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Article history:Received 17 November 2013Received in revised form16 May 2014Accepted 15 September 2014Available online xxx

Keywords:Machine toolEnergy consumption

Three Gorges Dam, which is the largest hydroelectric power stationof the world (Hu, 2012).

Hu (2012) also demonstrated that the average energy efciencyof machining process is less than 30%. For example, the energyefciency of a case described by Gutowski et al. (2009) is only 14.8%.As a result, machining systems have great potential for energy

mental emissionsthe CO2 emissionse power grid) cor-f a 22 kWmachinel to that of 61 SUVof environmentalo high.potential for both

energy savings and environmental emissions reduction, theresearch on the energy consumption of machine tools and themachining process has grown rapidly in recent years, with a focuson the following aspects.

(1) Energy efciency assessment: the US Department of Energyestablished an Industrial Assessment Center, which is aimedat promoting the energy efciency of manufacturing pro-cesses and at assessing and researching the energy* Corresponding author. Tel.: 86 23 65104172; fax: 86 23 65105098.

Contents lists availab

Journal of Clean

.e ls

Journal of Cleaner Production xxx (2014) 1e7E-mail addresses: iu@cqu.edu.cn, 13908304060@139.com (F. Liu).1. Introduction

The machining systems that mainly consist of machine tools arenumerous and are used in a wide range in industries. The totalamount of energy consumption by machining systems in the worldis extremely high. For example, machining in China involves over 7million machine tools, whose total power is greater than 70 millionkilowatts; this is three times more than the installed capacity of the

savings. According to the analysis of the environof machine tools provided by Gutowski (2009),(calculated by the relevant data of the USA statresponding to the annual energy consumption otool (congured with auxiliary assembly) is equaautomobiles, which indicates that the potentialemissions reduction of machining systems is als

Because the machining systems have greatPredictionMachining processhttp://dx.doi.org/10.1016/j.jclepro.2014.09.0580959-6526/ 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Liu, F., et almachining process, Journal of Cleaner Produa b s t r a c t

The machining systems that mainly consist of machine tools are numerous and are used in a wide rangeof applications in industry, which usually exhibit very low energy efciency; as a result, they have greatpotential for energy savings and environmental emissions reduction. To achieve such energy savings, theprediction of the energy consumption of the machining process has great signicance. Also, it canprovide a decision-support tool for the establishment of an energy consumption quota, the energy-saving optimization of cutting parameters, energy efciency evaluation, and so on. Although existingresearches on the energy consumption prediction of machine tools have been performed, a practicalmethod is still lacking. Therefore, a new method for predicting the energy consumption of the maindriving system of a machine tool in a machining process is proposed. First, a machining process is dividedinto three types of periods: start-up periods, idle periods and cutting periods. Second, the energy con-sumption prediction models for each type of period and the total prediction model for the machiningprocess are established. Third, by measuring energy consumption data of the start-up and idle processesat discrete speeds, the functions of the tted curves of the energy consumption of start-up periods andidle periods are obtained, which enables the energy consumption of the start-up period and the idleperiod at any different speed to be predicted. Fourth, using the cutting power calculated based on themachining parameters and the additional loss coefcients obtained based on the additional loss co-efcients equation set, the energy consumption of the cutting periods can be predicted. Finally, theprediction error analysis model is constructed, and the reasons why the error is not big in the predictionare expounded. The results of a case study indicate that the method is practical and has good applicationprospect.

2014 Elsevier Ltd. All rights reserved.a r t i c l e i n f oA method for predicting the energy consystem of a machine tool in a machinin

Fei Liu a, *, Jun Xie a, Shuang Liu b

a State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 4b College of Mechanical and Power Engineering, Chongqing University of Science & Tec

journal homepage: www., A method for predicting thection (2014), http://dx.doi.ormption of the main drivingprocess

0, Chinaogy, Chongqing 401331, China

le at ScienceDirect

er Production

evier .com/locate/ jc leproenergy consumption of themain driving system of amachine tool in ag/10.1016/j.jclepro.2014.09.058

Lau et al. (2008) proposed an energy consumption changeforecasting system using fuzzy logic. The approach can use to helpthe manufacturer forecast the energy consumption change in theplant when certain production input factors are varied.

