Available online at ScienceDirect. Procedia CIRP 62 (2017 )

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1 Available online at ScienceDirect Procedia CIRP 6 (017 ) th CIRP Conference on Intelligent Computation in Manufacturing Engineering CIRP ICME '16 Automatic assessment of machine tool energy efficiency and productivity Matthias Hacksteiner a*, Fabian Duer a, Iman Ayatollahi a, Friedrich Bleicher a a Institute for Production Engineering and Laser Technology (IFT), Vienna University of Technology, Austria * Corresponding author. Tel.: ; fax: address: hacksteiner@ift.at Abstract This work presents an approach to determine relevant energy efficiency and productivity KPIs of machining processes based on a realtime interpretation of sensor data and machine control data. A comparison of the actual power consumption during machining with an energetic model of the loadfree condition enables the calculation of energetic efficiency and primary processing time. The approach was tested on a CNC turning and milling center equipped with power meters and compressed air sensors. Sensor data as well as relevant machine control data are read, processed and recorded via SCADA software in order to automatically calculate certain KPIs. 017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BYNCND license 016 The Authors. Published by Elsevier B.V. ( Selection and peerreview under responsibility of the International Scientific Committee of 10th CIRP ICME Conference". Peerreview under responsibility of the scientific committee of the 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering Keywords: Machine tool; Monitoring; Sensor; Machine control; Data acquisition; Modelling; Energy efficiency; Productivity; OEE 1. Introduction and motivation As manufacturing industries are facing economic challenges due to increasing global competition, they continually need to increase productivity while reducing manufacturing costs. The industrial sector is a substantial consumer of energy and other resources and thus causes severe environmental impact [13]. Therefore, ambitions to reduce the energy intensity of manufactured products are suitable to enhance both economic competitiveness and environmental sustainability. In recent years, different legislative and normative measures have been taken in order to reduce industrial energy consumption. ISO 50001, for instance, provides a systematic approach to continuously improve energy performance and specifies requirements for process and equipment design, measurement and documentation [4]. EN 1631, on the other hand, suggests a methodology for the evaluation of energy data in order to determine the energy efficiency of certain units (such as production systems) enabling energy performance monitoring and a comparison with other units [5]. Energy efficiency benchmarking is a suitable way to reveal optimization potentials concerning energy consumption. Utilized in great quantities, machine tools constitute substantial industrial energy consumers [6, 7]. Therefore, a reduction of machine tool energy demand can significantly improve the environmental performance of manufacturing processes and thus the CO footprint of consumer products. A traditional product lifecycle consists of three stages: manufacturing, use and end of life. For a machine tool itself, the use phase is the most energy intensive phase causing 60% to 90% of CO emissions during its lifecycle [8]. Recently, a draft standard for the environmental evaluation of machine tools during their use phase was introduced, presenting a methodology for a reproducible quantification of energy supplied to the machine in different operating conditions [9]. Gontarz et al. presented a modular configuration approach for machine tools based on multichannel measurements in order to improve energy efficiency and enable total cost of ownership (TCO) calculations [10]. Several studies have been carried out in order to model the energy consumption of machine tools and thus to determine the environmental impact of goods produced [1116]. The machining time is a key influence factor for the energy demand of machine tools, especially such with high base load (i.e. machines with large peripherals such as hydraulic, machine cooling, exhaust and cooling lubricant systems). Various studies have shown that high material removal rates decrease machine tool overall energy consumption when keeping the volume of removed material constant due to decreasing machining time [13, 17, 18] The Authors. Published by Elsevier B.V. This is an open access article under the CC BYNCND license ( Peerreview under responsibility of the scientific committee of the 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering doi: /j.procir

