TMCnet News

Computer Monitoring Approach and System on Machine Tools Energy Efficiency [Sensors & Transducers (Canada)]
[April 22, 2014]

Computer Monitoring Approach and System on Machine Tools Energy Efficiency [Sensors & Transducers (Canada)]


(Sensors & Transducers (Canada) Via Acquire Media NewsEdge) Abstract: Energy efficiency improvement and energy consumption reduction in computer numerical control (CNC) machine tools is very important to realize sustainable manufacturing. However, the energy efficiency in practical machining is very low. In the paper the computer monitoring approach and system on machine tools energy efficiency is developed in detail. Firstly, the machine tools energy consumption characteristics are researched according to various components and manufacturing stages; Secondly, the machine tools control structure and cutting properties are studied; Thirdly, the machine tools energy efficiency computing approach is put forward; Finally, the energy efficiency computer monitoring system with Fluke Norma sensor and Fluke shunt sensors is built up, and the monitoring experiment on vertical machining center is carried out. The developed computer monitoring approach and system on machine tools energy efficiency is helpful to evaluate and improve machine tools energy efficiency further. Copyright © 2013 IFSA.



Keywords: Energy efficiency, Computer monitoring system, Computing approach, Vertical machining center, Fluke shunt sensor.

(ProQuest: ... denotes formulae omitted.) 1. Introduction Generally, machine tools manufacturing convert raw materials and electric energy into products, and bring about environmental wastes at the same time [1, 2]. It is significant to reduce energy resources used in manufacturing in order to decrease CO2 emissions produced from energy generation [3, 4].


The American sectors shares data of total energy consumption in 2011 showed that among the whole energy consumption [5], the industrial sector account for about 31 %, the transportation sector occupy about 28 %, the commercial sector occupy about 19 %, and the residential sector occupy about 22 %. In the whole industrial energy consumption, the manufacturing sector account for about 90 %, while the mining and construction sector occupy only 10 %. In the manufacturing, the machining sector occupies about 83 %, while the casting, forging, welding and other sector occupy only 17 %. In sum, it is important to reduce the energy consumption in machining.

However, the energy efficiency in machining is very low [6, 7]. Gutowski et al. pointed out that the non-cutting operations in modem milling machine tool usually consume most of the energy resources, and the energy consumed in the actual cutting occupy about only 15 % of the total energy [8].

The predictions of energy consumption can help engineers and operators to select the most effective process parameters to improve energy efficiency [9,10].

Gutowski brought forward an energy consumption empirical model in cutting [8]: ...(1) The variables meaning are as follows: P is the total power in the cutting process; Po is the idle power; k is the constant in kJ/cm3.

MRR: The rate of material processing with unit cmVsec.

On the basis of Equation (1), Li et al. put forward an empirical model on turning machines on the condition of dry cutting [11]: ...(2) ...(3) The variables meaning are as follows: E is the total energy consumption in machining process; SEC is the specific energy consumption; Q is the removed materials volume; Co and Ci are the constants; MRR is the material removal rate in cmVsec.

Consequently, energy consumption in turning could be predicted with given process parameters by Equations (2) and (3).

Bi et al. brought forward a method to compute machine tools energy consumption based on machine tools kinematic and dynamic models [12]: ...(4) The variables meaning are as follows: E is the total machine tools energy consumption under a given external load; n\ and nT are the actuated linear and rotary joints numbers; /i(t) is the linear motor driving force; 7i(t) is the rotary motor driving torque; Vi(t) is the actuated linear motion linear velocity; Wi(t) is the rotary motor angular velocity.

In the verification testing [13], the computing method is used to optimize the machine tools setup for energy saving, and can lead to 67 % energy saving for the specific drilling machining.

Oda et al. investigated the energy saving on a five-axis CNC machining center, and indicated that in contour machining with ball end milling, the inclined angle 15° was optimal to obtain the lowest power consumption [14].

The above research results mainly concentrated on the energy consumption prediction and reduction approaches in machine tools machining [15-17]. In the paper the computer monitoring approach and system on machine tools energy efficiency is developed. Firstly, the machine tools energy consumption characteristics are researched according to various components and manufacturing stages; Secondly, the machine tools control structure and cutting properties are studied; Thirdly, the machine tools energy efficiency computing approach is put forward; Finally, the energy efficiency computer monitoring system on vertical machining center was set up, and the testing experiment was carried out. The developed approach is much significant to evaluate energy efficiency of CNC machine tools, and to improve the machine tools energy efficiency further.

