数控机床专业英语翻译题

Journal of Cleaner Production

Volume 137, 20 November 2016, Pages 361–369

Analytical approach to establishment of predictive

models of power consumption of machine tools' auxiliary units

?Machine tool;

?Energy model;

?Energy consumption;

?Ecodesign

The issue of production machine energy consumption has been recently gaining prominence, particularly due to the efforts made by the developed countries to reduce the impact of human activity on the environment. Since the operation of production machines is very energy-demanding, it is during their operation that production machines contribute to damaging the environment the most, as shown by previous studies (CECIMO, 2009). Rising energy prices together with efforts to reduce manufacturing costs have resulted in machine tool users request for minimizing energy demands of manufacturing. This pressure on production machine producers is further increased by the EU directive on reducing energy demands in all areas of human activity, in particular in industrial production, where production machines are significant energy consumers (European Union, 2009). In order to meet the objective of reducing production machine energy demands, it is necessary to consider potential energy savings already during the design stage of these machines or when planning production on these machines. Simulation of energy consumption during the design phase of the machine or technology can be an advantage giving an overview on costs of planned production which is nowadays one of the current issues. This cannot be achieved without the application of predictive models of energy consumption. A large part of studies and models that have been carried out so far focuses in particular on predicting the consumption of drives. However, the contribution of auxiliary units to total energy consumption is significant and often higher (Holkup et al., 2013). Therefore, it is necessary to deal with them in further development of energy consumption predictive models systematically and to give them the attention they deserve.

1.1. State of the art

Draganescu et al (Draganescu et al., 2003). studied the influence of cutting conditions on machine tool efficiency and power consumption. They searched for a mutual relationship between these two parameters based on practical tests. Weinert et al (Weinert et al., 2004). focused on the possibilities of reducing the amount of cutting fluid used during machining, which is one of the methods of reducing manufacturing costs. Although they did not examine the effect on energy consumption directly, they are often mentioned since their research made it possible to increase cutting speeds. This allowed reduction in manufacturing time, an essential parameter affecting machine tool consumption. Rangarajan and Dornfeld (Rangarajan and Dornfeld, 2004) were also aware of the significant role operating times play in reducing energy consumption. They focused on the optimization of cutting tool paths during

machining. They also investigated the influence of workpiece clamping orientation on total time of machining planar surfaces. Gutowski et al (Gutowski et al., 2006). were the first to apply an exergic approach to energy consumption of manufacturing processes (exergy measures the potential of materials to do work). Based on this approach, Gutowski created a simple model of machine tool power consumption (1). This model is based on the simplistic assumption that the consumption of auxiliary units is independent of the machining process. Using tests, he also discovered that the consumption of these units may approximately constitute up to 85% of total machine tool energy consumption.

equation(1)

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where E [Ws] is the total energy consumed by the machine tool, P0 [W] is the idle power, k[Wsm?3] is the specific cutting process energy, [m3s] is the material removal rate and t[s] is total machining time.

This research was followed by Diaz et al. (Diaz et al., 2011), who focused on identification of relationships between cutting conditions represented by material removal rate, active power requirement and total energy consumption. Kara and Li (Kara and Li, 2011) brought new insights into energy consumption of production machines. They considered the machine as a holistic system, which is able to influence its subsections. Therefore, it is necessary to deal with the relationships between these subsections as it is no longer possible to strictly divide energy consumption between the cutting process and auxiliary units as has been the practice so far. Mori et al (Mori et al., 2011). focused on the possibilities of energy savings using enhanced acceleration and deceleration control with added synchronisation of the spindle with feed axes. Their improved model included power demand for the spindle to accelerate or decelerate. Mativenga and Rajemi (Mativenga and Rajemi, 2011) focused on the selection of optimum cutting conditions with respect to cutting tool lifetime. This initiated a discussion on power consumption during tool exchange. Li and Yan (Li et al., 2013) dealt with modelling machine tool energy consumption and established a refined empirical model of machine tool active power, which achieves significantly more accurate results in comparison with predictive models of their predecessors. In their further research, they looked at multicriterial optimization of cutting conditions as a search for a compromise between material removal rate, power consumption and surface quality (Yan and Li, 2013). Avram and Xirouchakis (Avram and Xirouchakis, 2011) focused

on predictive models of energy consumption using NC code analysis. A similar sophisticated model (2) was also developed by He et al. (He et al., 2012). equation(2)

E

t o t a l =E

s p i n d l e

+E

f e e d

+E

t o o l

+E

c o o l

+E

f i x

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where E total [Ws] is the total direct requirements, E spindle [Ws] is spindle energy requirements for the main cutting motion, E feed [Ws] is feed axes requirements for secondary cutting motions, E tool [Ws] is tool exchange energy requirements, E cool [Ws] is energy of cutting process cooling and E fix [Ws] is machine energy requirements.

