Framework for Controlling Energy Consumption of Machine Tools J. Schlechtendahl 1, P. Eberspächer 1, H. Haag 2, A. Verl 1, E. Westkämper 2 1 Institute for Control Engineering of Machine Tools and Manufacturing Units, University of Stuttgart, Seidenstraße 36, 70174 Stuttgart, Germany 2 Institute of Industrial Manufacturing and Management, University of Stuttgart, Nobelstraße 12, 70569 Stuttgart, Germany Abstract: Guaranteeing energy-efficient operation of production systems through a-priori optimization is an extremely complex task. Therefore an alternative approach is necessary where controls can decide locally if an energy reduction is possible and set components to energy-optimal states. In this paper results from the research group ECOMATION are presented, describing the information flow that generates energy control loops on different levels based on model information and an appropriate communication and control infrastructure. The communication mechanisms in production systems and working machines are presented which allow using energy consumption values automatically and coherently on all required levels of detail and abstraction. 1 Introduction Production efficiency is an important element for the lifecycle balance of many products. Studies have shown that the ecological impact of modern machine tools is dominated by the energy consumption during its operational life (Zulaika et al. 2009), and that the consumed electrical energy and pressured air often contribute with over 20% to the total lifecycle cost (Dervisopoulos and Abele 2008). Being responsible for one third of the total primary energy consumption in developed nations (UBA 2007), industrial production companies are looking for energy saving techniques to reduce their ecological impact and cost. Optimizing the product, the supply chain, the production process chain and steps as well as the structure and components of manufacturing systems can contribute to resource- and energy-efficient production. It has to be kept in mind, though, that these strategies yield to an average optimization. The operation in an energy-optimal state is not considered.
2 In this paper, results from the research group ECOMATION are presented, describing the mechanisms in communication that are required to operate in an energy-optimal state. In chapter 2 an energy-based classification of components and machines depending on the ability to provide energy consumption values and mechanisms to influence the energy consumption are introduced. Mechanisms describing how components in a working machine can provide their energy consumption in standardized manner are presented in chapter 3, along with mechanisms that influence the energy consumption of components. Chapter 4 describes an interface between a working machine and the plant floor which enables an optimized flow of energy-relevant information and influences the energy usage. Chapter 5 displays how the energy information collected from different working machines and periphery systems can be used to determine the energy-optimal state of the factory and the strategies that can be used to reduce energy consumption through the adjusting lever described in chapter 3. 2 CLASSIFICATION OF COMPONENTS Different machine tools consist of different components and even in machine tools of the same type, some components can differ. In figure 1 the components of an Index V100 turning machine and their connections to power supply and control can be seen. The power consumers have been associated to four color-coded classes regarding their ability to be controlled by the machine control: 1. always on; 2. switched on/off commanded by machine control; 3. continuous state commanded by machine control; 4. switched on/off or continuously controlled independently. Components that are always active are, for example, the electronics in the control cabinet, the HMI, the PLC, the NCU and some fans or cooling devices. The consumption of these components constitutes the basic power load of the working machine. Some components are switched on and off by the machine control based on the current program e.g. the chip conveyer, the hydraulic pump or coolant lubricant pump. Other components commanded by the machine control are the feed drives and the main spindle. These have a continuous state, depending on the requirements of the process on the machine tool. But the current process can also influence the power consumption of switching components, e.g. the coolant lubricant system.
3 The states of the switching components without connections to the machine control are unknown to the machine control. For these, the state either has to be estimated by a mean value or a behaviour model for the component is necessary, in addition to the energy consumption model. This is possible for components like cooling devices, which are switching based on temperature, but impossible for components like machine lamps, which are switched on and off by the user. Fig. 1: Connections of the components of an Index V100 (Verl et al. 2011). 3 WORKING MACHINE-BASED CONTROL OF ENERGY CONSUMPTION 3.1 Strategies for accessing consumption values of components In order to enable a detailed consumption monitoring in a machine control, consumption values of all components have to be present. Today continuous state-commanded components like main spindle drives, for example, are typically connected over an automation bus with the control. These components often have the ability to provide their energy consumption via the automation bus, through standardized communication parameters (see figure 2), in case the component can measure the consumption value itself.
