CC BY-NC-ND
0Available online at www.sciencedirect.com
SciVerse ScienceDirect
Procedia CIRP 7 (2013) 407 - 412
www. elsevier. com/locate/procedia
Forty Sixth CIRP Conference on Manufacturing Systems 2013
Integrated control system simulation for supporting changes of routing strategy in an automated material flow system
Azrul Azwan Abdul Rahman*, Günther Seliger
Technische Universität Berlin, Pascalstr. 8-9, 10587 Berlin, Germany
_* Corresponding author. Tel.: +49-30-31427095; fax: +49-30-314.22014 E-mail address: rahman@mf.tu-berlin.de_
Abstract
Increased demand for customised products, sophisticated scheduling requirements caused by shorter product life cycle and hardly foreseeable disturbances have created a new challenge for the manufacturing industry. Planned production schedules often become ineffective in actual execution on the shop floor. If forecasts become less and less accurate, support for continuous changes is helpful. Given the high degree of automation in manufacturing systems, automatic control systems have become central to shop floors' responsiveness. However, their state-of-the-art architectures are unable to cope with the challenge successfully. Improvements in information and communication technology makes the integration of simulation and control system more promising. The paper proposes an approach for supporting changes of routing strategy in an automated material flow system by utilising the integration. The approach includes (re-)planning of the automated material flow system, commissioning its logic control and controlling the material flow.
© 2013 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo C unha Keywords: Simulation; Control System; Changeability; Automated Material Flow System
1. Introduction
Impacts of globalisation have created a new challenge for manufacturing industry. The possibility for greater integration within the world economy through movements of goods and services, capital, technology and labour is leading to hardly predictable market situation. Customers are coming from all over the globe and demand for more customised products. Besides, global competitors keep competing for introducing new products which effects on shortening the product life cycle. The implications of this situation are the diversity of environments within which the production planning and control system must operate have increased and will continue to do so [1]. In other word the scheduling become more sophisticated and even worst the planned production schedules often become ineffective in actual execution on the shop floor due to barely foreseen disturbance.
As forecasting and planning become less and less reliable, the support for continuous changes is helpful.
Short response times and high changeability in layout and in processes for the production and logistics structures are strongly required [2].
Changeability is used as a generic term for various abilities to carry out change within the manufacturing industry [3]. Changeability is defined as a characteristic of a production system that enables an economical, timely and proactive adaptation of all factory components, levels, and processes [4]. Nyhuis et al. differentiates changeability from flexibility by its ability and potential to realize fast adaptation within narrow range of change, both at the organization and the strategic levels, with low investment (Fig.1) [5].
In current practices, ad-hoc solutions have been developed in response to changes, which the implementation often took several months or years. Frequently, the expenses used to implement the solution did not produce the expected payback, since the next change wave often starts while earlier adjustments are still taking place [6]. It will not only deprive of time and cost, unexpected results also might appear during the implementation.
2212-8271 © 2013 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha doi: 10. 1016/j .procir.2013.06.007
Flexibility range 2
Fig. 1. Flexibility vs changeability [5]
In response to the individual customer's demands and inconsistence market trends has result in small batch sizes production, thereby increasing the number of transport requests together with their routing path. This substantially increases the costs for the entire material flow and their control [7]. Therefore in dynamic production scenarios manual material flow systems are mostly used, as present automated material flow systems are less flexible and changes of their control are difficult in case of complex systems.
However manual material flow systems such as forklift and stacker possess neither highest productivity nor quantifiable advantages like the zero error strategy or time optimised applications. Furthermore manual transportations cause not only high working costs, but also more expose to the surrounding and might reduce quality [7], [8].
Thus improvement in automated material flow systems is still required, which allow operating in cost-efficient and changeable in hardly predictable production environment. A solution lies in the design of autonomous, decentralised controlled material flow system with standardise interfaces on the physical and its control level [7].
Evolvement of ICT has encouraged development of simulation software and broadens their application in manufacturing industry. The latest state-of-the-art simulation software offers automation hardware integration and real time online functionality. The paper will present an approach which utilised simulation capabilities with real time Programmable Logic Controller (PLC) based control system for supporting changes in routing strategies of an automated material flow system. In presenting this approach systematically, the paper is structured as follows: Section 2 will present the state of the art in controlling and reconfiguring of automated material flow systems. Continue with the state of the art in simulation technology that supports automation integration in section 3. Architecture of the integrated control system simulation approach is going to be presented in section 4. In section 5 the approach application study will be described and its outcomes will
be explained followed by future perspective and challenges in section 6.