In conclusion, some signicant research studies on the energyconsumption prediction of machine tools have been performed.However, to enable practical application of the prediction methods,the following problems must be solved. First, the existing researchstudies mainly refer to energy consumption of a specic machiningprocess, a practical workpiece machining process consisting of the

ner Production xxx (2014) 1e7consumption and energy efciency of industrial work site(Industrial Assessment Centers (IACS), 2009).

(2) The design of energy-efcient machine tools: the Interna-tional Standardization Organization (ISO) is drafting thedocument entitled Machine Tools d Environmental evalu-ation of machine tools dPart 1: Design methodology forenergy-efcient machine tools (ISO/TC 39/SC, 2012), whichproposes a series of design methods and norms of energy-efcient machine tools.

(3) Promoting the energy efciency of the machining process:for example, Mori et al. (2011) analyzed the factors thataffect the energy consumption of machine tools and pro-posed methods to reduce the power consumption in ma-chine tool operation. Kong et al. (2011) developed a web-based and application programming interface (API) basedprocess analysis software tools to estimate the energyconsumption of a CNC machine tool operation and to eval-uate its environmental impact as a rst step towards asustainable manufacturing analysis. Pfefferkorn et al. (2009)examined the ow of energy in thermally assistedmachining (TAM) in an attempt to determine the benets ofpreheating and some efciency metrics are suggested andused to study the data that have been collected to date. Liuet al. (2012) proposed a method to achieve the on-siteprocessing energy efciency. The method will provide sup-port for optimizing the energy efciency of machiningprocesses.

The above-mentioned studies are signicant, but the studiesprimarily refer to the actual machining process, and still lack of amethod which can be used to predict the energy consumption andenergy efciency of machining process before the actual machiningprocess. The method is signicant, because it is capable ofproviding support to promote energy efciency, to set the work-piece energy consumption quota, and to optimize the workpiecedesign and process planning for reducing energy consumption.These signicant contributions of the method are based on ourresearch on the energy consumption prediction method ofmachining processes in recent years.

Some related studies regarding the prediction of energy con-sumption of the machining process are described below.

Dietmair and Verl (2009) presented a modeling framework fortool machine energy consumption forecasting. A number of ex-amples were presented on the application of the model for energyefciency optimization.

Kara and Li (2011) presented an empirical approach to developunit process energy consumption models for material removalprocesses. The methodology was tested and validated on eightdifferent CNC turning and milling machines. The presented modelpredicted the energy consumption of machining processes with anaccuracy of over 90%.

Diaz et al. (2011) indicated that the machining time dominatesthe energy demand for high tare machine tools and provided amethod for characterizing the specic energy of a machine tool as afunction of the process rate. Themodel allows a product designer toestimate the manufacturing energy consumption of the productionparts without measuring the power demand directly at the ma-chine tool during operation.

Diaz et al. (2012) reviewed the accuracy of a specic energycharacterization model to predict the electrical energy consumedby a 3-axis milling machine tool during processing. The energycharacterization model exhibited good accuracy for the part man-ufactured under varied material removal rate conditions andhighlighted the potential for energy reduction using higher cutting

F. Liu et al. / Journal of Clea2speeds.

Please cite this article in press as: Liu, F., et al., Amethod for predicting themachining process, Journal of Cleaner Production (2014), http://dx.doi.orConsider the example of the machining process of the work-piece shown in Fig. 1 to analyze the characteristics of the energyconsumption of the periods in the machining processes of aworkpiece.

The machining process of the workpiece includes the initialcylindrical surface turning at low speed, followed by the head faceturning at high speed, and nally cutting off the workpiece at lowspeed.

The power schematic diagram of the entiremachining process isshown in Fig. 2, which reveals that the process of energy con-sumption consists of three classes of periods.

start-up period (1) idle periods (2) (4) (6) (7) (9) (10) (12) cutting periods (3) (5) (8) (11)

There are different energy consumption characteristics in thethree classes of periods. The power process of start-up periodschanges sharply and the law of energy consumption is complicatedstart-up periods, idle periods and cutting periods is not fullyconsidered, which makes it difcult to predict the energy con-sumption of the entire machining process of a practical workpiece.Second, the present methods of modeling and simulation based onthe historical production information and database of energyconsumption face difculties in predicting the energy consumptionof new workpieces. Third, the additional load loss in the machiningprocess is very complicated and cannot be neglected, whichsometimes is more than 20% of the cutting energy, so the predictionof the additional load loss is a question.