4 Machine Control Control parameters Component X Component attributes State 1 On Source: Bosch Rexroth Energy consumption value State 2 State 3 State 4 60% Power 30% Power Off Name of energy state Time to State (from operational into state) Time to On (from state into operational) Time min length of stay Time max length of stay Power consumption in State Energy to State Energy to operate Information about the available energy states in the device Fig. 2: Provision of energy consumption via automation bus. Components that are switched on/off commanded by the machine control are normally controlled by the PLC. The PLC controls components like hydraulic pumps which will be simply switched on or off depending on the process dependencies. For these components the machine control only knows the actual state but not the energy consumption itself. To calculate energy consumption, models for these components have to be created, which use the state of the component as input parameter and provide the actual energy consumption as output (see figure 3). Machine Control Control parameters Component X State 1 On Source: Bosch Rexroth Energy simulation values Simulation Models Component attributes State 2 State 3 State 4 60% Power 30% Power Off Control parameters Fig. 3: Simulation models with control information.
5 Components that are categorized as always on can also provide their energy consumption via models (see figure 4). Compared to switched on/off components these component models have no input values. Therefore a previously measured average value is used as simulation value. Machine Control Simulation Models Component X Source: Bosch Rexroth Energy simulation values no direct information State 0 On Fig. 4: Simulation models for always on components. For components that are switched on/off independently or continuously, independently controlled, it is difficult to provide an energy consumption value to the control. To enable a consumption value provision, additional measurement equipment has to be installed in the working machine. The measurement equipment can either measure the energy value directly or can determine the state of the component (e.g. measure if a fan is running through a current measuring); both cases shown in figure 5. The measuring of the state of a component or the energy consumption can be realized through the following two approaches. The first approach is to integrate the measurement system into the existing communication infrastructure. This will result in a reconfiguration of the machine control as new components have to be added to the automation bus. Depending on the machine type and the process the machine is used for, this can have an impact on the cycle times in which data is transferred from the control to the components, resulting in changes on the surface quality. On the other side, the energy measurement values can be transferred to the control in constant cycle times, only limited through the automation bus cycle time, which will enable a high resolution of the energy consumption measurement. The second approach for providing measured energy values to the control is through the Ethernet interface most control units are equipped with today. By means of a standardized communication protocol like OPC (OLE for Process Control) information can be read from the measuring unit by the machine control without the need to integrate additional components into the machine infrastructure. The disadvantage of this approach is that the cycle time cannot be as high as with an automation bus for data provision due to limitations by the OPC and Ethernet protocol.
6 Machine Control Measured energy values Measuring device Component state Component X State 1 On State 2 60% Power State 3 30% Power Source: Bosch Rexroth Source: Bosch Rexroth State 4 Off Acquired component state Energy simulation values Simulation Models Fig. 5: Additional measurement equipment for either state or energy consumption. Based on these approaches the overall energy consumption of a working machine can be monitored by the machine control in every state the machine can be operated in (except the machine off state). The problem in monitoring all components in a machine control is that the control does not know which components are present in a machine. All components have to be added manually to create an energy consumption map of the whole machine. How the energy consumption can be calculated automatically is described in the following subchapter. 3.2 Automatic provision of component information To enable the use of the strategies described in chapter 3.1 an automated approach for the exchange and the provision of energy information of all the machine tool s components is necessary. The approach has been implemented as energy information description language (EIDL) within the research group Ecomation. EIDL s main structure is based on the components and component-groups within a machine tool. The structure is built up as tree-like with different levels of detail considering the components and component groups. The machine tool itself represents the root node. Each attached sub node contains one component of the machine tool on the same level of detail. Depending on this level of detail, each sub node can contain further sub nodes with further components or component groups. An overview of EIDL s main frame is given in figure 6.