The research of the above-mentioned authors was further continued by Balogun and Mativenga (Balogun and Mativenga, 2013) and Dietmair and Verl (Dietmair and Verl, 2009), who developed own advanced models of energy consumption. These models use a division of the entire working cycle according to machine regimes. Witt et al (Witt et al., 2014). developed simulation software for real-time energy consumption and manufacturing cost predictions. This software is capable of providing valuable information already in the production planning phase. It uses data from a real control system (hardware in the loop) for the prediction of energy consumption of drives. As many other authors, they are confronted with the issue of determining the consumption of a substantial part of auxiliary units, which significantly contribute to the total consumption of a machine tool.

The analysis of existing machine tool energy models leads to conclusion that consumption of auxiliary units can be higher than consumption of drives. Unfortunately not so many researchers have been interested in the precise modelling of energy consumption of machine tools auxiliary units yet. Therefore this part of the simulation should be investigated in more details.

1.2. Research aim and scope

This paper proposes an analytical approach to the establishment of predictive models of power consumption of machine tools' auxiliary units. An estimation of power consumption of auxiliary units acquired by the model described below together with the consumption of drives. Drives can be predicted using the already published models and it will provide machine tool users with insights into total energy demands during production. The main objective of using this analytical approach is an increasing of the conformity between the consumption predicted by the model and the actual consumption of a machine tool.

2. Method of modelling

In this chapter a creation process of energy models of machine tools especially of their auxiliary units will be described.

2.1. Model establishment

The evaluation of the proposed model (see Fig. 1) can be described in the following three steps:

Step 1. Analysis of all installed machine auxiliary units and their

behaviour.

Step 2. Establishment of submodels of analyzed auxiliary units.

Step 3. Sum of energy flows of all auxiliary units, including the

consumption of compressed air and drives.

Fig. 1.

Model of machine tool energy consumption.

Figure options

2.2. Core of model

The core of the established model may be mathematically described by three basic equations. They express the relationship between the active power of the device and its activity (3), the above-mentioned summation of energy flows (4) and subsequent calculation of the energy consumed (5).

equation(3)

P

i (t)=A(t)·P

i n p u t

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where P i?(t) [W] is the time characteristic of the active power of a given auxiliary unit, A(t) [?] is the time characteristic of activity of a given auxiliary unit and P input [W] is the required active power of a given auxiliary unit in normal operation.

equation(4)

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where P total?(t) [W] is the time characteristic of total active power of the machine, P drive?(t) [W] is the time characteristic of active power of machine

drives, P air?(t) [W] is the time characteristic of equivalent active power of auxiliary units powered by compressed air (see Eq. (6)) and P i?(t) [W] is the time characteristic of active power of individual auxiliary units included in the model.

equation(5)

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where E total [Ws] is total consumed energy of the machine, P total?(t) [W] is the time characteristic of total active power of the machine, T [s] is total

simulation time, is the vector of machine active powers and is the

vector of simulation time increments.

The complexity of the model is dependent on the number of auxiliary units included in the model. It is also dependent on their selected main properties and their energy behaviour.

2.3. Auxiliary units categorization

The auxiliary units is possible classify according to the following basic criteria. Criterion 1 – mode of operation

The mode of operation is mainly influenced by time parameters that are entered into appropriate submodels. Based on this criterion, the following types of auxiliary units are distinguished (see Fig. 2).

Fig. 2.

Classification of auxiliary units based on mode of operation.

Criterion 2 – autonomy of auxiliary units behaviour

The autonomy of auxiliary units control is an important factor, which is an expression of the relationship between the control system of the machine and the controlled auxiliary unit. This key property of auxiliary units influences the possibility of predicting their activity based on knowledge of machine control system commands. Based on the autonomy of control, auxiliary units can be divided into three groups (see Fig. 3).

Fig. 3.

Classification of auxiliary units based on the autonomy of their control.