2 318 Matthias Hacksteiner et al. / Procedia CIRP 6 ( 017 ) Further optimization potential arises from a proper choice of the tool path strategy during machining [1, 18, 19]. As a conclusion, it is of high importance to use optimal machining procedures and parameters in combination with performant tooling systems in order to minimize cycle times and thus energy consumption. However, over the course of machining, the process performance might change due to tool wear, suboptimal machine settings or operating errors. Hence, it is expedient to monitor certain performance indicators over time in order to assess and compare different processes. A typical energy performance indicator (EnPI) used for benchmarking within or between units is the specific energy consumption (such as energy per unit produced) [5]. Emerging trends in such indicators can not only help to validate changes in energy efficiency but also act as evidence for issues like process plan deviations and changes in process stability or quality. Progress in the field of sensor and data acquisition technologies enables realtime acquisition and interpretation of machine tool data. Vijayaraghavan et al. developed an automated machine tool energy monitoring system using MTConnect and applied event stream processing techniques to automate the analysis of energy consumption [0]. Hu et al. introduced an online approach for energy efficiency monitoring of machine tools via spindle power measurement based on power balance calculations [1]. Shin et al. presented a predictive analytics model for machining processes using neural networks []. In their work, big data infrastructure was fed with STEPNC plan data and MTConnect machine monitoring data to derive an analytic model for the machine tool power consumption depending on cutting parameters. Bhinge et al. also introduced a machine monitoring system architecture based on data acquisition via MTConnect [3]. A datadriven energy prediction model using Gaussian process regression was developed using power consumption sensor data as well as operating data obtained from NCcode and cutting simulation. Eberspächer et al. presented a model and signalbased power consumption monitoring concept and approaches to reduce the power consumption [4]. In their work, machine control data read via OPC UA and additional sensor data are used as input for consumption simulation models to provide the machine operator with detailed power consumption and distribution data. This work presents a different machine monitoring approach and a methodology to determine relevant energy efficiency and productivity key performance indicators (KPIs) of machining processes based on realtime interpretation of sensor data and machine control data. A comparison of the actual power consumption during machining with an energetic model of the loadfree condition enables the calculation of the energetic process efficiency and the primary processing time. The approach was tested on a CNC turning and milling center equipped with power meters and compressed air sensors. Sensor data as well as relevant machine control data are read, processed and recorded via SCADA software and certain KPIs are automatically calculated, visualized and stored.. Experimental setup In the framework of the research project ecoproduction, which focused on the development of methods and tools to enhance energy efficiency and productivity of producing SMEs [5], an energy monitoring and control system was implemented in a pilot factory equipped with machine tools. The electric power and compressed air consumption of these machines and certain subcomponents as well as peripherals is recorded and visualized. Furthermore, machine control data acquisition was implemented for one of the machine tools. The used SCADA software system, SIMATIC WinCC Open Architecture, supports different communication protocols and features a SQLbased database. Fig. 1 shows a diagram of the experimental setup for sensor and machine control data acquisition for the CNC turning center EMCO MAXXTURN 45. The machine tool features a movable counter spindle as well as a tool turret with driven tools (for milling and drilling operations) and thus seven individual drives. The machine has a power rating of 5 kva and main spindle and counter spindle drive capacities of 13 and 10 kw, respectively. Figure 1. Diagram of the experimental setup. Active power is internally calculated by SENTRON PAC 400 power monitoring devices from measured electric voltage and current signals. The according data are transferred to the SCADA system via MODBUS TCP protocol. Compressed air volumetric flow and pressure are measured with FESTO SFAB and SDE1 sensors, respectively. The sensor data are read by SIMATIC S7100 PLC and transferred to the SCADA system using TCP/IP based S7 messaging. The same S7 protocol is used to read drive data (such as active power consumption and speed) from the SINUMERIK 840D sl machine control via according data block addresses. For both communication protocols (MODBUS TCP and S7), data transmission is softwaredriven with polling cycles of 100 ms. The realized temporal resolution of signals read from the machine control and PLC is around 100 ms on average. The power monitoring devices, however, deliver new data in a mean interval of around 00 ms.