7 A B C Fig. 6: EIDL s main frame. To provide the necessary energy information each node within EIDL s main frame is equipped with two information nodes: attributes and energy information (see Box A in figure 6). The node attributes (see Box B in figure 6) contains data concerning the component that has to be specified with e.g., Name, Type, Manufacturer. To enable the use of strategies to access consumption values of all components, the second information node energy information (see Box C in figure 6) contains data that support the use of models and the available live values, which are present and accessible for the machine tool s control. The node model consists of e.g., the model type, required inputs and outputs with data types and essential configuration for the use of each model. Information about the available live values within the machine control is described in the node live value (see figure 7). It includes available energy measurement values, either from intelligent components or from additional measurement equipment, auxiliary values like current spindle speed or feed rates of drives and adjusting levers, meaning available control outputs that can manipulate the current energy consumption.
8 Fig. 7: Contents of the node live value. EIDL is implemented in XML language as XML schema, which is widely supported on different operating platforms. By defining the specific components and including the modelling and live value information the user derives an energy description of a machine tool. This derivation process is shown in figure 8. With software already on the market, it is possible to check the resulting machine tool s description conformity to the define EIDL XML schema. conformity test XML-schema XML-file EIDL energy description of multiple machine tools user specific components and component groups with modeling information Fig. 8: Process of derivation of a machine tool s description with EIDL.
9 3.3 Control of energy consumption The behaviour of most milling machines can be described via state models similar to the one illustrated in figure 9. 1 j machine off 7 J processing with CL 8 dry processing J 2 j emergency stop 6 j MS & CL active 5 J spindle running 3 j idle 4 j drive movement MS: main spindle, CL: coolant lubricant supply 9 J CL active Fig. 9: energy states of a milling machine. In certain states (e.g. idle) energy is consumed, even if no process is in progress. Regarding former research of the ISW this can amount to 30 % (Dietmair and Verl 2009). In order to reduce the amount of energy consumption, the control needs to switch off consumers not needed in the current state or at least shift them to an energy-optimal state. For shifting components in energy-optimal states the control has to know which energy states are available in the component and through which adjusting lever components can be sent into the energy-optimal state. Two approaches are possible: If the component is continuously state commanded like a main spindle drive, automation bus mechanisms can be used to inform the machine control about the energy states within the machine. To decide which energy state is the optimal state, attributes of the energy state need to be provided by the component to the control (see figure 10). This information is as follows (PROFIBUS 2010): How much energy is necessary to switch to the energy state and how much energy is required to bring the component back into an operation mode.
10 The time required to send the component into the energy state and the time necessary to move the component into an operating mode. Furthermore, some components which can be shifted really fast to an energysaving state might have some construction-related restrictions that do not permit a continuous shifting between the energy state and the operating state. In this case, a minimum time of stay needs to be available so that the control can be informed about these limitations. It is also possible that a component can only stay for a maximum amount of time in a certain energy state before it has to switch back to the operating state. An example could be: If a fan is switched off over a long term period this would result in the destruction of the cooled unit. Further the energy consumption of the energy state has to be available to the machine control for calculating whether switching to the energy state is useful. Fig. 10: attributes of energy states (SERCOS 2011). For shifting the component into the energy-optimal state an adjusting lever like an automation bus command is required. This dynamic approach can only be used in components that have some intelligence and can be addressed via an automation bus like Profinet or sercos. For all other components this approach is not usable. A second, more static approach that can be used for informing the machine control about energy consumption values and mechanisms to control the energy consumption is through the energy information description language (EIDL, refer to chapter 3.2). Through EIDL information about the origin of consumption values (e.g. by means of models, live values or a combination of control information with models) can be provided. In addition, the adjusting levers of every component can be made available for the machine control. This means, for example, which output in a PLC has to be switched off for shifting a pump into an energy efficient state.