Figure options Non-autonomous auxiliary units are controlled directly by the machine control system and have no other regulation that would influence their operation and active power. Semi-autonomous auxiliary units are operated by the machine control system and they have their own autonomous regulation, which influences their activity. Fully-autonomous auxiliary units are not controlled by the machine control system and are completely independent on it.

Criterion 3 – type of performance control

The type of performance control of auxiliary units is the next criterion directly influences the complexity of submodels and thus in particular input performance parameters. The following types of auxiliary units control are significant (see Fig. 4). Auxiliary units with one-level control only operate in ON/OFF regime, whereas auxiliary units with discrete multi-level control operate on several discrete performance levels. Auxiliary units with continuous control can work in all performance level of the entire range.

Fig. 4.

Classification of auxiliary units based on the type of performance control.

Figure options

Criterion 4 – type of operation initialization

The mode of operation initialization of auxiliary units significantly influences the ability to predict their activity in advance. Based on this criterion, the following basic types of auxiliary units initialization can be distinguished:

AUTO The characteristic of activity of auxiliary units with automatic operation initialization is determined by the machine producer and their behaviour cannot be influenced by the machine user (for example electric cabinet air conditioning). The activity of auxiliary units can be predicted only based on a thorough knowledge of the PLC programme.

SEMI Auxiliary units with semi-automatic operation initialization are initialized by the NC programme (for example tool edge cooling). Their activity can be predicted based on the NC programme analysis.

MAN Auxiliary units with manual operation initialization are initialized manually by a machine operator using the control panel. The characteristic of their activity is very difficult to predict or they are completely unpredictable.

2.4. Acquiring submodel input parameters

Classification of auxiliary units based on the above-mentioned criteria has a significant impact on the complexity of submodels and their performance parameters. These parameters may be determined in two ways, i.e. directly without measuring on the machine and indirectly by measuring on the machine.

Time parameters and some performance parameters that may be determined directly without measuring on the machine. Time parameters may be acquired for example by a time analysis of the NC programme. Some performance parameters may be obtained in the same manner although the model established in this manner may provide very rough results (the upper limit of consumption). It is primarily performance parameters of individual auxiliary units that are determined indirectly by measuring on the machine. Using parameters obtained by measuring on a specific machine tool, relatively accurate results of energy consumption calculation are obtained.

2.5. Submodels of selected auxiliary units

A typical representative of auxiliary units that are difficult to simulate are air conditioning of an electrical cabinet of a machine tool. These auxiliary units usually belong to automatically operated devices with own autonomous regulation. Performed measurements (see Fig. 5) showed that at the beginning of the machine operation the period between the activation of the air conditioning became gradually shorter due to rising temperature of elements in the electrical cabinet. Later, the number of activations became stable, which is caused by stabilization of temperature in the electrical cabinet. It can be assumed that unless there is a dramatic change in machine tool load or ambient conditions, the air-conditioning unit will continue to be activated in this periodic manner.

Fig. 5.

Time characteristic of active power of electrical cabinet cooling unit.

Figure options These auxiliary units are usually replaced by an average value in models, so called “simple-submodel”. In case of long-term simulation the difference between model and reality can be neglected. For middle-term simulation it is more appropriate to select an enhanced submodel (combined-submodel of a permanently working and periodically started device). This enhanced

submodel provides a higher degree of compliance with more negligible error in comparison with a simple-submodel.

The combined-submodels may be also used for example in modelling machine tool fluid systems. This is evident from the active power measuring of auxiliary units on the three axis milling machine (see Fig. 6). The fluid systems for coolant comprise of a complex set of pumps. The main pump works continuously from its initialization by an appropriate M function in the NC programme, whereas the transfer pump is initialized discontinuously depending on the drop of fluid level in the tank.

Fig. 6.

Time characteristic of active power of selected machine tool auxiliary units.

Figure options Another type of submodel is a division of the total operation of a selected auxiliary unit into two and more sections (run-up and operation). This type of submodel is for instance applicable to the unit responsible for sucking vapour from the workspace as shown by the same measurement performed on the three axis milling machine (see Fig. 6). Measurements revealed that active power during the run-up of this unit is approximately double the normal operation active power and the run-up itself takes approximately from 3 to 5 s. Apart from electric appliances, it is also necessary to consider the electric equivalent of air consumption according the equation (6) (Holkup et al., 2013). equation(6)

P

a i r (t)=c·Q

a i r

(t)

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where P air?(t) [W] is the time characteristic of equivalent active power of auxiliary units powered by compressed air, c [Wdm?3min] is the conversion ratio between compressor active power and flow of air into the machine for a given compressed air distribution system and Q air(t) [dm3min?1] is the time

characteristic of flow of compressed air into given auxiliary units in the machine.