The method proposed in this paper may solve all of the aboveproblems.

The main driving system (MDS) of a machine tool consists of thespindle motor and themechanical transmission system. The energyconsumption of the MDS is the principal part of the energy con-sumption of a machine tool, and the energy consumption law of theMDS is the most complicated of all energy consumption. Therefore,this paper mainly focuses on the energy consumption prediction ofthe MDS.

2. The classication of the periods of the energy consumptionin the machining processes of a workpieceFig. 1. The blank drawing of the workpiece.

energy consumption of themain driving system of amachine tool in ag/10.1016/j.jclepro.2014.09.058

classes of periods in the machining process.

ner(1) In the start-up periods, the machine tool does not cut theworkpiece, so the value of the cutting power Pc is zero; whenthe rotation speed of the machine tools rise from 0 to thetarget speed in a very short time, themagnetic eld energy ofthe motor and the kinetic energy of the moving parts changein this period. The power of the idle periods is nearly constant. Thecutting periods can be divided into constant load cutting, shown asprocesses (3) and (5), and variable load cutting, shown as processes(8) and (11). The power consumption of both forms of cutting isgreater than that in the idle periods.

3. Energy consumption model of the main driving systemduring machining processes

The authors have established a transient energy consumptionmodel in previous studies (Liu and Liu, 2012):

Pit Plet Plmt Pct dEm=dt dEk=dt (1)

Pi is the input power of the motor. Ple is the power loss of the motor.Plm is the friction loss power of mechanical transmission system. Pcis the cutting power. Em is the magnetic eld energy of themotor. Ekis the sum of the kinetic energy of the mechanical transmissionsystem and the motor rotor.

The model (1) has different forms corresponding to the three

Fig. 2. The power schematic diagram of the machining process of the workpiece.

F. Liu et al. / Journal of Cleasharply, and the energy consumption also changes sharply.Therefore, the energy consumption model of start-up pe-riods is as follows:

Pit Plet Plmt dEm=dt dEk=dt (2)

(2) In the idle periods, the value of the cutting power Pc is alsozero and the rotation speed of the machine tools is constant,so the magnetic eld energy of the motor and the kineticenergy of the moving parts are nearly constant, i.e., the valueof dEm/dt and dEk/dt are both zero. Therefore, the energyconsumption model of the idle periods is as follows:

Pun Puen Pumn (3)

Pu is the idle power or unproductive power of the MDS at a specicrotation speed. Pue is the power loss of the motor, and Pum is the

Please cite this article in press as: Liu, F., et al., Amethod for predicting themachining process, Journal of Cleaner Production (2014), http://dx.doi.orconsumption in theMDS of amachining process can be obtained bythe sum of the energy consumption of all periods. Therefore, themodel framework for predicting the energy consumption of amachining process is as follows.

E XQs

j1Esj XQu

j1Euj XQc

j1Ecj (6)

In model framework (6), E denotes the total energy consump-tion in the MDS of a machining process. Qs, Qu and Qc denoteamount of start-up periods, idle periods and cutting periods,respectively. The subscript s, u and c denote start-up, unload (idle)and cutting, respectively.

Next, the energy consumption prediction models and themethods of three classes of periods are analyzed as follows.

4.1. Energy consumption prediction of the start-up periods

According to model (2), if the rotation speed of the start-upperiods is n and the time of the start-up periods is t, then the en-ergy consumption in this stage Es is:

Es Zt

0

Pitdt Zt

0

Plet Plmt dEm=dt dEk=dtdt (7)

The energy consumption law of the start-up periods is compli-cated; in particular, dEm/dt and dEk/dt in formula (2) are difcult todetermine. However, when speed is certain, the start-up energyshould be a constant, which means there is a functional relation-ship between energy consumption and spindle speed, i.e., we canPit Plet Plmt Pct (4)In the cutting periods, the power loss Ple of the motor can be

divided into the power loss Pue in the idle periods and the loadingloss Pae in the cutting periods; the power loss Plm of the mechanicaltransmission system can be divided into the power loss Pum in theidle periods and the loading loss Pam in the cutting periods.Therefore, equation (4) can be changed into equation (5).

PiPuePaePumPamPcPuePumPaePamPcPiPuPaPc

(5)

where Pa is the total loading loss in MDS.

4. Energy consumption prediction models of a machiningprocess

According to the analysis described above, the total energypower loss of the mechanical transmission systemwhen theMDS isidle.

(3) In the cutting periods, Pc is not equal to zero. As the me-chanical transmission system of the machine tool, especiallythe spindle, is of high inertia, the angular speed of the motorand the rotation speed of the transmission system changeslowly during the cutting periods. As a result, the magneticeld energy of the motor and the kinetic energy of themoving parts change slowly, and the value of dEm/dt and dEk/dt are approximately equal to zero. Therefore, the energyconsumption model of the cutting periods is as follows:

Production xxx (2014) 1e7 3predict the energy consumption of start-up periods by establishing

energy consumption of themain driving system of amachine tool in ag/10.1016/j.jclepro.2014.09.058

the energy consumption database or function library beforehand.The detailed method rst involves, measuring the energy con-sumption at several selected speeds, followed by constructing theenergy consumption function with speed as variable, and nallyobtaining the prediction model similar to formula (8), which can beused to predict the energy consumption of any start-up period ofany machining process.

Es x1n2 x2n x3 (8)

4.2. Energy consumption prediction of the idle periods

According to Fig. 2 and formula (3), we know that idle powerremains steady at a certain rotation speed. Thus, the energy con-sumption of idle periods can be calculated by multiplying the idlepower by the idle time, that is.

Eu Pu tu (9)Theoretically, the idle power of a certain rotation speed is a

constant, which means an idle energy consumption database orfunction library could be constructed beforehand. By havingdone so, the energy consumption of the idle power at any speed

4.3. Energy consumption prediction of the cutting periods

Pa a1Pc a2P2c (11)Therefore formula (5) can be rewritten as formula (12).

Pi Pu Pc a2P2c a1Pc a2P2c 1 a1Pc Pu (12)Referring to formula (12), the energy consumption of the cutting

periods can be calculated by the integral of power over time, whichis:

0

a2Ztc

0

P2c dt 1 a1Ztc

0

Pcdt tc Pu (13)

4.3.2. Methods of obtaining the basic parameters a1, a2 and thecutting power Pc

Because Pu in formula (13) was obtained by the above method,the key to energy consumption prediction of the cutting periods isto obtain the parameters a1, a2 and the cutting power Pc.

2ck

ijP2c

F. Liu et al. / Journal of Cleaner Production xxx (2014) 1e748>>>>>>>>>>>>>>>:

a2 P

PijPcjX

P3cj X

PujPcjX

P3cj X

PujP2cj

XP2cj

XPX

P3cj2 X P2cj

XP4cj

1 a1 P

PujP2cj

XP3cj

XPijPcj

XP3cj

XPujPcj

XP4cj

XP 2 X 4 X 324.3.1. Energy consumption prediction model of the cutting periodsAccording to formula (5), Pa is the load loss of the MDS that is

caused by cutting power Pc. Many experiment results indicate thatPa can be tted by a quadratic function of the cutting power Pc. As aresult, the load loss Pa can be expressed as formula (11).

8>>>>>>>>>>>>>:

4 Pi1 Pu1 a2P2c1 1 a1Pc1

2 Pik Puk a2Pv4

va2 0

v4

v1 a1 0can be predicted. The method of constructing the databaseand function library for the idle periods is similar to that of thestart-up periods. The function of idle power is in the form offormula (10).

Pu gn (10)Pcj Pcj Pcj

Please cite this article in press as: Liu, F., et al., Amethod for predicting themachining process, Journal of Cleaner Production (2014), http://dx.doi.or(1) Methods of obtaining the basic parameters a1 and a2

Parameters a1 and a2 reect the characteristics of the MDS, andeach transmission chain of machine tools corresponds to differentparameters a1 and a2.

For each transmission chain, taking k(k 2) groups of differentcutting parameters and measuring the corresponding Pi, Pu and Pc,the equation set can be obtained as follows.

8