IV. Section V presents current research directions. Concluding remarks are presented in Section VI.

II. Scope

This review focuses on the energy consumption and minimisation strategies of machine tools in manufacturing industries. The demand for machine tools has recorded high growth recently, and it is projected to continue in that trend due to the increase in the investment in industrial and auto-mobile sectors. The machine tool market was estimated at 93.2 billion USD in 2012 (Yoon et al. 2015), and it is predicted to exceed 120 billion USD by 2020, with China being at the top of the chart, having witnessed 29.8 billion dollars in 2017, which is 7.5 % increment from the previous year, followed by Germany and then Japan (Global Machine Tools Market 2018). Machine tool users prioritised productivity among several other qualities used to select the machine of their choice. Research conducted by Schischke (2012) indicated that energy consumption was not a paramount factor, i.e. was considered to be the least important factor of consideration when buying a machine tool. It, therefore, implies that majority of the machine tool users are not interested in the energy consumption of the machine tool and consequently the manufacturers pay little attention to the production of the machine tool with less energy consumption. Figure 5 shows the factors in rank order of their importance from 1 to 5 (1: Most important, 5: Least important). The aspects of productivity, quality, accuracy, and availability made the top four of the charts, while design and energy consumption were the least considered factors.

Figure 5. Sale aspects survey (Schischke 2012)

This trend has continued, despite the awareness of the need to reduce energy consumption. Also, the report obtained by Global Machine Tools Market (2018) indicated that process automation, additive manufacturing, and electric vehicle were the primary market influencer in 2017 and not energy consumption level of the machine tool. The neglect of the aspect of energy consumption may be due to fewer publications on the importance of minimisation of energy consumption by the machine tool during operation (intermediate level). This review is intended to fill this gap by providing feedback for machine tool builders (manufacturers) so that energy consumption quality of the machine be among the front runner qualities in the machine building process. The structure of the world’s energy consumption can be illustrated using the block diagram in Figure 6. This breakdown is termed as a top-to-bottom description, the world total energy consumption and $C{O}_2$ emission can be minimised at the generation level, via the minimisation of energy demand, and at the intermediate level through the production and the use of machine tools. Minimising energy consumption at the level of generation is equivalent to reducing the demand for energy, and that is far from achievable. More so, minimising the emission of $C{O}_2$ can be achieved through the use of alternate sources of energy production apart from fossil fuels, i.e. renewable energy sources, this is far from achievable landmark (P. IEA, International Energy Agency et al. 2011), as can be seen from Figure 3. The optimum and immediate solution is to minimise energy consumption and $C{O}_2$ emission at the intermediate level which entails critical evaluation and assessment of the machine tools at the production and operation stages in the manufacturing industries.

Figure 6. Block illustration of energy consumption

Research has revealed that 37% of the world’s total energy consumed is from the industrial sector, and this is an increasing trend as more countries are becoming industrialised. More so, 36% of world $C{O}_2$ emission comes from this sector (I. E. Agency and I. E. Agency 2007), the concentrations of $C{O}_2$ as of 2017 is above 400 ppm, as shown in Figure 4. Detailed distribution of $C{O}_2$ emission can be seen in Figure 7.

Figure 7. Global $C{O}_2$emission (P. IEA, International Energy Agency et al. 2011)

Also, 90% of the energy consumed by the industrial sector comes from production processes and manufacturing activities, predominantly through the use of machine tools (Jia et al. 2018). Energy consumed through machine tools can be minimised at the machine builder stage and end users’ stage, the former implies the designing stage of the machine tool through the use of modern technological innovation approaches and the use of energy saving materials, whereas, the latter means the minimisation of energy through the use of the machine tool in manufacturing activities. Therefore, this review focuses on the energy minimisation at the intermediate level, i.e. the minimisation of electrical energy consumption of machine tools mainly for production activities. Since a lot of manufacturing industries cannot afford to replace all the existing machines for new energy-saving machines completely, energy minimisation is still possible via adequate production planning and scheduling strategies. This is often achieved through different energy classification level for the machine tool during production.

III. Research aim

In manufacturing industries, different operations are processed by different machines, and each machine while in service can only be in one state out of several states at a time. The energy requirement at various state of a machine differs; this implies that the same machine may require a different level of energy to process operation at different state of the machine. Several energy-saving strategies can be applied to different machines depending on the level of assessment and management requirement. Hence, numerous studies have been carried out on energy-efficient strategies at different levels of manufacturing stages. A novel classification approached was presented by Wang, Liu, and Li (2013), where the assessment of energy consumption of machine tools on various levels such as the task layer, manufacturing unit layer and machine tool layer were discussed. The task layer comprises of several manufacturing units consisting of several machine tools; the target was to estimate total energy consumed through all the layer classifications. Another classification of energy assessment based on temporary decision scale and manufacturing analysis scale was made by Vijayaraghavan and Dornfeld (2010); here, the energy saving strategy was categorised into five levels: the device/unit process level, the line/cell/multi-machines level, the facility level, the multi-factor system and the enterprise/global supply chain level. These levels are so classified following the manufacturing analysis scale, where higher level comprises of sub-multiple levels. The above classifications have been said to be non-effective for lower-level decision makers, i.e from the perspective of energy saving strategies (O. Publishing 2012). Therefore, energy saving at the device unit process (intermediate level) is generally more efficient at both lower and higher levels of decision maker (O. Publishing 2012). Monitoring of energy consumption at this level requires an accurate model and assessment for the machine tools, and most researches have focused on this issue using different strategies, each of them with different level of success. This review aims to cover the state-of-the-art on this issue and will provide a relevant recommendation regarding energy saving strategies for decision makers.

IV. Review of preceding research

The minimisation of energy at end user stage are achieved using the following steps:

a. Developing an adequate/correct energy model for the machine tools at each stage of its operation to account for actual energy consumption by the machine during operation.

b. The use of operation planning such as job scheduling on the machine whose energy model is known.

In the following section, we will proceed to review related literature on some of the techniques outlined above.

A. Energy model for machine tools

To understand, in detail, the total energy consumption of a machine tool in manufacturing industries, we will review various ways by which energy consumption has been decomposed to its basic units or elements. This is so, because energy saving level can be varied differently on different basic aspects of energy consumption. The basic elements of energy consumption are those that can be directly measured independently or indirectly measured by obtaining its value from the measurement of other related operations. Researchers have carried out the decomposition of energy into many categories that include basic energy, momentum energy (stage and spindle energy), and additional process energy. Basic energy $(E_{BASIC})$ is the constant energy consumption value during operation. It is a combination of idling energy and energy associated with the machine set-up. This energy is difficult to control by any adjustment of its process parameters, and it is a constant value that has to do with machine characteristics. The only identified way to minimise this energy is by a reduction in the process or operation time of the machine tool. The energy consumed due to spindle rotation and moving (Table 1) is referred to as spindle energy $(E_{SPINDLE})$ and stage energy $(E_{STAGE})$, respectively, and both are grouped as momentum energy since they are both functions of speed and mass, thus they can be varied by adjustment in speed and time, since the mass is constant. Additional process energy, on the other hand, is mainly the energy consumed during machining $(E_{MACHINING})$, which is due to cutting or process loading. This energy due to cutting could be varied, in accordance with process parameters and cutting conditions (Zou et al. 2017); Wu and Che 2019).

Table 1. Survey of related literature on Optimisation in machining process

Ref.	Optimisation Model	Objective Function(s)	Decision Variables	Observation	Limitation(s)/Future work
(Li et al. 2015)	Neural Network(NN) (Multi-objective)	machining time (s), Energy Consumption and Surface Roughness	Spindle speed (rpm), feed rate (mm/min), Cut depth (mm) and path spacing (mm)	A 13.08% improvement in proposed approach compared to conventional approaches	Hybrid optimisation approaches
(Li, Tian, and Zhang 2020)	Improved teaching–learning-based optimisation (ITLBO) (Multi-objective)	Surface roughness and material removal rate (MRR)	Cutting speed, feed per tooth, axial depth of cut (mm), and radial depth of cut (mm)	A 49.42% increase in MRR, 6.63% reduction in surface roughness and a 2.70% increase in cutting force	Machining constraints e.g tool wear not incorporated in model.
(Han et al. 2020)	Linear Decreasing Particle Swarm (LDPS) (Multi-objective)	Spindle speed, feed rate, depth of cut and width of cut	Cutting Power and MRR	Energy consumption	Integrated optimisation of cutting parameters and toolpaths.
(Xie et al. 2021)	Artificial Neural Network (ANN)	The machining efficiency and the loading stability of the spindle power	Feed rate of each tool path segment	12.2% increase and a 50.3% reduction in machine efficiency and spibndle power variance compared with benchmark	Constraints such as cutting force and tool wear not included in model.
(Xiao et al. 2021)	Adaptive Particle Swarm Optimisation (APSO) and NSGA-II (Single and Multi-Objective)	Processing cost and energy consumption	Spindle speed, processing time,Feed per tooth and Milling speed	Improvement in energy saving and cost. Model performance not validated with other existing models in the literature
(Arafat, Sjafrizal, and Anugraha 2020)	Artificial neural network (ANN)	Power consumption and Surface roughness	Spindle speed, depth of cut and feed rate	95.4% prediction accuracy for the ANN model	Variation in physical properties of material used in experiment could impact on prediction accuracy.

Energy consumption by machine tool for grinding is divided into fixed power and grinding power (Park et al. 2009). The total energy consumed in machine tool is grouped into three: constant start-up operation’s energy, run-time operation’s energy and material removal operation’s energy as explained by Kong et al. (2011). The prediction of energy consumption from numerical control (NC) codes of a machine tool was carried out by He et al. (2012) through the analysis of energy-consuming components during machining. They categorised machine tool components into the spindle, axis feed, tool change system, coolant pump, and fixed energy consumption, and thereafter, they derived total energy consumption values for machining processes using the NC tags. The theoretical energy demand of a machining process can be obtained using equation [1] (O. Publishing 2012), for $F_c$F_c signifies cutting force; v, denotes cutting speed; and t equal the cutting time. The prediction of cutting forces in 3-axis milling of sculptured surfaces directly from the CAM tool path was carried out by Zeroudi, Fontaine, and Necib (2012). Earlier researches, such as by Kim and Ehmann (1993) and Lin, Lee, and Wu (2001), have also contributed to the modelling of cutting force. This form of energy computed using these approaches is called tool-tip energy of cutting processes, and it is the minimum energy required for material removal (Li and Kara 2011), this, however, does not account for other energy consumed by auxiliary operations like rapid positioning, tool changing, cutting flood spraying, lightening, etc.