Using this calculation of compressed air equivalent active power (6) is possible to improve any standard models they do not account with this like (1) and (2).

As shown by the measurement results (see Fig. 7), compressed air consumption in this specific case is a typical area where it is possible to replace the measured characteristic by the average value for the calculation of consumption. This is due to the fact that in 80% of the monitored characteristic the value of average and real compressed air flow differs approximately only by 10%. However, it depends on the specific type of machine and the number of installed auxiliary units powered by air as well as on the quality of elements used and compressed air distribution with respect to leakage. Last but not least, the machine working regime is important as well.

Fig. 7.

Time characteristic of compressed air flow into the machine.

Figure options

3. Experiment proposal

An experiment was proposed in order to compare the accuracy of the model of machine tool auxiliary units energy consumption and measurements on a real machine. The experiment was performed on the three axis horizontal milling machine (see Fig. 8 and Table 1).

Table options

?

external cooling of cutting tool (M8).

?

flushing chips from workspace (M20, M22).

?

lubricating unit.

Fig. 10.

Time characteristic of total active power of machine tool auxiliary units (uncalibrated

model).

Figure options

Fig. 11.

Time characteristic of total energy consumed by machine tool auxiliary units

(uncalibrated model).

Figure options Therefore, it is necessary to perform a simple calibration of real energy consumption of all auxiliary units used for the model. Each unit is measured separately and real consumption is monitored. This simple calibration resulted in greater accuracy of input performance parameters of monitored auxiliary units submodels. These calibrated data are than used to make a verification measurement with the proposed model.

4.2. Model verification

This calibration led to greater accuracy of the calculation of the total energy consumed as shown in Fig. 12 and Fig. 13. The error of the model is now around 12?% during the whole simulation period. This difference is caused by unknown energy consumer activity during the last phase of the measuring. In

case of simulation of all known consumers in ti me 0 to 700?s, the accuracy of the model is around 1?%. This difference is discussed in the next chapter.

Fig. 12.

Time characteristic of total active power of machine tool auxiliary units (calibrated

model).

Figure options

Fig. 13.

Time characteristic of total energy consumed by machine tool auxiliary units

(calibrated model).

Figure options

4.3. Discussion

Clear error that is noticeable from the comparison of real and predicted machine tool active power is the activity of an unidentified auxiliary unit in the time section from 700 to 720?s. An error of the model relative to the measurements arose during the activity of this auxiliary unit. The relative deviation of the modelled consumption in comparison to the measured consumption was 1?% until this moment in time. However, it rose to final 12?% due to the activity of the unidentified auxiliary unit. This situation shows the necessity to know all energy consumers and account with them in the model

even if it looks like negligible from the point of view actual power input. The overall energy consumption calculation can be significantly affected.

Another small error is evident from the time characteristic of total active power of the monitored group of auxiliary units. The model contains errors in the time offset of initialization of given auxiliary units. In this case, these errors are caused by the method of NC code analysis, which failed to take into account transfer delays in initializing individual auxiliary units. These errors may be suppressed by using real or simulated control systems (e.g. virtual iTNC). However, the impact on total consumed energy in the performed test is minimal (see Fig. 13).

5. Conclusions

The functionality of the simple model was proved. The performed tests showed the great significance of a thorough identification of all auxiliary units contributing to energy consumption. This accurate identification of machine tool auxiliary units is an important factor affecting the resultant accuracy of the model of the entire machine tool. The necessary step is to calibrate the real consumption of tested units and devices because the plates parameters and real situation can be quite different. Without this calibration, the accuracy of the modelling is not good.

The next issue for the modelling is to have a proper time line for the activation of tested devices. Therefore, the next step would be using the virtual control system with the real PLC setting to cover drives performance such as real positioning and speed control loops. Due to this model improvement, a better time line of the machining and auxiliary unit initialization can be achieved to make a more accurate simulation of energy consumption.

Acknowledgement

The paper has received funding from the Technology Agency of the Czech Republic