2. Machine tools Energy Consumption Characteristics The machine tools energy consumption is complex, and the machine tools energy consumption characteristics are researched according to machine tools components, and according to various machining stages in the paper.

2.1. Energy Consumption Classification According to Various Components Machine tools include various components [18]. Each component performs a specific action, and the whole machine tools will achieve more complex and comprehensive functions [19]. As shown in Table 1, the machine tools electrical components could be classified into spindle drives, servo drives, hydraulic system, cooling and lubrication system, control system, auxiliary system, and periphery system [20].

Consequently, the machine tools energy consumption can be divided into spindle motor energy consumption, servo motors energy consumption, hydraulic equipment energy consumption, cooling and lubrication devices energy consumption, CNC system energy consumption, auxiliary devices energy consumption, and periphery equipments energy consumption. In sum, the energy consumption reduction of each machine tool component is helpful to save the whole machine tools energy requirement.

2.2. Energy Consumption Classification According to Various Machining Stages The Cooperative Effort in Process Emission (CO2PE!) divided machine tools machining into two parts, including the Basic State and Cutting State [21]. In the Basic State, the energy is consumed to activate the required machine components such as main spindle motor, axis motors and control panel. In the Cutting State, the energy is consumed to remove workpiece material to form the part surface.

Between the Basic State and Cutting State, Balogun brought forward a transitional state defined as Ready State [22]. The Ready State is after machine tool start but before cutting, which includes change tool, brings the worktable to servo Home Location, and setup the machine tool process parameters. The machine tool energy consumption is divided into ¿Basic, ¿Ready and ¿cutting according to the three machining stages shown in Fig. 1.

3. Machine Tools Control Structure and Cutting Properties The CNC lathe and milling machines are the two most common machine tools in factory production. The cutting force is all-important to cutting mechanism, machine tool energy consumption, and reasonable cutting parameters. The CNC machine tools control structure and cutting properties are analyzed in detail.

3.1. The Steady-state Behavior Analysis of CNC PID Position Controller Three-loop cascade control scheme is adopted on modem CNC machines mostly, which consist of position loop, velocity loop and current loop from outside to inside. The position loop is main control loop, while the velocity loop and current loop are auxiliary control loops. The position loop plays an important role on dynamic tracking performance and output static accuracy. The output of position loop is the input of auxiliary control loop, so it should be provided with better control property.

The screw pair drives are adopted in CNC feeding system usually. Neglecting the influence of mechanically-driven nonlinear factors such as screw pairs, the approximate structure model of closed loop feeding servosystem is shown in Fig. 2. The transfer function of the PID position controller is as follows: ...(5) where Kp is the proportional control coefficient, Kt is the integral control coefficient and Kd is the differential control coefficient. The D/A portion performs proportional control function and the coefficient is KA . The speed control unit is regard as an inertia tache approximately whose transfer function is ,... where T is the inertia time constant and K~ is the magnification times of speed regulation unit. The position measurement portion performs proportional control function and the coefficient is K~.

Let the position instruction signal of feeding servosystem be acceleration signal, i.e. r{t) = A / 2 * t2, where A is constant and t expresses time. The image function of r(t) after Laplace transform is as follows: R{s) = Als2 * The tracking error transfer function of feeding servosystem shown in Fig. 2 can be concluded: ...(6) Then the steady-state error can be obtained: ...(7) As shown in Equation (7), the integral control coefficient Ki inversely relates with steady-state error ess . The steady-state error will reduce when K, increases. However, the oversize Ki will lead to unacceptable overshoot or integral saturation when system deviation error is bigger, which is unallowed in high accuracy CNC machining. So it is very limited of steady-state error reduction through Ki regulation. The conclusion is that it is difficult to obtain satisfied steady-state error and obtain high accuracy machining when adopting PE) position controller only.

3.2. The Feedforward Control in CNC Position Controller In order to obtain satisfied steady-state error and perfect servocontrol precision, the conventional PID position controller structure has to be transformed. Append feedforward control to the feeding servosystem structure.

Let the transfer function of PID position controller be Gj(<S) , the transfer function of feedforward control portion be F(S), and the total transfer function including D/A portion, speed control unit and succèdent integrating element is as follows: ...