2. Automated Material Flow System
Automation is the independent performance of processes by means of suitable technical means [9]. The main elements of an automated material flow system are the control, sensors and actuators that record and influence the material flow processes such as translocation, i.e. the transport of products (Fig. 2).
Fig. 2. Principle of automated material flow systems
The standard DIN 19233 defines control as the operation in a system, in which one or several input variables influence the output variables according to the system rules or laws [9]. The material flow control coordinates the flow of material through synchronizing information and material flow, in order to meet the goal, by providing the material on the right time and right place in the desired quantity and quality [10]. The technical structure of a material flow control is mainly determined by the type and number of the processes to be automated. It may either be centralized or decentralized.
2.1. Reconfigure of control system
Most of the automated material flow system particularly in automotive production lines or systems have generally been designed using PLC [11], which is the preferred choice for user-customised automation on the industrial shop floor [12]. An actual control of the system are normally configured once for a designated mechanical structure and routing strategy. If the mechanical structure or their routing strategy is changed, the control system has to be configured again.
Reconfiguration of control system implies a change to the control software or hardware that is initiated by the user and/or by some automatic process. Typically, a change would be required in the event of a system upgrade (software components or mechanical structure)
or as a contingency to some event such as failure of mechanical structure or a systematic error in the software.
At present the reconfiguration of the automated material flow control system is has often to be done manually and dominated by a long-term procedure of configuring and the subsequent start-up [13]. The reuse of the control system, configured once, is not always possible because of the lack of a systematic reconfigure method. That means after each changed of mechanical structure or routing strategy the control system has to be configured and started up again.
The reconfiguration procedure involves a process of first editing the control software offline while the system is running, then committing the change to the running control program. If the change involves modification of mechanical structure, the dependencies between different software modules inside the control software and between the mechanical structure and the control software are needed to be resolved.
When the change is committed, severe disruptions and instability can occur as a result of high coupling between elements of the control software and inconsistent real time synchronisation. For example, changes of an output statement can cause a chain of unanticipated events to occur throughout a control logic program as a result of high coupling between various logics in the program. Consequently, the current approach for reconfiguration is risky. The task is usually reserved for an expert who has intimate knowledge and rules of generating a valid control system configuration, and usually not the planner himself. In order to reduce the reconfiguration time and the risk, a procedure for reconfiguring is needed, which describes all necessary reconfiguration steps. These integrated and systematic approaches that support the user or planner in the reconfiguration process of an automated material flow control system are still missing [14].
Pritschow et. al. has introduced one solution to cope with reconfiguration of control system due to mechanical structure modification. They described a self-adapting control system supported by a reconfiguration of a Reconfigure Manufacturing System (RMS). It is an open architecture control system with additional software components that detect changes in the mechanical structure of the RMS, to deduce the necessary adaptations of the control system and to automatically generate reconfiguration orders for the control system [13].
The need for self-adapting control system has brought several efforts in improving the control software itself. To support it, International Electrotechnical Commission (IEC) has developed a new standard IEC 61499 specifically as a methodology for modelling distributed control systems. A group called Intelligent Systems
Group has done some of the earliest work using IEC 61499 for dynamic reconfiguration during the runtime. This work used agent based architecture in managing the reconfiguration [15], [16].
Another contribution towards self-adapting control system has been conducted at Automation and Control Institute (ACIN), Vienna University of Technology in cooperation with PROF ACTOR Research, Austria with a project named ¡iCRONS (Microholons for Next Generation Distributed Automation and Control). The objective of the project was to develop middleware for flexible, modular mechatronic components, which support reconfiguration in real-time [17].
3. Simulation
In a world where complexity, dynamics, and change dominate, it becomes vital to understand systems behaviour and the parameters that affect their performance. This is particularly true in the development and operation of manufacturing systems; activities in themselves are characterized by complexity and change. To represent, analyse and evaluate this complex, dynamic reality, the need for simulation has long been recognized.