(1) $E=F_c\times v\times t$(1)

Energy consumed during machining is grouped into two parts as shown in Li and Kara (2011), which includes: Specific tool-tip energy (STE) and Specific unproductive energy (SUE). They defined STE as the energy consumed by a specific cutting force during the machining operation, and SUE as the energy is used up due to friction during cutting. They expressed cutting force $F_c$ as a function of specific cutting force $k_c$ and cutting geometry of the material as in (2), where f is the feed, d is the depth of cut, $\gamma$ is the approach angle, ${\gamma }_o$ is the cutting rake angle and k is the shear stress.

(2) $F_c=k_c\times f\times d=\frac{1-0.01{\gamma }_o}{fsin\alpha }k\times f\times d$(2)

In Balogun and Mativenga (2013), the authors estimated the energy requirements for machining experiments in an attempt to minimise resources usage (energy). Using mathematical approaches to characterise energy requirements for the machine modules used, they were able to estimate the total energy consumption of a MAC-V2 Takisawa Milling Machine and Mikron HSM 400 machining to be at 399 Whr and 415 Whr. The study found that with machine tools left in no-cutting mode for a long time, the energy footprint was significantly increased. However, the work did not benchmark their model with other machine tools energy minimisation models such as that in Diaz, Redelsheimer, and Dornfeld (2011) and Santos, Pereira, and Ferreira (2020). In contrast to the research above, Liu, Xie, and Liu (2015) proposed a model for predicting the energy consumption of the main driving system (MDS) – spindle motor and mechanical transmission system – of machine tools in the machining process. The authors proposed a practical model which involved dividing the machining process in to distinct stages (start-up, idle and cutting), and evaluated the energy consumption model for the start-up and idle process at discrete speeds. The cutting power based on the machining parameters was calculated and this was then used to predict the energy consumption of the cutting process. To verify the practicability and accuracy of their model, the authors utilised the model in the MDS of a CNC lathe (C2-6136HK/1).

A large body of the literature on energy models in manufacturing industries (including those described above) have been applied to specific machines. Taking a different approach, Zeng et al. (2019) developed a general empirical model using actual cutting experimental data to develop an energy consumption model for milling machines at certain power levels. Using the Material Removal Rate (MRR) as the cutting tool, they utilised experimental data to fit a regression model which was developed based on the analysis of the relationship between the power ratio, energy consumption and MRR. Results obtained showed an accuracy of over 80% for the cases considered. However, a limitation of this work is that it could only predict energy consumption with similar or same power capacity and could only be applied to certain ranges of MRRs due to limitations of the experimental data used. We summarise other relevant work in literature that examines optimisation approaches for energy consumption in machining processes in Table 1.

Other energy machining process can be found in Dahmus and Gutowski (2004) and Diaz et al. (2010). Detailed surveys of energy consumption using these basic decompositions have been extensively carried out by (O. Publishing (2012) and Peng and Xu (2014), including different models proposed by various researchers and we present a comparative survey of some of these works, as shown in Table 3. To this end, the behaviour and energy demand by machine tools cannot still be well described and modelled because there is still no sufficient method to model the energy demand of machining process that will capture the different energy demands due to the basic motion of CNC machine tools under different operation states. This is essential because machine can be in different states during machining activities.

Table 2. Therbligs of CNC machine tools (Jia, tang, and lv 2014)

Table 3. Computational methods for Therbligs power (Jia, tang, and lv 2014)

Therbligs	Power formulae	Parameters
Spindle power	Due to the complexity, experimental and	$P_{sn}$ is unloaded
	statistical approach is used.	n is spindle rotation speed.
	$P_{sn}=f(n)$	A,B are fitting parameters
	$P_{sn}=A+B_n$
X, Y, Z axis feed power	$P_f^r=+P_y^r+P_z^r$	$P_f^r$ is rapid positioning power (kW).
	$P_f^c=P_x^c+P_y^c+P_z^c$	$P_x^r$, $P_y^r$, $P_z^r$ the respective power of x,y,z axis feed motors during rapid positioning.
	$v_f=\sqrt{v_x^2+v_y^2+v_z^2}$	$P_c^f$ is power cutting feed (kW)
		$P_x^c$ is power of x-axis motor at speed $v_x$
		$P_y^c$ is power of y-axis motor at speed $v_y$
Cutting Power	$P_{(}Tip-cutting)$ = $MRR\times SEC$	$P_{(Tip-cutting)}$ is the cutting power of tool tip (kW)
	$P_{cut}=\alpha P_{(Tip-cutting)}$	$MRR$ is the material removal rate $(c{m}^3/s)$
	MRR (milling) = $\frac{1}{60}\times \frac{v}{\pi d}\times z\times f_z\times w\times a_p$	$SEC$ is specific energy consumption $(kJ/c{m}^3)$
	MRR (turning) = $\frac{1}{60}\times v\times f\times a_p$	$\alpha$ is power loss coefficient
	MRR (drilling) = $\frac{1}{240}\times v\times f\times d$	$v$ is the cutting edge speed $(m/min)$
	MRR (other) = $\frac{V_m}{t}$	$d$ is the cutting tool diameter
		$f$ is the feed speed
		$a_p$ is the cutting depth (mm)
		$V_m$ is the volume of removal material $(c{m}^3)$
		$t$ is the machining time (s)

In Jia, Tang, and Lv (2014), several machine states and activities are represented using a series of Therbligs, which are subsequently used to model the behaviour and energy demand of machining processes. They established a mathematical model for the energy demand of machining processes by linking the activity and Therblig with machining state. They identified 14 types of Therbligs for CNC machine tools that can be used to describe its energy states, as shown in Table 2.