The closed loop transfer function of feeding servosystem without feedforward control portion is as follows: ...(8) The closed loop transfer function of feeding servosystem with feedforward control portion can be concluded: ... (9) The Equation (8) and (9) have uniform closed loop characteristic equation. So they have the same pole and same system stability. The conclusion is that the system stability doesn't change when appending feedforward control portion to the primary servosystem.

Let us analyze the tracking error change when appending feedforward control portion to the primary servosystem. The tracking error transfer function of feeding servosystem structure can be computed: ... (10) Let the numerator of Equation (10) be 0, it can be concluded that ...(11) Therefore, the servosystem tracking error will be 0 and the ideal machining precision will be achieved theoretically if feedforward control portion meets with Equation (11). Expand the Equation (11): ...(12) As shown in Equation (12), the feedforward control portion introduces the first derivative and second derivative of position direction input signal, where the first portion expresses acceleration feedforward control and the second portion expresses speed feedforward control.

The practical CNC feeding servosystem is highorder system, has nonlinear tache such as friction whose mathematic model is difficult to be computed accurately. So the feedforward control portion can't reduce the tracking error to zero actually. However, if the speed feedforward coefficient and acceleration feedforward coefficient can be computed exactly through some experimentation according to Equation (12), the feedforward control structure can remarkably reduce steady-state tracking error, enhance servocontrol precision and not change the primary servosystem stability.

3.3. The Lathe Machine Cutting Force and Energy Consumption As shown in Fig. 3, the cutting force comes from three aspects in turning operation: 1) The resistance force of the material to be processed to overcome elastic deformation; 2) The resistance force of the material to be processed to overcome plastic deformation; 3) The friction force among the rake face, flank, transition surface and the machined surface.

The cutting force can be divided into three orthogonal force, such as Fc, Ff and Fp. Fc is tangential force, which is necessary to the tool strength calculation and the determination; Ff is the feed machine power force, which is necessary to the feed mechanism design and the tool feed power calculation; Fp is the resistance of cut depth, which cause the workpiece deflection and machining accuracy damage.

The power consumed in the cutting process is defined as the cutting power Pm. The cutting power Pm is the consumption sum of Fc and Ff shown in Equation (13). Fp doesn't consume power because without displacement in Fp direction.

...(13) The variables meaning are as follows: Fc is the tangential cutting force in N; v is the cutting speed in m/min; Ff is the feed force in N; nw is the workpiece rotational speed in r/s; /is the feed amount in mm/r.

3.4. The Milling Machine Cutting Parameter and Cutting Force The various milling cutters are used to machine complex surface in workpiece shown in Fig. 4. Usually the cutter rotation is the main movement of machine tools, while the workpiece moving is machine tool feed motion. Milling machining can process plane, groove, surface, and gear.

As shown in Fig. 5, four process parameters are concerned in the milling machining, including the depth of cut, lateral depth, feed amount, and the milling speed.

1) Depth of cut ap: it is the cutting layer dimension parallel to the cutter axis; 2) Lateral depth ae: it is the cutting layer size perpendicular to the cutter axis; 3) Amount of feed / it is the relative displacement of the workpiece and milling cutter in mm/r when milling cutter take a turn; 4) Milling speed vc: it is the line velocity of milling cutter on the outer edge.

In milling machining, the process precision and efficiency are influenced by cutting force. Consequently, the accurate measurement to milling force is much important. The cutting force has something to do with process parameters, workpiece materials, cutting tool conditions and cutting fluids. In general, there are two schemes to measure milling force. Firstly, the milling force can be measured by force sensor directly; Secondly, the milling force can be obtained through measuring feed servo motors current indirectly.

4. Machine Tools Energy Efficiency Computing Approach in Cutting Hu et al. developed an energy efficiency measurement approach in turning, in which the lathe machine energy efficiency measurement is simplified to spindle energy consumption measurement [23]. The spindle energy consumption is ... (14) The variables meaning are as follows: Pin is the spindle input power; Pu is the spindle no-load power; Pc is the cutting power; Pa is the additional load consumed power.

The energy efficiency monitoring model in turning is ...(15) The variables meaning are as follows: 77 is the energy efficiency in turning; Prfo is the lathe machine constant energy consumption.

The energy efficiency in turning can be computed with Equation (15).