3 Matthias Hacksteiner et al. / Procedia CIRP 6 ( 017 ) Table 1 shows an exemplary overview of data read from the machine control and Fig. a visualization of the acquired data in the SCADA software system. The current electric power and compressed air consumption of the machine tool as well as the power consumption of its cooling lubrication, hydraulic and drive systems are displayed and plotted. Furthermore, data read from the machine control such as the operation mode (JOG, AUTO etc.), the NC program status (program running, cancelled etc.) is visualized. 3.. Determination of process productivity Fig. 3 shows the measured power consumption of a machine tool drive system (axes and spindle drives) for exemplary machining operations performed on a work piece and also without work piece (i.e. air cutting). According valueadding and nonvalueadding times are highlighted in different colors. Table 1. Exemplary data read from SINUMERIK 840D sl machine control. type example data block address operation mode bool JOG DB11.DBX6. NC program status bool program running DB1.DBX35.0 speed float zaxis DB50.DBD18F active power float main spindle DB50.DBD04F Figure. Visualization of the acquired data in SCADA software. 3. Method 3.1. Determination of operating condition and availability For the given machine tool, following operating conditions were defined from an energetic point of view: off, standby, ready for processing and processing. While the operating condition processing is determined from the NC program status program running, the others are distinguished via an interpretation of measured drive system power consumption on the basis of an energetic evaluation of the machine tool according to ISO/DIS [9]. On a side note, an approach was developed to additionally determine the operating condition processing from measured energy data only by detecting dynamic changes in the power and/or compressed air consumption. This enables the assessment of the operating condition also for machine tools without machine control data acquisition. Finally, the availability is simply determined as the share of the condition processing in a desired timeframe (e.g. one shift). This lays the basis for the calculation of the overall equipment effectiveness (OEE) of the machine which can be determined from availability, effectiveness and quality rate [6, 7]. Figure 3. Power consumption of a machine tool drive system for exemplary machining operations performed with and without work piece. To determine the productivity of the process ς P, the primary processing time t pp is set into relation to the total cycle time t cycle of the machining process (see equation 1). This KPI is suitable to compare machining processes concerning their productivity (i.e. the ratio of valueadding time compared to the total machining time) as low values might act as an indication that the process design or execution is suboptimal. = t pp t cycle (1) The cycle time corresponds to the according duration of the program running signal in condition true. The primary processing time is defined as the total duration of tool engagement including preceding and subsequent axis jogging without chip removal (cf. Fig. 3). Thus, it is the sum of durations of movement with machining feed rate during a machining process. In the presented approach, the primary processing time is determined via an interpretation of the resulting velocity v res of all axis motions: v res = v x + v y + v z + v z () The primary processing time is increased if the resulting axis velocity is larger than zero (e.g. standstill) and considerably smaller than the minimal rapid traverse speed (and, thus, corresponds to typical machining feed rates). The proportion of tool engagement time t cut during movement with machining feed rate acts as another indicator for inefficient NC programming or machine operation:

4 30 Matthias Hacksteiner et al. / Procedia CIRP 6 ( 017 ) ς cut = t cut t pp (3) The tool engagement time is determined via spindle power consumption. It is only increased if spindle power signals are in certain ranges, i.e. nonzero and below high values typical for spindle acceleration processes Determination of process power and efficiency The power necessary for the cutting process P P is calculated from the actual power consumption of the active spindle drive P loaded and the power consumption of the according drive at the same speed in unloaded condition P unloaded (see equation 4). While the former is directly read from the machine control during machining, the latter is determined via spindle models P k(n) depending on the according spindle speed read from the control. Note that in equation 4, the indices refer to the spindle type (1 = main spindle, = counter spindle, 3 = tool head). 3 P P = P loaded P unloaded = P control k P k (n control k ) k = 1 (4) In order to determine the power consumption of the spindle drives in unloaded condition (i.e. air cutting) as a function of speed, spindle tests were carried out. In these tests, the speed of the according spindle was increased from idle state to top speed in 100 rpm increments with defined hold times at each speed. During the tests, which were carried out five times for each spindle, all other drives were kept in idle state. The power consumption during the hold times was arithmetically averaged for sensor and machine control data over all tests. Fig. 4 shows the results of this evaluation in which the sensor data was corrected by the respective base load of the drive system (i.e. the power consumption of the drive control). The results show good agreement, especially for the tool head. It is noteworthy, however, that for the main and counter spindle the machine control data scatters strongly at certain speed ranges which can lead to uncertainties in the calculation of the process power. For this reason, the process power is calculated from a moving average value of the control data. Mathematical fitting was performed on the results in order to obtain spindle models P k(n) as a basis for the calculation. Table shows polynomial fit coefficients of measured spindle power consumption (in kw). In order to determine the process efficiency η P, the process power is set into relation with the measured total electric power consumption of the machine P el as well as an equivalent electric power due to compressed air consumption P pn and thermal flow P th (e.g. due to external machine cooling, cooling lubricant processing or exhaustion): Figure 4. Power consumption of main spindle, counter spindle and driven tool head in unloaded condition (sensor data and machine control data). Table. Modelled power consumption of main spindle, counter spindle and driven tool head in unloaded condition via polynomial P(n) = a n + b n + c. main spindle counter spindle n 1800 rpm n > 1800 rpm n 400 rpm n > 400 rpm a b c R tool head Fig. 5 shows a graphical representation of the spindle power models P k(n) used for the calculation of the process power. P P η P = P P = (5) P tot P el + P pn P th Figure 5. Modelled spindle power consumption.