Three out of 14 Therbligs are not used (A-axis Rotate, B-axis Rotate and C-axis Rotate), as they are only applicable when considering multi-axis CNC machine tools. Each Therbligs is associated with basic energy demand. Therbligs are classified into two types: The first is those that are indirectly related to material remover, and their power drawn is constant and do not change with cutting parameters during the machining process, they are: BM, L, CC, TS, TC and CFS. Their Therblig’s power is obtained via experimental measurement approach. For example, to obtain the Therblig power BM, (3) is used. Where $P_{BM}{,}_i$ is the measured power value of Therblig-BM at point i, N is the number of measurement points.

(3) $P_{BM}=\sum _{i=1}^N\frac{P_{BM}{,}_i}{N}$(3)

The second category is Therbligs that are directly related to material remover, and they include SR, XF, YF, ZF and C; their Therblig’s powers are not constant during machining processes and are highly dependent on the machining parameters. The power of each of Therblig in this category are obtained as illustrated in Table 3.

They developed a mapping relationship between Therbligs, and machining state as seen in Table 4. Also, the mapping relationship between machining state and activity is presented in Table 5.

Table 4. MAPPING RELATIONSHIP BETWEEN THERBLIGS AND MACHING STATES (Jia, tang, and lv 2014)

Table 5. Mapping relationship between machining states and activities (Jia, tang, and lv 2014)

Activity	Machining State
Machine Power on	$q1$ Basic module run
Spindle rotate	$q2$ spindle rotation;$q3$ Rapid position; $q4$ Feed $q9$ Tool select;$q10$ Tool change
Select cutter	$q9$ Tool select
Change cutter	$q10$ Tool change
Rapid positioning – x,y,z; Rapid Positioning – xy,-xz,-yz; Rapid positioning – xyz	$q3$ Rapid position
Feeding -x,y,z; Feeding -xy,-xz,-yz; Feeding -xyz	$q4$ Feed
Cutting -x,y,z; Cutting -xy,-xz,-yz; Cutting -xyz	$q5$ Cut
Spindle stop	$q1$ Basic module run; $q3$ Rapid position; $q4$ Feed; $q9$ Tool select; $q10$ Tool change
Cutting flood on	$q1$ Basic module run; $q2$ Spindle rotation $q3$ Rapid position ;$q4$ Feed; $q6$ Cut and Cutting flood spray; $q8$ Cut and Chip convey and Cutting flood spray; $q9$ Tool select; $q10$ Tool change
Cutting flood off	$q1$ Basic module run; $q2$ Spindle rotation; $q3$ Rapid position;$q4$ Feed; $q5$ Cut; $q7$ Cut and chip convey; $q9$ Tool select; $q10$ Tool change
Chip conveyor on	$q1$ Basic module run; $q2$ Spindle rotation; $q3$ Rapid position; $q4$ Feed; $q7$ Cut and chip convey; $q8$ Cut and chip convey and Cutting flood spray; $q9$ Tool select; $q10$ Tool change
Chip conveyor off	$q1$ Basic module run; $q2$ Spindle rotation; $q3$ Rapid position;$q4$ Feed; $q5$ Cut; $q6$ Cut and Cutting flood spray; $q9$ Tool select; $q10$ Tool change
Light on	Except $qo$ Machine off
Light off	Except $qo$ Machine off

Matrix of Activity-Therblig relation (ATR) and time activity (T) were formed based on the execution states of Therblig on each activity, and the time taken for the actual machining process. where $S_{ij}$ = (i = 1,… .n; j = 1,… .m) represents execution state Therblig i in activity j, expressed by 0 or 1. $S_{ij}$ =0 implies the non-execution state of Therblig; $S_{ij}$ =1 means execution state of Therblig. $t_i(i=1,\dots .,n)$ is the execution time of activity i. From (4) and (5), the Therblig time (TT) is calculated as given in (6)and Therbligs power (TP) is expressed in the matrix equation of (7) as:

(4) $TT=T\times ATR$(4)

(5) $\left[\begin{array}{ccccc}{S}_{11}& S_{12}& .& .& S_{1m}\\ S_{21}& S_{22}& .& .& S_{2m}\\ .& .& .& .& .\\ .& .& .& .& .\\ .& .& .& .& .\\ S_{n1}& S_{n2}& .& .& S_{nm}\end{array}\right]$(5)

(6) $\left[\begin{array}{ccccccc}{t}_1& .& .& .& .& .& \hspace{0.17em}\\ .& t_2& .& .& .& .& \hspace{0.17em}\\ .& .& .& .& .& .& \hspace{0.17em}\\ .& .& .& .& .& .& \hspace{0.17em}\\ .& .& .& .& .& .& \hspace{0.17em}\\ .& .& .& .& .& .& t_n\end{array}\right]$(6)

(7) $\left[\begin{array}{ccccc}{p}_{11}& p_{12}& .& .& p_{1m}\\ p_{21}& p_{22}& .& .& p_{2m}\\ .& .& .& .& .\\ .& .& .& .& .\\ .& .& .& .& .\\ p_{n1}& p_{n2}& .& .& p_{nm}\end{array}\right]$(7)

where $p_{ij}(i=1,\dots n,j=1,\dots .m)$ represent the power of Therblig j in activity i.