3.1. Controller integrated
Evolvement of ICT has inspired development of simulation software and broadens their application in manufacturing. The latest state-of-the-art simulation software offers hardware integration and real time online functionality. Examples of this include Virtual Reality (VR) and Augmented Reality (AR) as well as Hardware in the Loop (HiL), Software in the Loop (SiL) and Realistic Robot Simulation (RRS) [18].
HiL concept substitutes parts of the system by simulation or mathematical models, while an electronic or mechanical component (e.g. controller) is linked with the simulation computer in a closed control circuit [18] while in SiL concept, simulated control system is used with connection to the real system [19].
One of the linking possibilities offered by simulation software is OPC. OPC is the acronym for Object Linking and Embedding (OLE) for Process Control and it is the most widely used vendor-independent specification for communicating control system products, normally drivers between field equipment and control or human interface devices. OPC has and is still developed by the OPC Foundation. The OPC server, which is vendor specific, is connected to the controller [20]. The OPC clients e.g. as implemented in material flow simulation, are general and can connect to any server. This makes it possible to connect to a controller in a general way, even if the OPC server is vendor specific.
Several researches have applied HiL and SiL concept in performing commissioning process [21], [22], [23]. With the aim to use simulation in verifying the control logics code before implementation at the real system, this activity is called virtual commissioning. The virtual commissioning technology allows control software commissioning and ramp-up process to be parallelised to the production and assembly of the manufacturing system phase resulting saving of time and cost. It has shown that the control software quality in term of fulfilled requirements was improved by more than 100% whereas the commissioning time was reduced by 75% [24]. In 2010, automotive companies such as Daimler AG, Dürr AG and Audi AG have seen to include virtual commissioning in their production planning phase [25].
4. Integrated control system simulation
Synchronization of online and offline simulations by building and providing the digital system model with the same details for real time control and medium term production planning will ensures rapid response on dynamic manufacturing disturbances on multi-level of factory organisation. In order to allow this synchronization, connection between virtual and real system has to be established and the system architecture need to be well defined.
By extending the HIL and SiL concept with real time functionality has brought the possibility for simulation software to be used in online material flow system processes and not only limited to the offline processes such as planning and virtual commissioning [26]. Based on the outcome of several experiments, an approach for
integrating the material flow simulation software with the PLC-based control system of a material flow system will be presented.
The main idea of this integration is to utilize material flow simulation for logical reconfiguring of the control system in response to changes of routing strategy. Modification of the control logics program can be done in virtual environment and the effect of the new routing strategy can be analysed by simulation before its implementation. The control logics program of the improved routing strategy written in the simulation software can then be transferred to the real system and directly used for controlling the automated material flow system.
4.1. Architecture
Application-oriented integration of three software programs is used in realising this approach (Fig.3). The software programs are material flow simulation, software controller, and OPC Server. OPC server-client architecture is used as basis for this connection which required all of the software to supports OPC standard.
The core of this integration is the material flow simulation software. It will consist of system models and control models. System model has to be constructed as close as possible to the real system with homogeneous level of details. All the relevant I/Os have to be considered and included in the model as Boolean variables. These variables are then mapped with the system I/Os through OPC items. OPC server has to be used for creating the items.
Fig. 3. The application-oriented integration in approach for integrated control system simulation
The control model consists of control logics program. Here the rules and routing strategies have to be coded in the form of simulation programming language not the lower level PLC programming language based on IEC 61131. This allows the changes of routing strategy to be performed by changing the simulation codes without converting or translating from one language to another.
Depending on either the system model or the real system is connected to the control model, two operations can be performed within this architecture. By connecting with the system model, analysis of the routing strategy and virtual commissioning can be performed. While controlling of the real system with the selected routing strategy can be done by enabling the OPC connection.
The information flow in controlling of the real system can be described as: The software controller which connected with the system I/O modules will receive the signal from real system. This signal will be sent to the material flow simulation software for evaluation by the control logics program. If the condition is satisfied the signal will then be sent back to the software controller where actuators or drives are then being activated
4.2. Demonstration
In order to test and evaluate the approach, experimentation with a real PLC-controlled material flow system located at Production Technology Centre (PTZ), Technische Universität Berlin has been conducted. The material flow system used in this experiment consists of six linear motor drives belt conveyors which connected to each other to perform a closed loop flow (Fig. 4). Each conveyor line is divided into several segments. Each segment allows only one carrier at one time in order to avoid collision between carriers. Thirty one sensors and eighteen stoppers are used to ensure this condition. Switches are used for changing the direction of the carrier from one conveyor line to another. There are eight switches in the system and four lifting table for workplaces.
■ '.m.r ■ n- n - n
♦ 3 ■ * c ■ c □ * u * J c f
n .n ■C >■ 888 lQ i u i T
Fig. 4. An automated material flow system for demonstration
In this experimentation, Siemens Tecnomatix Plant Simulation has been used as material flow simulation software, Siemens Semantic WinAC as software controller and Siemens Semantic OPC Server. Reliability of the approach and its architecture has been tested by performing three scenarios interchangeable. The scenarios are developed at the control model with three different routing strategies. Simulations are performed and the material flows are observed. At this stage, the experiment is done through connection of the system model and the control model. The outcome from the simulation is recorded and analysed. Then the same control logics programs in the control model are tested with the real system. Observation has been made during the process and the time for the real system to complete each scenario is recorded.
From the observation, the real system performed the same material flows as shown in the simulation. The only drawback is that simulation time will be completely different than the one expected from a real-time system. The comparison of the completing time between simulation and real system is shown in Table 1.
Table 1. Completion times' comparison between simulation and real system
Scenario Simulation (min) Real (min) Difference (min)
One 1:03:41 56:21 -7:20
Two 27:49 26:55 -0:54
Three 30:33 32:45 2:12
5. Conclusion and future remarks
In respond to routing changeability challenge for a PLC-controlled material flow system, the paper presented an approach for supporting reconfiguration of its control system due to changes in routing strategy while maintaining predictable and stable system behaviour. To give the explicit picture of the approach, the architecture and an application study are described.
The outcomes of the study have seen to be positive. The changes from one routing strategy to another can be performed in significant short time due to no conversion is needed from the PLC language to simulation language. Using the same model for planning and controlling the automated material flow system has ensured direct implementation of the planned routing strategy. The difference of completion times between simulation and real system has been expected due to some simplification in the model e.g. neglecting of times taken by stopper for reaction. This issue however can be improved.
From the presented approach, possibilities for expansion and improvement can be seen either at the organisation aspect or technical aspect. Further initiative
has been taken in integrating the approach with others aspect of planning e.g. scheduling of production orders. Some further work also has been planned in including resources such as industrial robot in the approach. The idea is to use material flow simulation software not only for controlling the material flow system but it wills also managing the utilisation of their connected resources.
Acknowledgements
We extend our sincere thanks to Universiti Teknikal Malaysia Melaka (UTeM) and Kementerian Pengajian Tinggi Malaysia (MoHE) for their funding. We also prolong our thanks to who has contributed in preparing this paper.
References
[1] Dangelmaier, W., Mahajan, K.R., Aufenanger, M., Seeger, T.,
2007. Simulation Assisted Match-up Rescheduling of Flexible Production Systems Subject to Execution Exceptions, Proceedings of the 2007 Winter Simulation Conference, p. 1805.
[2] Wilke, M., 2006. Wandelbare automatisierte Materialflusssysteme
für dynamische Produktionsstrukturen (Changeable automated material flow systems for dynamic production structures), Dissertation, Technische Universität München.
[3] Wiendahl, H.P., ElMaraghy H.A., Nyhuis, P., Zäh, M.F.,
Wiendahl, H.H., Duffie, D., Brieke, M., 2007. Changeable Manufacturing — Classification, Design and Operation. In: Annals of the CIRP — Manufacturing Technology, Vol. 56/2, p.783-809.
[4] Wiendahl, H.-P., 2005. Die wandlungsfähige Fabrik: Konzept und
Beispiel (The changeable factory: concept and example), K. H. Engel (Hrsg.) Betriebsleiter, WEKA MEDIA, Kissing.
[5] Nyhuis, P., ElMaraghy, H., Azab, A., Pachow-Frauenhofer, J.,
2011. A Control-based Modeling Approach to Changeable Manufacturing, Proceedings of 44th CIRP Conference on Manufacturing Systems, Madison, Wisconsin, USA.