11 All adjusting levers should have attributes similar to the ones described in the automation bus approach. Based on a dynamic and / or static approach a machine control knows how to influence energy consumption, even though the control has no strategies to do so yet. 4 INTERFACE BETWEEN WORKING MACHINE AND PLANT FLOOR For the development of efficient strategies, information from the plant floor is required. This information includes multiple criteria like product to produce, quality, time, energy consumption and the relation between them. This information needs to be generated in the plant floor and transferred to the machine control. Furthermore, a single manufacturing resource is not able to detect disturbances in the flow of material, in a peripheral system, or at an upstream station. Thus, the manufacturing resource cannot initiate measures, and a central management system is needed. On this level, the energetic optimization of the entire process chain including possible peripheral systems can be realized. In view of an overall planning, it is important to distinguish between the tasks detailed scheduling and process control and long-term planning (figure 11). Long term planning m-bom Detailed scheduling and process control Scheduling Schedule Process chain Validation (Balancing) Capacity planning Layout planning Development of conflict resolution Conflicts Process Fig. 11: Long-term planning and detailed scheduling (Verl et al. 2011). Detailed scheduling and process control is usually a functionality of the Manufacturing Execution System (MES) and consists of the anticipatory consideration and the reaction to unexpected occurrences. Supervisory control could e.g. switch downstream stations into a standby mode based on upstream machine states and a
12 model of the temporal and causal relationships between the stations. Here the realtime capability is a central feature (VDI 2007). The long-term planning considers a longer period of time and is a prerequisite for a detailed scheduling and process control. For this, real-time capability is irrelevant. Based on the required product properties, processes and resources are selected. In a further step, capacity and layout planning will be executed. A first approach for a model-based energy efficiency optimization for production planning was presented in (Dietmair et al. 2010): The approach is to link the material flow simulation of the process chain with state-based energy models of manufacturing resources. This makes it possible to optimize various organizational aspects of production in relation to low energy consumption. In a first step, various (basic) parallel and serial production system topologies and patterns for job scheduling have been investigated. It turned out that an enormous energy-saving potential lies in the organizational optimization. Details of the long-term planning including the peripheral systems were presented in at ICPR2011 (Haag et al. 2011). The focus of the following approach is on the main level-based detailed scheduling and process control. The aim is to exchange energy-relevant data with the machine controller or model-based. Implementation can take place based on the standardized software interface OPC, which fulfils all the conditions necessary. To carry out a detailed scheduling and process control on the main level two features are required in particular: The time-discrete, quasi-continuous capturing of readings on a monitoring system The event-discrete data collection and communication in a messaging system The monitoring system is needed to monitor the continuous energy data collection and expanded by a time stamp in the production management system (e.g. MES). Much more important is a functionality of a messaging system. This is the core functionality of the communication architecture. It enables bi-directional communication between the models or machine PLCs and the management system on the main level. A differentiation must be made between bottom-up messages and top-down messages. In bottom-up messages all communication of a hierarchically lower level to a higher level are summarized. Typically, this corresponds to the communication from the machine to the higher-level management system. Top-down messages are defined as all communication, which escalated from a hierarchically higher level to a lower level. Typically, it is therefore the communication from the management system to the machine.
13 To keep the data volume low, transparent communication is an important precondition. Here, the exchange of the following information seems to be useful: Timestamp (date on which the message was issued) Station (recognition of the sending respectively the receiving facility) Status (optional) (current status of the machine only in bottom-up messages) Message (message in the form of an identification number) Time span (optional) (suspected fault time or time in which the information is valid) Communication follows a mailing principle which allows a cross-level communication. OPC items are stored on the OPC Server. Each participant can access their own inbox and output. At first, a prototype of the system-wide communication architecture is being developed in ECOMATION. It is currently being tested for suitability for multilevel Monitoring and Control of Energy Consumption of a process chain including the peripheral systems. 5 PLANT BASED CONTROL OF ENERGY CONSUMPTION 5.1 Monitoring of Energy Consumption and Condition Monitoring The monitoring functionality can visualize the current energy consumption graphically and give employees on the shop floor an idea of the current consumption. Due to the permanent log of minimal consumption, average energy consumption and maximum use, a tolerance channel can be specified in which the system ranges energetically. This tolerance channel is continuously adjusted based on new field data. The implementation can take place either on one machine or for the entire production line including the peripheral systems. Deviations of energy consumption from the tolerance channel can be detected at an early stage (see figure 12). This functionality can be expanded into a comprehensive condition monitoring system, which is connected with the maintenance.