Therblig energy was then computed using Equation (8) below

(8) $E_j=T{P}_j^T\times T{T}_j$(8)

where $E_j$ is the energy consumption of Therblig j of machining processes, hence the total energy consumption E of the machining process was computed from (9). This energy model approach is applied to a case study (Jia, Tang, and Lv 2014) involving face turning, rough external turning, fine external turning, chamfering and grooving with results showing that the introduction of Therblig provides energy optimisation and efficiency improvement. The comparison between the calculated cutting power and measured power is shown in Figure 8.

Figure 8. Comparison of calculated and measured power for five main machining processes (Jia, tang, and lv 2014)

Also, the comparison of calculated and measured energy value for machining a sample work-piece, is shown to have 94.86% accuracy, as shown in Figure 9.

Figure 9. Comparison of calculated and measured energy value for machining sample work-piece (Jia, tang, and lv 2014)

When this approach was compared with that proposed by Li and Kara (2011), for the same sample (Stepped shaft work-piece), the lowest accuracy was found to be 94.86%, the highest accuracy was 98.95% and the average accuracy was 97.09% (Jia, Tang, and Lv 2014), while the lowest, highest and average accuracy of energy estimation using method given by Li and Kara (2011) was 90.99%, 95.56% and 92.39%, respectively. Similar research can be found by Shen et al. (2018), where a practical energy consumption prediction method for CNC machine was carried out Despite some level of achievement compared with existing energy model approaches, it was revealed that models obtained based on this method show some discrepancy when compared with actual energy consumed by the same machine tool that the model represents. It is so because the models were based only on the steady state of the machine (Li and Kara 2011). In He et al. (2012), it was shown that the calculated energy obtained from a steady state energy model was 9.3% less than the value obtained from direct energy measurement, this is the case because the transient state of machine was neglected. Such a model was presented by Zhang et al. (2016).

Recently, energy decomposition has been also grouped into two major categories where the previous grouping can be a subset. These categories are steady and transient state energy consumptions. The transient energy model is obtained by consideration of the finite state of the machine and understanding that the machine can only be in one of the states and transition from a state to other results in a transient state. Machining in transient state encompasses motor starting, instant increment in the speed or torque of moving parts, loading, etc. this usually results in some spike in the power profile during these states, referred to as peak power phenomena as seen in Figure 10. Though the duration of these spikes or peaks of power is minimal, various studies have shown that they should not be neglected (Seyedzadeh et al. 2019), since the accumulation of these power over some time can sum up to a significant amount of energy.

Figure 10. Power peaks in transient state (Jia et al. 2017a)

Previous researchers have neglected this transient energy consumed by machine tools. Only a very few researchers have accounted for this energy demand (Jia et al. 2017a; Jia et al. 2017b), this implies that more research regarding the application and model of this energy concept is still at the infant stage. In the research carried out by Jia et al. (2017b), the transient energy is considered in the energy demand, and it is model using finite state of the machine. A Pareto chart of energy consumption of transient state indicates that spindle rotation (Ls$\to$Hs), positioning (Ls$\to$Hs), cooling (off$\to$on) and tool changing (off$\to$on) are the critical transient state that consumed more than 80% of total transient energy demand as shown in Figure 11.

Figure 11. Pareto chart of energy consumption of transient state (Jia et al. 2017a)

From the experiment conducted and case studies performed on a CK6153i CNC lathe, the results obtained shows the accuracy with this method is generally above 90% for the state transition cases. Specifically, the accuracy was improved by 33.01% and 54.71% for the two-state transition cases (spindle rotations (Ls$\to$Hs)-[0$\to$750 r/min] and (Ls$\to$Hs)-[500$\to$1000 r/min]) as compared with the average power model. This research however considered only the selected transient energy demand associated with few transient states. Energy consumption modelling of machining transient states based on finite state machine was also investigated by Jia et al. (2017a). This research is similar to that conducted by Jia et al. (2017a) because both make use of Therblig energy based, but with advancement, in the construction of state transition of machine tools, and its extended application to CAK6150Di machine. The result from the experiment, and case study, shows some level of improvement in the prediction of energy demand for the key transient state considered when compared with average power model approach as compared in Figure 12.

Figure 12. Energy consumption distribution of transient states and comparison of predicted and measured energy (CAK6150Di) (Jia et al. 2017a)

Hence, a better and more accurate energy model is achieved with the inclusion of the transient state energy demand. However, the research does not include machine tool (off$\to$on), which is also among the key transient states, as shown in Figure 11. Machine tool (off$\to$on) is manually operated and varies with different operator and that makes actual energy consumption due to machine tool (off$\to$on) difficult to obtain. In addressing this issue, an energy modelling method of the machine-operator system for sustainable machining was presented by Jia et al. (2018). In their work they included activities related to machine tool and operators in the machining process. The total energy consumed (TEC) by machine operator system (MOS) was obtained by the summation of machine energy consumption (MEC), and operator energy consumption (OEC). The MEC was obtained using the approach in (Jia, Tang, and Lv 2014), whereas, OEC was modelled by dividing the workman’s operations into simple elements of motion (Therbligs) hat classified activities of the operator in an industrial environment (see Table 6). The intensities of different activities vary and are expressed using metabolic equivalent (MET), which is the amount of oxygen consumed while sitting at rest and is equal to 3.5 ml O₂ per kg of body weight per minute. Table 7 shows the intensity of regular operator activity.

Table 6. Typical activity types of the operator in an industrial environment (Jia et al. 2018)

Table 7. Intensity of typical operator activities (Jia et al. 2018)

No	Activity of operator	Activity intensity	Annotation
1	Standing-Still	1.3MET	Treated as a constant value
2	Standing-Operation	1.8MET	Treated as a constant value
3	Sitting-Still	1.3MET	Treated as a constant value
4	Sitting- Operation	1.5MET	Treated as a constant value
5	Walking	$f(v_w)$	$v_w$ is walking speed $km/h$
6	Running	$f(v_r)$	$v_r$ is running speed $km/h$
7	Loading Workpiece	3.0MET	Treated as a constant value
8	Unloading Workpiece	3.0MET	Treated as a constant value
9	Driving Forklift	1.3MET	Treated as a constant value
10	Resting Metabolism	1.0MET	Treated as a constant value

The framework that is used to obtain TEC of MOS is shown in Figure 13, with the total energy consumption as expressed in (10).

Figure 13. Framework of the total energy consumption of MOS (Jia et al. 2018)

(9) $\begin{array}{c}TEC=MEC+OEC=\sum _{i=1}^n\sum _{j=1}^{m_i}{\int }_0^{t_{ij}}{P}_{ij}^{mt}(t)dt+\\ \sum _{i=1}^n\sum _{k=1}^{u_i}{\int }_0^{t_{ik}}{P}_{ik}^{op}(t)dt\end{array}$(9)

where $P_{ik}^{op}(t)$ is the power function of the operator during sub-stage k in machining stage i; $t_{ik}$ is the duration of sub-stage k in machining stage i; $P_{ij}^{mt}(t)$ is the power function of the machine during sub-stage j in machining stage i; $t_{ij}$ is the duration of sub-stage j in machining stage i. This model was applied to a case study (Jia et al. 2018) whose operator activities and machining stages were broken down, as shown in Figure 14.

Figure 14. Five stages of the machining case (Jia et al. 2018)

The result obtained is shown in Figure 16. For detail of the calculation of the energy, the reader is been referred to Jia et al. (2018).

The result obtained is an improvement from the previous ones without the operator model. Hence, it has given room for new energy-saving strategies which has an energy saving potential of 15.85% in the machining case under consideration. Research regarding the application of this model is required as adequate energy model for machine tool that combine both the static and transient state of machine is required to account for its energy usage so that such model can be used in formulating energy objective in the planning and production process.

An efficient energy model is not enough if it is not correctly integrated into production processes (Liu et al. 2014). Nevertheless, it has been pointed out that an incomplete understanding of energy consumption with regards to process level will be a problem in the evaluation of energy consumption by the machine or in the creation of approaches to minimising energy at the operational level (Kordonowy 2002; Weinert, Chiotellis, and Seliger 2011). Therefore, we shall review research on energy minimisation via operational strategies.

B. Energy minimisation via operational methods

The use of new energy-efficient machines may require additional investment and impose enormous pressure on companies, so managers seek alternative measures of minimising energy consumption through operational processes. In manufacturing industries, where the machine tools are used to carry out large job productions, i.e. job shop, flow shops, etc., the standard strategy to employ is job scheduling. In job scheduling, jobs are assigned to machines following a certain routine or precedence so that machines are effectively put to use, and only when their services are needed (Pinedo 2012).

Initially, the most common objectives that are mostly considered by researchers in scheduling problems are production related objectives such as makespan, mean tardiness, inventory cost, etc., energy minimisation as an objective is often neglected. But, due to recent advocating of the need to minimise energy consumption and emission at the production level (end users). Also, the fact that the operational method has proven to be a feasible and effective method to reduce the energy consumption of manufacturing companies (Fang et al. 2011; Mouzon and Yildirim 2008). Researchers have responded by carrying out further research on scheduling problems to minimise the energy used up during production using several strategies. In this section, we will present a review of some of these research and also highlights some key future research areas.

The major areas of energy improvement techniques during operation planning that are popular in the literatures are: Reduction of non-processing energy (NPE) consumed by the machine; Changing of operation speed level during machining process; Time of use strategies; and reduction of total energy during production.

1) Reduction of NPE consumed by machine: This method eliminates or reduces the energy consumed by the machine while it is not producing. Three levels of power consumption by machines during manufacturing processes has been identified vis-a-viz: the idle power, start-up power, and machining power. The first two are mostly referred to as the NPE, and the third categories is known as processing energy consumption (PE) (Mouzon 2008; Liang et al., 2015; Liu et al. 2016). The target in this technique is to minimise the non-processing energy consumption of the machine tools. To apply this method of energy minimisation, the energy consumed during each idle period of the machine and the energy used up when the machine is switched ‘off’ from ‘on’ state and back to ‘on’ state are calculated and compared. When the consumed energy during the idle period is higher than a particular value (Breakeven value), which is the value that is economically justifiable to shut down the machine for that particular idle period, then the machine is shut down or otherwise left to consume idle power. The research by Hu et al. (2017) investigates the energy consumption associated with changes in spindle rotation and tool changes; this research considered the minimisation of the NPE during operation using Ant colony optimisation algorithm. This approach has been successfully deployed by many other researchers (Mouzon and Yildirim 2008)(; Liu et al. 2016; Che et al. 2017; Firas and Denis 2015). This approach may, however, not be suitable for all machines, especially machines with a large warm-up time and warm-up energy such as an industrial oven. In this case, it may be economical not to switch off the machine until after the entire production.

2) Changing machine’s operating speed: This method of energy minimisation techniques is based on variable speed selection approach for machining process during operation. Energy consumed by a machine is proportional to the speed with which the machine is processing the operations. This implies that a machine operating at a higher speed consumed much power, though its processing time is reduced. Based on this principle, energy minimisation algorithms for parallel machine scheduling through variable speed selector (speed scaling approach) was carried out by Wu and Che (2019). They create several variable speed selector vectors from which the machine optimally selects the appropriate speed to be used for the processing of the operation so that minimal energy is used up. Similar technique was used (Bampis et al. 2012) where a polynomial algorithm was used to solve a speed-scaling single-machine scheduling problem. they considered the use of power-down strategies to minimise total energy consumption. Also, a flexible speed-scaling flow shop scheduling problem was formulated in (Dai et al. 2013) with objective of minimising both makespan and total energy consumption. This problem was solved with the help of a genetic-simulated annealing algorithm. Other researchers that have employed speed scaling approaches in energy minimisation are but not limited to Ding, Song, and Wu (2016), Fang et al. (2016), Chen, Luo, and Zhang (2011) and Luo, Zhang, and Fan (2019). This speed scaling approach may compromise the quality of the finished jobs since the quality of jobs is sometimes a function of speed.

3) Time of use technique: The production time is classified into peak and off-peak time, the jobs scheduling is then strategised such that, a significant amount of jobs is processed during off-peak periods, i.e. when the peak-power load is reduced, so that cost and energy of production are minimised. This technique is referred to as the time of use (TOU) strategy and has gained acceptance by many researchers as means of minimising cost of energy usage by the machine. The energy minimisation through the reduction of peak-power load inflow shop was considered in (Fang et al. 2011). A similar approach was used by Bruzzone et al. (2012), where a modification of jobs scheduled in flexible flow shops was developed in order to adjust to maximum peak power constraints. Also, a single-machine scheduling problem under the time-of-use (TOU) strategy was considered by Che, Zeng, and Lyu (2016). A new mathematical model which determines efficient scheduling for minimisation of makespan and electricity consumption cost (ECC) in flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy was presented by Zhang et al. (2017) and Sharma et al. (2015). This TOU approach may be difficult for the large-scale manufacturing industry.

4) Reduction in total energy consumption: Other researchers minimise total energy consumption by machine during jobs production. The total energy consumed by a machine during operation is computed by taking the product of the constant power consumed by the machine and the whole period in which the machine tool is used. Researchers use this total energy reduction methods as one of the objectives in formulating a scheduling problem. It is important to mention that the scheduling problem is one of the hardest combinatorial optimisation problems (Al-Hinai and ElMekkawy 2011; Pinedo and Hadavi 1991), as it is a non-deterministic polynomial (NP-hard) problem (Fattahi, Mehrabad, and Jolai 2007). Therefore, classical mathematical optimisation methods are not suitable for solving this type of problem (Marler and Arora 2004), hence optimisation techniques are used. The performance of optimisation techniques is problem dependent or problem specific; however, optimisation techniques is good if it possess both good exploration and exploitation capabilities; the exploration quality refers to the ability of an optimisation algorithm to seek for the global optimum in the solution space of an unknown optimisation problem, while the exploration process refers to its ability to apply the knowledge of previous and current solutions to look for the better solution (Estrada-Wiese, del Ro-Chanona, and Del Ro 2018). The performance of the optimisation techniques can be evaluated using convergence time, computation level involved, multi-objectives/constraints capabilities, and ease of implementation. This paper does not review the performance of optimisation techniques available but rather, mentions the type that is used in the research considered to solve energy minimisation problems.

In Lei, Gao, and Zheng (2017), a novel teaching-learning based optimisation algorithm was used to minimise the total energy consumption in a hybrid flow shop using the steady power consumption approach. Energy-efficient dynamic scheduling for a flexible flow shop was presented by Tang et al. (2016), they used an improved particle swarm optimisation to minimise the total energy consumption of the scheduling problem. The minimisation of total energy consumption by machine tool was also carried out in flexible flow shop (Dai et al. 2013), an energy efficiency objective problem was formulated, and solved using an improved genetic-simulated annealing algorithm. In Yildirim and Mouzon (2011), a mathematical model for minimising total energy consumption, and maximum completion time on machine tool was carried out. The total energy consumption of a single machine was also minimised by Shrouf et al. (2014) through production scheduling. A mixed integer linear multi-objective optimisation for two-machine flow shop problem was carried out by Mansouri, Aktas, and Besikci (2016), they developed a low bound heuristic that minimised both the total energy consumption and the make-span. An energy efficient job shop scheduling problem was solved using multi-objective genetic algorithm with local search capability in Zhang and Chiong (2016), where both the total weighted tardiness and energy consumption were minimised. Similarly, in Lei, Zheng, and Guo (2017), the total energy consumption in a flexible job shop scheduling was minimised using a shuffle frog-leaping algorithm. In Luo et al. (2013), an ant-colony optimisation algorithm was to solve a minimisation problem involving production efficiency and cost of electric power in a hybrid flow shop. It was revealed in the research presented by Hu et al. (2018) that the machining energy consumption can be reduced via optimum sequencing of the features of the part being processed by the machine at the planning stage. Using different optimisation techniques, other studies that considered the minimisation of total energy consumption during production includes but not limited to Fu et al. (2019), Kawaguchi and Fukuyama (2018), Shukla, Kumar, and Kushwaha (2021), Gao et al. (2011).

All of these energy minimisation techniques on machine tool considered steady state energy consumption of the machine and the transient energy consumption by the machine is ignored. The transient energy consumption cannot be ignored since the machine always transits from one steady state to another during operation. To the best of our knowledge, all the research on energy minimisation at the operational level ignored the transient energy consumption aspect of machine tools.

V. Research directions

In various machine tool models, very few researchers include the transient state of the machine in their formulated energy model, and this has a considerable impact on the efficiency of these models. Also, at the machine operations level, researchers have reported several energy minimisation methods central to job scheduling approach. Techniques such as energy minimisation using variable speed selector, minimisation of non-processing energy, minimisation of total energy consumption, etc., have all been reviewed. Majority of these researches have only considered the steady state of the machine tool in their energy minimisation approaches, little or no attention is paid on the energy consumption during the transient operation states of the machine, as a result of this, accurate monitoring of the total energy consumed by the machine tool during operation is not feasible. Therefore, more research should be encouraged on the application of energy modelling of machine tools, that will include all the possible states (transient and steady state) of the machine during machine operations

VI. Conclusion

Energy is a valuable commodity that is inevitable for the sustainability of lives. The demand for energy is continuously increasing in proportion to the world population growth, and development. As more than 80% of this energy is sourced from fossil fuels, harmful emissions are enormous and, on the rise, this has to be checked as recommended by IEA, and other environmental governing bodies. As a result of this, more environmentally friendly methods of energy generation have been encouraged, though the response and the investment towards alternative (ecologically friendly) sources of energy production are still far from achieving the landmark because less than 14% of the world energy is generated from these sources at the moment. Based on our review, energy minimisation through efficient use of energy is seen as the intermediate and immediate solution. Therefore, this research has reviewed the existing methods that have been deployed so far in this regard, using a top-to-bottom approach, while also, highlighting area that requires more attention. Also, with the industrial sector accounting for about 37% of the world total energy consumption, with 90% of the energy consumed by the industrial sector via manufacturing processes and 36% of $C{O}_2$ emission, with machine tools taking the highest percentage, the actual amount of energy consumed by machine tools is unknown due to lack of efficient energy models. Researchers had formulated several energy models for machine tools during operations so that the actual amount of energy consumption on each operation process by the machine is estimated. The output from the energy model serves as input for machine tool builders (to adjust the designed method), and end users (guides on use for application), so that appropriate steps/methods are put in place to reduce the energy demand by the machine tools. From the literature survey, we have identified that very few researchers had considered the transient state of the machine in their energy model; therefore, more research is needed in this area. At the operations level, researchers have reported several methods centrally to job scheduling for energy minimisation, methods like variable speed selector, minimisation of non-processing energy, or minimisation of total energy consumption, have all been reported. To the best of our knowledge, almost all of the researches had only considered the application of the steady-state model of the machines in their methods of energy minimisation, while neglecting energy consumption due to the transient state of machine tools. The energy consumption during the transient state of machines is inevitable for accurate monitoring of the actual energy used up by the machine tools. Therefore, more research is also required in the application of transient energy consumption model at the operation level. In conclusion, to reduce the emission and energy consumption, more investment should be made in the production of energy from environmentally friendly sources, more researches on efficient energy models for machine tool should be considered; finally, the application of energy models incorporating transient energy consumption should be researched and included in production planning for manufacturing industries.

Acknowledgments

The authors would like to acknowledge and thank the management of Petroleum Technology Development Fund (PTDF), Nigeria for their financial and moral support in conducting this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Petroleum Technology Development Fund, Nigeria [PTDF/ED/PHD/PKA/1140/17]; Petroleum Technology Development Fund, Nigeria [PTDF/ED/PHD/YLA/1114/17].

Notes on contributors

Lukman Aminu Yusuf

Lukman Yusuf received the B.Eng. degree in Electrical Engineering from Kano State University of Science and Technology (KUST), Wudil and the M. Eng degree from Bayero University, Kano, Nigeria. He is currently pursuing a PhD degree at the Department of Electrical and Electronic Engineering, University of Glasgow.   His research interest includes AI, Acoustic Cavitation and Energy modelling.

Kayode Popoola

Kayode Popoola received the B.Eng. and M. Eng. degree in Electrical Engineering from Bayero University, Kano, Nigeria. He is currently pursuing a PhD degree at the Department of Electronic Engineering, University of York. His research interest includes Aerial platform wireless communication, Vehicular networks and Energy modelling.

Haruna Musa

Haruna Musa is currently a Professor of Electrical Engineering, Bayero University, Kano. He holds a PhD and MSc degree from Bayero University, Kano and University of Lagos respectively. He has over 40 publications in local and international journals. His area of interest include Distributed generation systems, Intelligent optimization techniques and Energy modelling.