[6] Hernández Morales, R., 2003. Systematik der
Wandlungsfähigkeit in der Fabrikplanung (Systematic of changeability in factory planning). Dissertation, Leibniz Universität Hannover.
[7] Wilke, M., 2008. Control concept for autonomous changeable
material flow systems, Logistics Journal.
[8] Dullinger, K.H., 1999. Das Zauberwort heißt Flexibilität - Wie
sich Logistikanlagen von vornherein flexible für zukünftige Anforderungen anlagen lassen, Materialfluss 30, p. 15-18.
[9] DIN19233, 1998. Prozeßautomatisierung — Automatisierung mit
Prozeßrechensystemen, Beuth Verlag GmbH, Berlin.
[10] VDI 3961, 1989. Planung der Materialflußsteuerung in
Fertigungsbetrieben (Operating unit for material flow in plants), Beuth Verlag GmbH, Berlin.
[11] Lewis, R., 2008. Modelling Control Systems Using IEC 61499,
The Institution of Engineering and Technology, London, United Kingdom.
[12] Brennan, R.W., Vrba, P., Tichy, P., Zoitl, A., Sünder, C., Strasser,
T., Marik, V., 2008. Development in dynamic and intelligent reconfiguration of industrial automation, Computers in Industry Journal.
[13] Pritschow, G., Kircher, C., Kremer, M., et al., 2006. Control
Systems for RMS and Methods of their Re-configuration, In: A. I. Dashchenko (ed.), Reconfigurable Manufacturing Systems and Transformable Factories, Berlin/Heidelberg, Springer Verlag: 195-212.
[14] Landers, R. G., Ruan, J., Liou, F., 2006. Reconfigurable
Manufacturing Equipment, In: A. I. Dashchenko (ed.),
Reconfigurable Manufacturing Systems and Transformable Factories, Berlin/Heidelberg, Springer Verlag: 79-110.
[15] Brennan, R.W., Zhang, X., Xu, Y., Norrie, D.H., 2002. A
reconfigurable concurrent function block model and its implementation in real-time Java, Journal of Integrated Computer-Aided Engineering 9 (3) p.263—279.
[16] Zhang, X., Brennan, R.W., Xu, Y., Norrie, D.H., 2001. Runtime
adaptability of a concurrent function block model for a real-time holonic controller, Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, pp. 16^—168.
[17] PROFACTOR, 2006. Micro holons for next generation
distributed embedded automation and control and evolution control environment for distributed automation components, PROFACTOR Research Report.
[18] VDI 3633, 2007. Simulation of systems in materials handling,
logistics and production: Machine-oriented simulation, Beuth Verlag GmbH, Berlin.
[19] VDI 3633, 2007. Simulation of systems in materials handling,
logistics and production: Machine-oriented simulation, Beuth Verlag GmbH, Berlin.
[20] Ling, Z., Chen, W., Yu, J., 2004, Research and implementation of
OPC server based on data access specification, WCICA 2004 -Fifth World Congress on Intelligent Control and Automation, Conference Proceedings, Hangzhou, China.
[21] Hoffmann, P., Maksoud, T.M.A., Schumann, R., Premier, G.C.,
2010. Virtual Commissioning of Manufacturing Systems - A Review and New Approaches for Simplification, Proceedings 24th European Conference on Modelling and Simulation, Malaysia.
[22] Kim, H.-J., Kernbaum, S., Seliger, G., 2009. Emulation-based
control of a disassembly system for LCD monitors, International Journal Manufacturing Technology, Springer Verlag, Nr. 40, p. 383-392.
[23] Wünsch, G., 2007. Methoden für die virtuelle Inbetriebnahme
automatisierter Produktionssysteme, Dissertation Technische Universität München.
[24] Reinhart, G, Wünsch, G., 2007. Economic application of virtual
commissioning to mechatronic production systems, Production Engineering, Vol. 1, Nr. 4, p. 371-379.
[25] Internationaler Fachkongreß Digitale Fabrik, 2010-11, Fulda,
Germany.
[26] Abdul Rahman, A.A., Seliger, G., 2011. Simulation as a tool in
Self Adaptive Control for Flexible Assembly Systems, Proceedings of 44th CIRP Conference on Manufacturing Systems, Madison, Wisconsin, USA.