5 Matthias Hacksteiner et al. / Procedia CIRP 6 ( 017 ) The proportional compressor power is determined from the compressed air volumetric flow V and pressure p of the machine tool assuming adiabatic compression (see equation 6). The efficiency of the compressed air system η pn was determined from monitoring data and modelled as a function of volumetric air flow V. The equation was simplified by assuming ambient conditions (T a and p a) and physical properties of compressor intake air. Note that with simplified equation 6, the compressor power is obtained in kw when the volumetric flow is inserted in Nl/min and the pressure in bar. Therefore, the process energy calculated from machine control data via the presented approach could be validated. The results show excellent agreement with less than 1% deviation. P pn = V ρ c p T a p η pn p a κ 1 κ 1 33 V (p ) V (6) As the considered machine tool is not connected with external cooling or exhaustion systems, the thermal share in the total power calculation is omitted. However, it could be modelled according to equation 7 as a function of flow V, inlet temperature T i and return temperature T r of the according fluid: P th = V ρ c p (T i T r ) η th (7) To calculate the mean process power and mean process efficiency for a given machining process, the according signals are simply averaged over the cycle time. The total energy and process energy for a given work piece manufactured are determined via mathematical integration of the power signals: t cycle E tot = P tot dt = P tot i t i t i 1 0 t pp E P = P P dt = P P j t j t j 1 0 j i (8) (9) Fig. 6 shows a schematic flowchart of the calculation procedure which is executed at each update of the acquired machine control data (i.e. every 100 ms on average). All relevant calculated variables are then stored in the database in order to enable the detection of emerging trends, the comparison of different processes or the validation of optimization measures. Fig. 7 shows a visualization of the calculation results for an exemplary workpiece machining process on the main spindle. The calculated process power is visualized in a graph and compared to the measured machine total and drive system power consumption. Furthermore, the current operating condition and the resulting axis speed as well as spindle speeds are plotted in separate graphs in order to facilitate an interpretation of the power signals. Finally, the calculated productivity and energy efficiency KPIs are displayed and updated as soon as a new process starts (i.e. the operating condition changes to processing again). The displayed machining process was repeated without workpiece (i.e. air cutting) which enabled a calculation of the process energy from measured drive system power signals (cf. Fig. 3 and equation 4). Figure 6. Flowchart of the calculation program. Figure 7. Visualization of the calculation results in SCADA software.

6 3 Matthias Hacksteiner et al. / Procedia CIRP 6 ( 017 ) Conclusions and outlook This work presents a monitoring approach for the automatic and realtime determination of energy efficiency and productivity KPIs of machine tools. It was tested on a CNC turning and milling center equipped with power meters and compressed air flow and pressure sensors. Sensor data as well as relevant machine control data are read, processed and recorded using a conventional SCADA software system. The approach can be used to raise the awareness of machine tool operators and NC programmers concerning equipment and process performance and to demonstrate the consequences of their actions towards energy efficiency and productivity. Furthermore, it is suitable to benchmark different machine tools producing similar products and to evaluate machine tool availability and effectiveness as a basis for OEE calculation. Future activities will focus on developing a new monitoring framework for the pilot factory using MTConnect for platform independent sensor and machine control data acquisition. In parallel, following previous work [9, 30], OPC UA servers will be developed for sensor data acquisition and incorporated into an existing OPC UA server exposing machine tool control data. Thus, testing the presented approach on other machine tools (i.e. other machine controls) will be facilitated. Furthermore, future work could focus on extending the presented methodology by the last step of OEE determination, i.e. quality rate. It has been shown that spindle power consumption increases with increasing tool wear [31]. Therefore, emerging trends in the process power determined via the presented approach could act as an indicator for tool wear and therefore work piece quality. According empirical cutting power models for tool wear monitoring as proposed in previous studies [3, 33] could be adapted for tool energy input and implemented into the presented calculation routine. Following up previous studies [34, 35], the presented approach could also be extended by machine learning functionalities in order to enable indirect quality monitoring by predicting tool wear or surface roughness. Acknowledgements The presented research was mainly funded through the Collective Research Networking (CORNET) project ecoproduction [5]. The authors gratefully appreciate the support of EMCO and Siemens. Commercial systems mentioned in this work should not be interpreted as recommendation or as implication that these products are necessarily the best available for the purpose. References [1] Hauschild M, et al., From Life Cycle Assessment to Sustainable Production: Status and Perspectives. CIRP Annals Manufacturing Technology 005; 54(): 11. 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