However, in milling not only the spindle motor but also the X, Y, Z axis feed motors consumes electricity energy. For example, the energy consumed by the vertical axis motor can't be ignored, because the vertical axis motor has to bear the gravity of spindle box, and supply the cutting force in the perpendicular direction. In like manner, the energy consumed by the X, Z axis motors when turning conical surface can't be neglected.

Aimed turning and milling machine, the machine tools energy efficiency computing approach is developed in the paper: ...(16) The variables meaning are as follows: 77 is the machine tools energy efficiency in cutting; Pmc is the spindle cutting power; Pxc is the X axis cutting power; Pyc is the Y axis cutting power; Pzc is the Z axis cutting power; Pmin is the spindle input power; Pxin is the X axis input power; Pyin is the Y axis input power; Pzin is the Z axis input power; Prfo is the machine tools constant energy consumption.

5. Energy Efficiency Computer Monitoring System and Experiments in Milling The energy efficiency computer monitoring system is built up in the 3-axis CINCINNATI Arrow 500 CNC vertical machining center. In the machining center shown in Fig. 6, the spindle is driven by the 1PH7 servo motor, while the X, Y, Z axis are driven by the 1FK7 servo motors.

The adopted power analyzer is Fluke Norma 5000 in the computer monitoring system. Because the currents in spindle, X, Y, Z axis motors are very large, and can't send to power analyzer directly, the Fluke shunt sensors are used, which translate the big current signal to small voltage signal and input to the power analyzer shown in Fig. 7.

In order to display and control better, the personal computer (PC) is used which communicates with power analyzer through RS232. In PC, the power data and curve, the active power, the reactive power, and the total power in each input channel can be displayed clearly. The running interface in PC is shown in Fig. 8.

In the vertical machining center, the energy efficiency is monitored when milling groove in the plane. The aluminium alloy workpiece block is 220 mmx20 mmxl5 mm. The used cutting tool is a 20 mm diameter end milling cutter with one PVD (Ti, Al) N/TiN-coated carbide insert.

The process parameters are as follows: - milling speed Fc=65 m/min; - axial depth of cut ap=0.5 mm; - feed rate v=150 mm/min; - radial depth of cut ae=0.5 mm.

Firstly, measure the machine tool constant standby power consumption, the feed axis motors and spindle motor no-load power consumption; Then mill the groove along X axis direction. The spindle motor and X axis motor input power are measured by power analyzer, and the machine tool energy efficiency in milling groove can be computed with Equation (16) shown in Fig. 9. The average energy efficiency in milling the groove is 13.092 %.

6. Conclusions The energy consumption reduction in machining is very important to realize the sustainable manufacturing, protect environment, and improve the economic benefit. In the paper the computer monitoring approach and system on machine tools energy efficiency is developed. The accurate machine tools energy efficiency monitoring and evaluation is helpful to select the optimal process parameters, optimize the machine tools function components, and improve energy efficiency finally. The machine tools energy efficiency computing approach in machining is brought out, and the energy efficiency computer monitoring system in vertical machining center is built up in the paper. At last, the machine tool energy efficiency when milling groove in the plane is monitored. In sum, the developed computer monitoring approach and system on energy efficiency in cutting is much significant to energy consumption evaluation and energy consumption reduction.

Acknowledgements The authors are grateful to the Project of the National Natural Science Foundation of China (No. 51105236).

References [1] . Chris Yuan, Qiang Zhai, David Domfeld, A three dimensional system approach for environmentally sustainable Manufacturing, CIRP Annals Manufacturing Technology, 61, 1,2012, pp. 39-42.

[2] . Joost R. Duflou, John W. Sutherland, Towards energy and resource efficient manufacturing: A processes and systems approach, CIRP Annals - Manufacturing Technology, 61,2,2012, pp. 587-609.

[3] . A. Vijayaraghavan, D. Domfeld, Automated energy monitoring of machine tools, CIRP Annals - Manufacturing Technology, 59, 1,2010, pp. 21-24.

[4] . Rajesh Kumar Bhushan, Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites, Journal of Cleaner Production, 39,2013, pp. 242-254.

[5] . EIA (Energy Information Administration), Annual Energy Review 2011 (http://www.eia.gov/ totalenergy/data/annual/pdf/aer.pdf).

[6] . Vijayender Singh, P. Venkateswara Rao, S. Ghosh, Development of specific grinding energy model, International Journal of Machine Tools & Manufacture, 60,2012, pp. 1-13.

[7] . Jun'ichi Kaneko, Kenichiro Horio, Planning method for fixture conditions of workpiece in continuous multi-axis controlled machining process with consideration of energy consumption about translational axes of machine tool, in Proceedings of the 5th CIRP Conference on High Performance Cutting, May 2012, pp. 126-131.

[8] . Timothy Gutowski, Jeffrey Dahmus, Alex Thiriez, Electrical energy requirements for manufacturing processes, in Proceedings of the 13th CIRP International Conference of Life Cycle Engineering, April 2006.

[9] . M. F. Rajemi, P. T. Mativenga, A. Aramcharoen, Sustainable machining: selection of optimum turning conditions based on minimum energy considerations, Journal of Cleaner Production, 18, 10, 2010, pp. 1059-1065.

[10] . G. Quintana, J. Ciurana, J. Ribatallada, Modelling power consumption in ball-end milling operations, Materials and Manufacturing Processes, 26, 5, 2011, pp. 746-756.

[11] . W. Li, S. Kara, An empirical model for predicting energy consumption of manufacturing processes: a case of turning process, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225, B9, 2011, pp. 1636-1646.

[12] . Z. M. Bi, Lihui Wang, Optimization of machining processes from the perspective of energy consumption: A case study, Journal of Manufacturing Systems, 31,4, 2012, pp. 420-428.

[13] . Z. M. Bi, Lihui Wang, Energy modeling of machine tools for optimization of machine setups, IEEE Transaction on Automation Science and Engineering, 9, 3,2012, pp. 607-613.

[14] . Y. Oda, M. Mori, K. Ogawa, S. Nishida, M. Fujishima, T. Kawamura, Study of optimal cutting condition for energy efficiency improvement in ball end milling with tool-workpiece inclination, CIRP Annals - Manufacturing Technology, 61, 1, 2012, pp. 119-122.

[15] . K. Kellens, E. Yasa, R. Renaldi, W. Dewulf, J.P. Kruth, J. Duflou, Energy and resource efficiency of SLS/SLM processes, in Proceedings of the International Solid Freeform Fabrication Symposium, Texas, May 2011, pp. 1-16.

[16] . S. Rahimifard, Y. Seow, T. Childs, Minimising embodied product energy to support energy efficient manufacturing, CIRP Annals - Manufacturing Technology, 59, 1,2010, pp. 25-28.

[17] . W. R. Morrow, H. Qi, I. Kim, J. Mazumder, S. J. Skerlos, Environmental aspects of laser-based and conventional tool and die manufacturing, Journal of Qeaner Production, 15, 10,2007, pp. 932-943.

[18] . R. Neugebauer, K.-D. Bouzakis, B. Denkena, F. Klocke, A. Sterzing, A. E. Tekkaya, R. Wertheim, Velocity effects in metal forming and machining processes, CIRP AnnalsManufacturing Technology, 60,2,2011, pp. 627-650.

[19] . Yan He, Bo Liu, Xiaodong Zhang, Huai Gao, Xuehui Liu, A modeling method of task-oriented energy consumption for machining manufacturing system, Journal of Cleaner Production, 23, 1, 2012, pp. 167-174.

[20] . Wen Li, André Zein, Sami Kara, Christoph Herrmann, An investigation into fixed energy consumption of machine tools, in Proceedings of the I8,h CIRP International Conference on Life Cycle Engineering, July 2011, pp. 268-273.

[21] . K. Kellens, W. Dewulf, M. Overcash, M. Z. Hauschild, J. R. Duflou, Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) CO2PE! initiative (cooperative effort on process emissions in manufacturing), Part 2: case studies, International Journal of Life Cycle Assessment, 17, 2, 2012, pp. 242-251.

[22] . V. A. Balogun, P. T. Mativenga, Modelling of direct energy requirements in mechanical machining processes, Journal of Cleaner Production, 41, 2013, pp. 179-186.

[23] . Shaohua Hu, Fei Liu, Yan He, Tong Hu, An on-line approach for energy efficiency monitoring of machine tools, Journal of Cleaner Production, 27, 2012, pp. 133-140.

* Guoyong Zhao, Guangming Zheng, Yunli Xu Department of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China *Tel.: 86-5332767917, fax: 86-5332786910 * E-mail: [email protected] Received: 2 October 2013 /Accepted: 22 November 2013 /Published: 30 December 2013 (c) 2013 International Frequency Sensor Association

[ Back To TMCnet.com's Homepage ]