14 WORK WARMUP WAIT BLOCK SAVE ERROR SETUP OFF Min Average Max 0 500 1000 1500 2000 Power [W] Power [W] 2500 Event examine 2000 1500 1000 actions Max. measured value Min. 500 0 WORK WARMUP SAVE Time [h] Fig. 12: Monitoring of energy consumption. 5.2 Messaging for the energy-optimal control of production The bidirectional messaging system between the resources of the main processes and the management system and between the peripheral systems and the management system provides the core functionality of the developed method. One exemplary application of the messaging system is the energy-optimal control of all resources that are involved in production: In case of failure of one machine, the entire process chain including the periphery has to be set in an energy-optimal state. For this purpose, the failed resource reports a bottom-up message to the management system. This message includes the current time of the system, the station ID and the message ID of the failure. This message can be generated automatically. Alternatively, the maintenance engineer or the operator may select the message directly on the use terminal of the resource (see figure 13).
15 management system...... peripheral systems.......................... failture resource 1 resource 2 resource 3 resource 4........ = bottom-up message...... = top-down message Fig. 13: Messaging system between resources and management system The failure ID allows the management system to determine the expected period of the failure via a connected database system. Based on this information, it can be decided whether any further systems involved will be put in a standby or sleep mode. It is not always appropriate that the same decision is made for all resources involved: Variable transient or long warm-up phases may be the reason for this. It is also possible that centralized peripheral systems provide several production lines and upstream and downstream resources of the main processes which have different buffer levels to be processed. These facts have to be considered when determining an energy-optimal global set of parameters for both resources of the main processes and peripheral systems. On this basis, the management system sends a top-down message to all systems involved to put them into an energyoptimal state. 6 SUMMARY In this paper results of the research group ECOMATION have been described, for example, how different consumers in a working machine can be categorized into different groups depending on their ability to provide energy consumption values. Based on this categorization different approaches have been presented to determine attributes of different energy states required for energy consumption control. To support the automated use of the presented energy saving strategies, an energy information language containing all the necessary information for the use of simulation models and data available on the machine control has been introduced. Based on attributes of the different energy states, controls can switch to an
16 energy-optimal state if information from the plant floor is available. To provide this information, mechanisms for the communication between machines and the plant floor have been illustrated. These different mechanisms can be used to create a production system-wide energy control loop to reduce the energy consumption considerably. 7 ACKNOWLEDGMENTS The work presented was funded by the German Research Foundation (DFG) in project FOR1088 ECOMATION. 8 REFERENCES Dervisopoulos M, Abele E (2008): Project CO$TRA- Life Cycle Costs Transparent. Final Report, PTW, University of Darmstadt Dietmair A, Verl A (2009): Energy Efficiency Fore-casting and Optimisation for Tool Machines. MM Science Journal, 2009/03:62-67 Dietmair A, Haag H, Böck J, Rahäuser R (2010): Model based energy efficiency optimization for planning and Control. Third International Conference on Eco-Efficiency, Egmond aan See, Netherlands Haag H, Siegert J, Westkämper E (2011): Planning and Optimization of Energy Consumption in Factories considering the peripheral systems. ICPR 2011, Stuttgart, Germany Verl A, Westkämper E, Abele E, Dietmair A, Schlechtendahl J, Friedrich J, Haag H, Schrems S (2011): Architecture for Multilevel Monitoring and Control of Energy Consumption. CIRP LCE 2011, Braunschweig, May 2011 Zulaika J. et al (2009) Using Stability Lobe Diagrams for the Redesign of a Machine-Tool based on Productivity and Eco-efficiency criteria. Proc. 12th CIRP Conf. of Modeling of Machining Operations, Spain PROFIBUS Nutzerorganisation e.v. (2010): PI White Paper: The PROFIenergy Profile, pp.10-11, Karlsruhe, Germany. SERCOS International e.v. (2011): Draft SERCOS Energy. SERCOS International e.v., Süssen, Germany. UBA (2007): Umweltdaten Deutschland. Umweltbundesamt, Germany VDI (2007): VDI 5600 Blatt 1 Fertigungsmanagementsysteme Manufacturing Execution Systems (MES), VDI Richtlinien 2007 9 CONTACT Jan Schlechtendahl (jan.schlechtendahl@isw.uni-stuttgart.de) Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart