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Virtual Commissioning of Modular Automation Systems
Virtual Commissioning of Modular Automation Systems Atul Jain*, D A Vera** and R Harrison*** * Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, UK (Tel: +44 (0) 1509 227 566; e-mail: [email protected]) ** Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, UK (e-mail: [email protected]) ***Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, UK (e-mail: [email protected]) Abstract: A new concept in modular automation systems and their virtual engineering is presented. The new approach is aimed to provide support for the reconfigurable manufacturing systems (RMS) approach to automation. Manufacturing industries are moving to the concept of RMS to fulfill the great demand to deliver greater levels of product customisation at a higher quality and at reduced cost. RMS allows the manufacturing companies to quickly implement innovative design changes in their products and produce a greater number of product variants on leaner production lines with shorter times to market. The aim of this research is to enable production machines to be more easily and cost effectively built and subsequently reconfigurable through the adoption of a component-based approach to their implementation utilising virtual manufacturing tools. It is proposed that through the decomposition of manufacturing machines into standardized mechanisms and their associated data structures, a RMS can be designed and implemented. Research conducted at Loughborough University into the development of concepts and tools to support the global engineering of component-based reconfigurable manufacturing automation systems in the automotive production domain is described in this paper. A collaborative framework to integrate and coordinate the various engineering activities of globally distributed engineering teams involved in the design, implementation, operation and diagnosis of production machinery is also described. Keywords: Product Lifecycle Management, Automation, Lifecycle, Modular, Reconfigurable, Component-Based Reconfiguration, Virtual Prototyping. 1.
INTRODUCTION
Today manufacturing industries most typically deliver technologically advanced high-quality product ranges which have a short life-cycle due to rapid changes in market demand, competition and environmental requirements (Hammer and Champy, 1993). Therefore, there is an ongoing need within manufacturing industries to reorganise and redeploy resources, including people, software and machines. Moreover, this also makes product customisation a principal theme of the modern manufacturing paradigm (Ralston and Munton, 1987). The need to manufacture products and production facilities rapidly and cost-effectively has led to the concept of agile manufacturing (Gunasekaran and Yusuf, 2002; Lee et al., 2005). Agility also emphasizes the reuse of knowledge and core competencies to reduce cost and improve efficiency (Gunasekaran, 1998). In addition to agile manufacturing, modern constructs in mechatronic engineering such as Flexible Manufacturing Systems (FMS) and Reconfigurable Manufacturing Systems (RMS) along with manufacturing methodologies such as ComputerIntegrated Manufacturing (CIM), Concurrent Engineering (CE) and Virtual Enterprises (VE) are all geared to enabling greater system agility. However the success of these methods depends on a shift in the manufacturing culture, greater integration of computer software to manage flexibility and a
common model for representation and visualisation of machines for process engineers. Traditional manufacturing automation systems are often implemented in rigid hierarchical structures. Specification, development, implementation, and validation of these automation systems are complex and time-consuming tasks that rely heavily on the knowledge and the past experience of the engineering teams involved. This demands a high level of concurrent engineering between product designers and manufacturing system developers which make projects increasingly difficult to manage owing to continuous and unexpected changes that can occur during the project duration especially in the case of automotive industry. Typical engineering processes of a traditional manufacturing automation system are shown in Fig. 1. To overcome challenges of traditional manufacturing systems, machine manufacturers have recently investigated the potential of new machine design, machine architectures, and technologies (e.g., modular machines, distributed control hardware/reconfigurable control software) to adapt (i.e., reconfigure, reuse) production systems effectively, in terms of project time and cost, to new or changing requirements (ElMaraghy, 2005; Mehrabi et al., 2000). Hence, there is a growing need to have reconfigurable, modular and reusable systems in the manufacturing industry.
Fig. 1. Current Engineering Process of a Traditional Manufacturing System. Virtual engineering and commissioning tools aim to provide engineers with a digital environment in which design, process, and control engineering can be integrated into a three dimensional (3D) dynamic model of the to-be system before its physical implementation is carried out. Running systems in a virtual form allows system engineers to conduct what-if scenarios, test various design and system configurations and anticipate design problems and errors. However, existing virtual engineering tools are generic in their architecture and implementation and are not adapted to the prototyping of what is commonly referred to as RMS (Harrison et al., 2001; Mehrabi et al., 2002). The objective of this paper is to provide a background on virtual commissioning and modular approaches to manufacturing. The scope of this paper also extends to describe key commercial existing virtual commissioning tools along with the virtual commissioning tools developed at Loughborough University. 2.
VIRTUAL COMMISSIONING
The general tendency of shorter product life cycles makes the production ramp-up an important factor for a product’s economic success. The ramp-up phase starts with the complete assembly of a production system and ends with reaching full target quality at a specified cost and a specified output rate. It can be divided into the commissioning and runup phases. The process of designing and commissioning large automated systems is complex and requires the involvement of several engineers with different domains of expertise such as mechanical design, process, and control engineering. Typically, as witnessed in the automotive industry, the design of such system starts with the overall production-line layout which is derived from the manufacturing process requirement, followed by the mechanical design and finally the implementation of the control software, with activities being conducted with a variable level of concurrency. During commissioning all activities aim at putting a completely assembled and mechanically reviewed production system into operation. It ends with the production of the first work pieces that meet the specifications and the acceptance by the customer. The run-up phase follows the commissioning and transfers the operational production system into stable production conditions in compliance with target cost, demanded quality and output. Issues arise during the actual
commissioning phases when unseen design errors translate into non-working machines, unmatched functionality and sometimes catastrophic failure (e.g., collisions between actuators/products). Such unforeseen events are due to the fact that prior commissioning engineers do not have any means to check the consistency of mechanical, process and control system designs altogether. According to an analysis of critical points of the ramp-up process of a final assembly, Eversheim et al. (1990) determine control system malfunctions to be a major source of time delay. The particular reasons mostly lie within untested and newly developed control systems, new communication technology and the lack of adequate monitoring and diagnosis systems. However, according to Glas (1993), control software engineering is responsible for over 50% of the functionality of highly automated production equipment. An investigation for the German Association of Machine Tool Builders showed that the commissioning phase of a production system accounts for up to a quarter of the total project cycle time (VDW-Bericht, 1997). A remarkable 90% of the commissioning time is used for delays and activities related to electric and control devices. Again, 70% of this time delay was associated with errors in control software. In other words, the correction of defective control software consumes up to 60% of commissioning time or 15% of time-todelivery. The current trend in the manufacturing engineering domain is to encourage the deployment of IT tools to support what is referred to as Virtual Commissioning (VC), Virtual Engineering (VE) or Virtual Prototyping of manufacturing systems. The above highlighted problem which involves commissioning problems, complexity of production systems and growing importance of a short ramp-up phase can be addresses by virtual commissioning. Virtual commissioning is the emulation of a real sys.tem for logic validation. This involves replicating the behaviour of one or more pieces of hardware with a software environment. The main application of the virtual commissioning is to validate the code on an actual Programmable Logic Controller (PLC) and HumanMachine Interface (HMI) that will be on the shop floor, weeks or months before the integration of all the devices from tooling, robots, clamps, safety devices, electrical, hydraulics and pneumatics on the shop floor occur. This improves the quality, avoids any unforeseen event and enables a seamless transition from the virtual to physical, thus saves the actual ramp up time, as shown in Fig. 2. Without VC
Real Commissioning Assembly
With VC
Activity feasible using simulation
Assembly
Modeling
Virtual Commissioning
Real Commissioning
Time Gain of time
Fig. 2. Virtual Commissioning Procedure
Virtual commissioning tools make use of 3D modelling technologies to provide an intuitive model which characteristics (spatial and behavioural) results from the integration of various types of engineering data. The number, type and format of the data that can be integrated will depend on the particular tools being used, although, generally, three main data types are targeted, which are product design, manufacturing resources and processes related engineering data. 3.
MODULAR AUTOMATION SYSTEMS
In order to realise a modular reconfigurable automation system with the desired capabilities, it is vitally important to be able, reliably and repeatably, to construct and compose distributed embedded systems that can meet and adapt readily to ever changing user application requirements. Such systems need to be generally applicable to a broad spectrum of application domains and yet be capable of easy and precise tailoring to specific applications. The objective is not only to support application design, simulation and monitoring of real-time distributed automation components from the control perspective (control dimension) but also to support the integration of these devices with higher-level business process systems (enterprise dimension), with supply chain partners (value/supply-chain dimension) and within a lifecycle engineering context (lifecycle dimension). Fig. 3 presents an example of a modular automation system composed of distributed mechatronic components and highlights its support and integration needs. Work on the research requirements for an engineering environment to support this four dimensional approach is the subject of ongoing research at Loughborough University (Harrison et al., 2004). The potential benefits of modularity include: Economies of scale, increased feasibility of product/components change, increased product variety, reduced lead time, decoupling tasks and the ease of product upgrade, maintenance, repair, and disposal.
incorporate; system, software, control, machine and process factors into standard modules (Mehrabi et al., 2000). The Manufacturing System Integration (MSI) Research Institute at Loughborough University has been involved in an Engineering and Physical Sciences Research Council (EPSRC) funded research programme with a major automotive producer (Ford Motor Company Ltd.), production machine builders (ThyssenKrupp Krause Ltd.) and secondtier component suppliers in investigating appropriate integration structures that could support the distributed engineering of automotive production machinery (Harrison et al., 2004). A common data model to support the design and implementation of automation from the machine design and process specification stage to the build, installation, commissioning and operational stages is core to the research and development. Instead of independent engineering teams directly communicating process specifications information with each other, the common data model allows the teams to determine the specific information they are interested in from a common representation, as shown in Fig. 4. The data can be presented in different views or formats according to perspectives required or areas of concern of each user. Based on the roles of the users of the model, they are selectively allowed to add or modify specific parts of the common model.
Fig. 4. Reconfigurable Single Common Model Approach to Machine Development
Fig. 3. Concept of a Modular Automation System Modularity aims to identify independent, standardised, or interchangeable units to satisfy a variety of functions (Kusiak and Chun-Che, 1996). The term “modularity” indicates a high degree of independence among individual elements, excellent general usability, and seamless interfacing between elements. Separate element groups can be assembled into a hierarchical system, and the system can also be decomposed into the original element groups (Bi and Zhang, 2001). The challenge in developing reconfigurable systems is to
The work conducted between Ford Motor Company engineers and the Loughborough team looked at the development of mechanism taxonomy for the decomposition of manufacturing machines/facilities into reconfigurable modular elements. The approach adopted was to identify reusable, configurable components, the aim being to mask complexity, maximize reuse and build domain-specific libraries of configurable components and associated services, minimising the need for new custom components for each new application. It is recognised that decomposing complete automotive production facilities into mechanisms has differing levels of granularity which related to the level of involvement in the manufacturing lifecycle. The finest granularity exists at the machine tool builder where machines may be decomposed
into individual components; at the resource level decomposition is courser with mechanism being collections of components that provide a function. The principal focus of the study looked at common machine mechanisms on the engine lines. To support the activities of engineering partners, largely ad hoc integration methods and mechanisms are currently employed (Harrison et al., 2001). A mechanism can be either physical (i.e. actuators, lifts, clamps), logical (i.e. RF data readers, position interrogation sensors) or structural (i.e. conveyance, fixturing) Mechanism decomposition can be viewed from three levels, firstly functionally: what is the physical operation to be performed by the mechanism? (i.e., translation, join, test etc), secondly process: what process steps does the mechanism perform to achieve the function? (i.e. grasp, rotate 180 , etc) and finally detail: considers mechanisms at a low level looking at the control logic, geometric, hydraulic, pneumatic, electrical requirements that combine to fulfil the mechanism function. An example of decomposition of a machine tool used at Ford into component library is shown in Fig. 5.
machine tools, industrial robots, and inspection machines. Examples of state-of-the-art digital manufacturing solutions include commercial software such as Delmia Dassault Systems Quest/IGrip (Delmia, 2010), and Siemens Tecnomatix products (Siemens Tecnomatix, 2010). Both companies provide software modules that can be used to model various types of mechatronic system commonly deployed in the manufacturing industry, typically numerical control (NC) machining systems, industrial multiaxis robots, coordinate measurement machines, and PLC systems. A number of academic projects have also investigated the development and use of simulation environments for NC machine programming and machining process simulation (Dietrich et al., 2002; Min et al., 2002; Suh et al., 2003). The existing virtual engineering tools are designed as highly specialized tools adapted to the modelling of specific systems. Functionality is focused on the simulation of FMS control layout owing to the invariant nature of the FMS mechanical layout. There is a lack of and a need for virtual engineering tools that can effectively support the engineering life cycle of RMS (Mehrabi et al., 2002; Mersinger and Westkämper, 2002), such as customized machining stations and assembly and transfer lines (Harrison et al., 2001). A part of ongoing research at Loughborough University is designing virtual engineering tools that allow virtual models of RMS to be easily and readily implemented and used for prototyping purposes. The COMPAG/COMPANION (COMponent-based Paradigm for AGile automation, and COmmon Model for PArtNers in automatION) projects funded by the Engineering and Physical Science Research Council (EPSRC) and conducted at Loughborough University in the UK (Harrison et al., 2001) provide an example of how a so-called single common component-based (CB) approach, as shown in Fig. 4, can be applied to the design of distributed machine control systems.
Fig. 5. Decomposition of a Machine into Components 4.
VIRTUAL COMMISSIONING TOOLS
As the use of reconfigurable or flexible manufacturing systems is considered a key factor for the future success of manufacturing it is important that digital manufacturing tools are likewise reconfigurable. The use of a product lifecycle management (PLM) tool to facilitate digital manufacturing and process development offers many benefits (1) both the real and digital realities work with the same data structures, such as a Bill of Process (BoP), (2) encourages collaboration between engineering departments at earlier development phases, (3) reuse of existing data and a tool to drive standardisation, (4) PLM is a mechanism for enabling the use of best practice information. PLM technology delivers high data consistency and transparency, which assists process and product quality (Abramovici and Sieg, 2002). Currently available virtual engineering software tools are highly specific and adapted to ‘well-established’ (Zhao, 1998) types of flexible manufacturing system (FMS) such as
The CC (Core Component) virtual engineering and commissioning tool set is being currently developed at Loughborough University UK. The CC tools development was initiated based on the requirements of the automotive industry in terms of large-scale complex automation system design and commissioning. The CC tools provide User Interfaces (UI) and functions dedicated to the design of automation systems’ control layout as well as a lightweight 3D virtual environment that can be linked to a real-time control simulation engine in order to visualise, test, debug and validate the system behaviour in a virtual form. A set of system representations (state transition, sequence interlock, timing/Gantt chart diagrams) provide a variety of specialist and non-specialist views that were designed to support both detailed engineering tasks and general collaboration between project partners and engineers from different domains. The CC tools functions are developed on top of an open, webbased IT architecture that allows the CC tools functionalities to be expanded and linked to external software environments and amongst potentially distributed project partners. Unlike other commercially available virtual engineering solutions, the CC tool set relies on generic, open data formats for both control and modelling data, which further increase its integration capabilities with other engineering tools. The
whole CC tools development is driven by the concepts of a “component-based” system architecture which seeks to enable re-usability and re-configurability of basic modelling constructs, as shown in Fig. 6. The concept of “Component”, which is defined as a re-usable, re-configurable data block providing the data integration mechanisms for control, 3D modelling, kinematics, and any other data types describing a particular resource, is central to the CC Tools development. Re-Usability R e- C on f ig ura bil it y Com posability
from ergonomic analysis to real time machine simulation. Commercially available virtual engineering tools have a limitation that they offer point solutions that while being of high value only impact on specific technical challenges and fail to offer global business benefits and do not directly support the concept of RMS.
Data Repository Data M apping Mechanical Data
Modelling Data
Control Data
Component Web Servi ces Process Data
BOP Data 1,*
(s ub ) System
Com ponent Based sy stem Hierarc hy m odel
1,*
Co mp o nent
Fig. 6. Component Based Architecture of CC Tools Using the CC tools, the overall control design and simulation model editing workflow is broken down into two main tasks which are (i) the “component (library) editing” and (ii) “system editing”, as shown in Fig. 7. The component editing task consists in building up a library of reusable system components which encapsulate both modelling (3D geometry, kinematics data) and control data (as a set of state, possible state transitions and associated conditions). An overview of the CC tools user interface is shown in Fig. 8. Com ponents Sy stem
Fig. 7. Example of Component and System Editing 5.
CONCLUSION
A key factor in the success of automotive manufacturers is linked (i) to their capacity to deliver greater product customisation through more flexible and reconfigurable manufacturing machines, (ii) to adapt and test existing facilities whilst they continue to make the current product, (iii) to quickly change to the new configuration and to launch with robustness and speed, and (iv) to delivering increased flexibility while maintaining reliability, quality and volume. To fulfil the above requirements there is a high level of demand for virtual engineering or digital manufacturing by manufacturing engineers to solve a wide range of problems
Fig. 8. Overview of the CC tools User Interfaces (left: component library, center: 3D view, right:TreeStructure, Fore: Simulation run timing chart) A component-based virtual engineering and commissioning modeling approach to the implementation of automation systems has been discussed and presented. The main advantage of this approach is that (i) the modelled components can be reused and reconfigured in order to investigate the relative performance of various machine configurations, (ii) virtual machine prototypes are highly portable as the designed CC tools uses virtual reality modelling language (VRML) and Java Script for VRML, (iii) the complete machine model can be viewed, simulated and analysed using only a VRML compliant web browser. Other methods like, AutomationML where also investigated along with VRML to be used for the virtual engineering tool. AutomationML is a data exchange format used to facilitate exchange of engineering data between different partners and across different phases of a project lifecycle, whereas VRML along with the above capabilities also offers real-time capabilities which can be implemented using Java script programming. To meet the needs of agile manufacturing, collaborative automation systems are needed capable of supporting not only real-time control requirements but also the business needs, supply-chain integration requirements and lifecycle support needs of each application. This paper has focused on the provision of a framework for the distributed and reconfigurable engineering support of the automation system lifecycle for use by the supply chain partners, e.g., end users and machine-builders. As part of this research, an engineering workflow is designed to support the modular approach and its integration into engineering processes of our partners (car manufacturer and OEM) is under trial. An approach was also proposed to specification and implementation of a virtual prototyping environment intended to support the design of reconfigurable manufacturing systems.
6.
ACKNOWLEDGMENT
The authors gratefully acknowledge the support of the EPSRC and out industrial collaborators through the IMCRC Business Driver Automation (BDA) project in carrying out this research. REFERENCES Abramovici, M., Sieg, O., (2002). Status and development trends of product life cycle management systems. Proceedings of IPPD 2002, Wroclaw, Poland (2002). Bi, Z. M., Zhang, W. J., (2001). Modularity technology in manufacturing: taxonomy and issues. The International Journal of Advanced Manufacturing Technology 18 (5), 381-390. Delmia (2010). Delmia 3D digital manufacturing solution, Dassault Systems [Online]. Available from: http://www.delmia.com [Accessed: 01 March 2010] Dietrich, U., Schulz, T., Yaramanoglu, N., (2002). Bringing real and virtual worlds together in the manufacturing process. Journal of Advanced Manufacturing Systems 1 (1), 51-65. ElMaraghy, H., (2005). Flexible and reconfigurable manufacturing systems paradigms. International Journal of Flexible Manufacturing Systems 17 (4), 261-276. Eversheim, W., Koerth, D., Gentzcke, J. (1990). Gesellschaft Produktionstechnik: Inbetriebnahme komplexer Maschinen und Anlagen: Strategien und Praxisbeispiele zur Rationalisierung in der Einzelund Kleinserienproduktion. Du¨sseldorf: VDI-Verl. Glas, J., (1993). Standardisierter Aufbau anwendungsspezifischer Zellenrechnersoftware. MIT 80 Abb. Berlin [u.a.] Springer (iwb-Forschungsberichte; 61). Gunasekaran, A., (1998). Agile manufacturing: enablers and an implementation framework. International Journal of Production Research 36 (5), 1223 - 1247. Gunasekaran, A., Yusuf, Y. Y., (2002). Agile manufacturing: a taxonomy of strategic and technological imperatives. International Journal of Production Research 40 (6), 1357 - 1385. Hammer, M., Champy, J. (1993). Reengineering the Corporation, New York: Harper Business. Harrison, R., Lee, S. M., West, A. A. (2004). Lifecycle Engineering of Modular Automated Machines. 2nd IEEE International Conference on Industrial Informatics (INDIN'04). Berlin, Germany. Harrison, R., West, A., Weston, R., Monfared, R., (2001). Distributed engineering of manufacturing machines. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 215 (2), 217-231. Kusiak, A., Chun-Che, H., (1996). Development of Modular Products. IEEE Transactions on Component, Packing and Manufacturing Technology Part A, 19 (4), 523-538. Lee, S., Harrison, R., West, A., (2005). A component-based control system for agile manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 219 (1), 123-135.
Mehrabi, M. G., Ulsoy, A. G., Koren, Y., (2000). Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing 11 (4), 403-419. Mehrabi, M. G., Ulsoy, A. G., Koren, Y., Heytler, P., (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intelligent Manufacturing 13 (2), 135-146. Mersinger, M., Westkämper, E., (2002). Virtual reality for supporting the configuration of transformable assembly systems. Journal of Advanced Manufacturing Systems 1 (1), 107-112. Min, B. K., Huang, Z., Pasek, Z. J., Yip-Hoi, D., Husted, F., Marker, S., (2002). Integration of real-time control simulation to a virtual manufacturing environment. Journal of Advanced Manufacturing Systems 1 (1), 6787. Ralston, D., Munton, T., (1987). Computer Integrated Manufacturing. . Computer-Aided Engineering Journal August, 167-174. Suh, S.-H., Seo, Y., Lee, S.-M., Choi, T.-H., Jeong, G.-S., Kim, D.-Y., (2003). Modelling and Implementation of Internet-Based Virtual Machine Tools. The International Journal of Advanced Manufacturing Technology 21 (7), 516-522. Siemens Tecnomatix. (2010). Siemens digital manufacturing solutions, Tecnomatix [Online]. Available from: http://www.plm.automation.siemens.com/en_us/products /tecnomatix/ [Accessed: 01 March 2010]. VDW-Bericht, N., (1997). Abteilungsu¨bergreifende Projektierung komplexer Maschinen und Anlagen. Aachen: Verein Deutscher Werkzeugmaschinenhersteller. Zhao, Z., (1998). A variant approach to constructing and managing virtual manufacturing environments. International Journal of Computer Integrated Manufacturing 11, 485-499.
INTRODUCTION
Today manufacturing industries most typically deliver technologically advanced high-quality product ranges which have a short life-cycle due to rapid changes in market demand, competition and environmental requirements (Hammer and Champy, 1993). Therefore, there is an ongoing need within manufacturing industries to reorganise and redeploy resources, including people, software and machines. Moreover, this also makes product customisation a principal theme of the modern manufacturing paradigm (Ralston and Munton, 1987). The need to manufacture products and production facilities rapidly and cost-effectively has led to the concept of agile manufacturing (Gunasekaran and Yusuf, 2002; Lee et al., 2005). Agility also emphasizes the reuse of knowledge and core competencies to reduce cost and improve efficiency (Gunasekaran, 1998). In addition to agile manufacturing, modern constructs in mechatronic engineering such as Flexible Manufacturing Systems (FMS) and Reconfigurable Manufacturing Systems (RMS) along with manufacturing methodologies such as ComputerIntegrated Manufacturing (CIM), Concurrent Engineering (CE) and Virtual Enterprises (VE) are all geared to enabling greater system agility. However the success of these methods depends on a shift in the manufacturing culture, greater integration of computer software to manage flexibility and a
common model for representation and visualisation of machines for process engineers. Traditional manufacturing automation systems are often implemented in rigid hierarchical structures. Specification, development, implementation, and validation of these automation systems are complex and time-consuming tasks that rely heavily on the knowledge and the past experience of the engineering teams involved. This demands a high level of concurrent engineering between product designers and manufacturing system developers which make projects increasingly difficult to manage owing to continuous and unexpected changes that can occur during the project duration especially in the case of automotive industry. Typical engineering processes of a traditional manufacturing automation system are shown in Fig. 1. To overcome challenges of traditional manufacturing systems, machine manufacturers have recently investigated the potential of new machine design, machine architectures, and technologies (e.g., modular machines, distributed control hardware/reconfigurable control software) to adapt (i.e., reconfigure, reuse) production systems effectively, in terms of project time and cost, to new or changing requirements (ElMaraghy, 2005; Mehrabi et al., 2000). Hence, there is a growing need to have reconfigurable, modular and reusable systems in the manufacturing industry.
Fig. 1. Current Engineering Process of a Traditional Manufacturing System. Virtual engineering and commissioning tools aim to provide engineers with a digital environment in which design, process, and control engineering can be integrated into a three dimensional (3D) dynamic model of the to-be system before its physical implementation is carried out. Running systems in a virtual form allows system engineers to conduct what-if scenarios, test various design and system configurations and anticipate design problems and errors. However, existing virtual engineering tools are generic in their architecture and implementation and are not adapted to the prototyping of what is commonly referred to as RMS (Harrison et al., 2001; Mehrabi et al., 2002). The objective of this paper is to provide a background on virtual commissioning and modular approaches to manufacturing. The scope of this paper also extends to describe key commercial existing virtual commissioning tools along with the virtual commissioning tools developed at Loughborough University. 2.
VIRTUAL COMMISSIONING
The general tendency of shorter product life cycles makes the production ramp-up an important factor for a product’s economic success. The ramp-up phase starts with the complete assembly of a production system and ends with reaching full target quality at a specified cost and a specified output rate. It can be divided into the commissioning and runup phases. The process of designing and commissioning large automated systems is complex and requires the involvement of several engineers with different domains of expertise such as mechanical design, process, and control engineering. Typically, as witnessed in the automotive industry, the design of such system starts with the overall production-line layout which is derived from the manufacturing process requirement, followed by the mechanical design and finally the implementation of the control software, with activities being conducted with a variable level of concurrency. During commissioning all activities aim at putting a completely assembled and mechanically reviewed production system into operation. It ends with the production of the first work pieces that meet the specifications and the acceptance by the customer. The run-up phase follows the commissioning and transfers the operational production system into stable production conditions in compliance with target cost, demanded quality and output. Issues arise during the actual
commissioning phases when unseen design errors translate into non-working machines, unmatched functionality and sometimes catastrophic failure (e.g., collisions between actuators/products). Such unforeseen events are due to the fact that prior commissioning engineers do not have any means to check the consistency of mechanical, process and control system designs altogether. According to an analysis of critical points of the ramp-up process of a final assembly, Eversheim et al. (1990) determine control system malfunctions to be a major source of time delay. The particular reasons mostly lie within untested and newly developed control systems, new communication technology and the lack of adequate monitoring and diagnosis systems. However, according to Glas (1993), control software engineering is responsible for over 50% of the functionality of highly automated production equipment. An investigation for the German Association of Machine Tool Builders showed that the commissioning phase of a production system accounts for up to a quarter of the total project cycle time (VDW-Bericht, 1997). A remarkable 90% of the commissioning time is used for delays and activities related to electric and control devices. Again, 70% of this time delay was associated with errors in control software. In other words, the correction of defective control software consumes up to 60% of commissioning time or 15% of time-todelivery. The current trend in the manufacturing engineering domain is to encourage the deployment of IT tools to support what is referred to as Virtual Commissioning (VC), Virtual Engineering (VE) or Virtual Prototyping of manufacturing systems. The above highlighted problem which involves commissioning problems, complexity of production systems and growing importance of a short ramp-up phase can be addresses by virtual commissioning. Virtual commissioning is the emulation of a real sys.tem for logic validation. This involves replicating the behaviour of one or more pieces of hardware with a software environment. The main application of the virtual commissioning is to validate the code on an actual Programmable Logic Controller (PLC) and HumanMachine Interface (HMI) that will be on the shop floor, weeks or months before the integration of all the devices from tooling, robots, clamps, safety devices, electrical, hydraulics and pneumatics on the shop floor occur. This improves the quality, avoids any unforeseen event and enables a seamless transition from the virtual to physical, thus saves the actual ramp up time, as shown in Fig. 2. Without VC
Real Commissioning Assembly
With VC
Activity feasible using simulation
Assembly
Modeling
Virtual Commissioning
Real Commissioning
Time Gain of time
Fig. 2. Virtual Commissioning Procedure
Virtual commissioning tools make use of 3D modelling technologies to provide an intuitive model which characteristics (spatial and behavioural) results from the integration of various types of engineering data. The number, type and format of the data that can be integrated will depend on the particular tools being used, although, generally, three main data types are targeted, which are product design, manufacturing resources and processes related engineering data. 3.
MODULAR AUTOMATION SYSTEMS
In order to realise a modular reconfigurable automation system with the desired capabilities, it is vitally important to be able, reliably and repeatably, to construct and compose distributed embedded systems that can meet and adapt readily to ever changing user application requirements. Such systems need to be generally applicable to a broad spectrum of application domains and yet be capable of easy and precise tailoring to specific applications. The objective is not only to support application design, simulation and monitoring of real-time distributed automation components from the control perspective (control dimension) but also to support the integration of these devices with higher-level business process systems (enterprise dimension), with supply chain partners (value/supply-chain dimension) and within a lifecycle engineering context (lifecycle dimension). Fig. 3 presents an example of a modular automation system composed of distributed mechatronic components and highlights its support and integration needs. Work on the research requirements for an engineering environment to support this four dimensional approach is the subject of ongoing research at Loughborough University (Harrison et al., 2004). The potential benefits of modularity include: Economies of scale, increased feasibility of product/components change, increased product variety, reduced lead time, decoupling tasks and the ease of product upgrade, maintenance, repair, and disposal.
incorporate; system, software, control, machine and process factors into standard modules (Mehrabi et al., 2000). The Manufacturing System Integration (MSI) Research Institute at Loughborough University has been involved in an Engineering and Physical Sciences Research Council (EPSRC) funded research programme with a major automotive producer (Ford Motor Company Ltd.), production machine builders (ThyssenKrupp Krause Ltd.) and secondtier component suppliers in investigating appropriate integration structures that could support the distributed engineering of automotive production machinery (Harrison et al., 2004). A common data model to support the design and implementation of automation from the machine design and process specification stage to the build, installation, commissioning and operational stages is core to the research and development. Instead of independent engineering teams directly communicating process specifications information with each other, the common data model allows the teams to determine the specific information they are interested in from a common representation, as shown in Fig. 4. The data can be presented in different views or formats according to perspectives required or areas of concern of each user. Based on the roles of the users of the model, they are selectively allowed to add or modify specific parts of the common model.
Fig. 4. Reconfigurable Single Common Model Approach to Machine Development
Fig. 3. Concept of a Modular Automation System Modularity aims to identify independent, standardised, or interchangeable units to satisfy a variety of functions (Kusiak and Chun-Che, 1996). The term “modularity” indicates a high degree of independence among individual elements, excellent general usability, and seamless interfacing between elements. Separate element groups can be assembled into a hierarchical system, and the system can also be decomposed into the original element groups (Bi and Zhang, 2001). The challenge in developing reconfigurable systems is to
The work conducted between Ford Motor Company engineers and the Loughborough team looked at the development of mechanism taxonomy for the decomposition of manufacturing machines/facilities into reconfigurable modular elements. The approach adopted was to identify reusable, configurable components, the aim being to mask complexity, maximize reuse and build domain-specific libraries of configurable components and associated services, minimising the need for new custom components for each new application. It is recognised that decomposing complete automotive production facilities into mechanisms has differing levels of granularity which related to the level of involvement in the manufacturing lifecycle. The finest granularity exists at the machine tool builder where machines may be decomposed
into individual components; at the resource level decomposition is courser with mechanism being collections of components that provide a function. The principal focus of the study looked at common machine mechanisms on the engine lines. To support the activities of engineering partners, largely ad hoc integration methods and mechanisms are currently employed (Harrison et al., 2001). A mechanism can be either physical (i.e. actuators, lifts, clamps), logical (i.e. RF data readers, position interrogation sensors) or structural (i.e. conveyance, fixturing) Mechanism decomposition can be viewed from three levels, firstly functionally: what is the physical operation to be performed by the mechanism? (i.e., translation, join, test etc), secondly process: what process steps does the mechanism perform to achieve the function? (i.e. grasp, rotate 180 , etc) and finally detail: considers mechanisms at a low level looking at the control logic, geometric, hydraulic, pneumatic, electrical requirements that combine to fulfil the mechanism function. An example of decomposition of a machine tool used at Ford into component library is shown in Fig. 5.
machine tools, industrial robots, and inspection machines. Examples of state-of-the-art digital manufacturing solutions include commercial software such as Delmia Dassault Systems Quest/IGrip (Delmia, 2010), and Siemens Tecnomatix products (Siemens Tecnomatix, 2010). Both companies provide software modules that can be used to model various types of mechatronic system commonly deployed in the manufacturing industry, typically numerical control (NC) machining systems, industrial multiaxis robots, coordinate measurement machines, and PLC systems. A number of academic projects have also investigated the development and use of simulation environments for NC machine programming and machining process simulation (Dietrich et al., 2002; Min et al., 2002; Suh et al., 2003). The existing virtual engineering tools are designed as highly specialized tools adapted to the modelling of specific systems. Functionality is focused on the simulation of FMS control layout owing to the invariant nature of the FMS mechanical layout. There is a lack of and a need for virtual engineering tools that can effectively support the engineering life cycle of RMS (Mehrabi et al., 2002; Mersinger and Westkämper, 2002), such as customized machining stations and assembly and transfer lines (Harrison et al., 2001). A part of ongoing research at Loughborough University is designing virtual engineering tools that allow virtual models of RMS to be easily and readily implemented and used for prototyping purposes. The COMPAG/COMPANION (COMponent-based Paradigm for AGile automation, and COmmon Model for PArtNers in automatION) projects funded by the Engineering and Physical Science Research Council (EPSRC) and conducted at Loughborough University in the UK (Harrison et al., 2001) provide an example of how a so-called single common component-based (CB) approach, as shown in Fig. 4, can be applied to the design of distributed machine control systems.
Fig. 5. Decomposition of a Machine into Components 4.
VIRTUAL COMMISSIONING TOOLS
As the use of reconfigurable or flexible manufacturing systems is considered a key factor for the future success of manufacturing it is important that digital manufacturing tools are likewise reconfigurable. The use of a product lifecycle management (PLM) tool to facilitate digital manufacturing and process development offers many benefits (1) both the real and digital realities work with the same data structures, such as a Bill of Process (BoP), (2) encourages collaboration between engineering departments at earlier development phases, (3) reuse of existing data and a tool to drive standardisation, (4) PLM is a mechanism for enabling the use of best practice information. PLM technology delivers high data consistency and transparency, which assists process and product quality (Abramovici and Sieg, 2002). Currently available virtual engineering software tools are highly specific and adapted to ‘well-established’ (Zhao, 1998) types of flexible manufacturing system (FMS) such as
The CC (Core Component) virtual engineering and commissioning tool set is being currently developed at Loughborough University UK. The CC tools development was initiated based on the requirements of the automotive industry in terms of large-scale complex automation system design and commissioning. The CC tools provide User Interfaces (UI) and functions dedicated to the design of automation systems’ control layout as well as a lightweight 3D virtual environment that can be linked to a real-time control simulation engine in order to visualise, test, debug and validate the system behaviour in a virtual form. A set of system representations (state transition, sequence interlock, timing/Gantt chart diagrams) provide a variety of specialist and non-specialist views that were designed to support both detailed engineering tasks and general collaboration between project partners and engineers from different domains. The CC tools functions are developed on top of an open, webbased IT architecture that allows the CC tools functionalities to be expanded and linked to external software environments and amongst potentially distributed project partners. Unlike other commercially available virtual engineering solutions, the CC tool set relies on generic, open data formats for both control and modelling data, which further increase its integration capabilities with other engineering tools. The
whole CC tools development is driven by the concepts of a “component-based” system architecture which seeks to enable re-usability and re-configurability of basic modelling constructs, as shown in Fig. 6. The concept of “Component”, which is defined as a re-usable, re-configurable data block providing the data integration mechanisms for control, 3D modelling, kinematics, and any other data types describing a particular resource, is central to the CC Tools development. Re-Usability R e- C on f ig ura bil it y Com posability
from ergonomic analysis to real time machine simulation. Commercially available virtual engineering tools have a limitation that they offer point solutions that while being of high value only impact on specific technical challenges and fail to offer global business benefits and do not directly support the concept of RMS.
Data Repository Data M apping Mechanical Data
Modelling Data
Control Data
Component Web Servi ces Process Data
BOP Data 1,*
(s ub ) System
Com ponent Based sy stem Hierarc hy m odel
1,*
Co mp o nent
Fig. 6. Component Based Architecture of CC Tools Using the CC tools, the overall control design and simulation model editing workflow is broken down into two main tasks which are (i) the “component (library) editing” and (ii) “system editing”, as shown in Fig. 7. The component editing task consists in building up a library of reusable system components which encapsulate both modelling (3D geometry, kinematics data) and control data (as a set of state, possible state transitions and associated conditions). An overview of the CC tools user interface is shown in Fig. 8. Com ponents Sy stem
Fig. 7. Example of Component and System Editing 5.
CONCLUSION
A key factor in the success of automotive manufacturers is linked (i) to their capacity to deliver greater product customisation through more flexible and reconfigurable manufacturing machines, (ii) to adapt and test existing facilities whilst they continue to make the current product, (iii) to quickly change to the new configuration and to launch with robustness and speed, and (iv) to delivering increased flexibility while maintaining reliability, quality and volume. To fulfil the above requirements there is a high level of demand for virtual engineering or digital manufacturing by manufacturing engineers to solve a wide range of problems
Fig. 8. Overview of the CC tools User Interfaces (left: component library, center: 3D view, right:TreeStructure, Fore: Simulation run timing chart) A component-based virtual engineering and commissioning modeling approach to the implementation of automation systems has been discussed and presented. The main advantage of this approach is that (i) the modelled components can be reused and reconfigured in order to investigate the relative performance of various machine configurations, (ii) virtual machine prototypes are highly portable as the designed CC tools uses virtual reality modelling language (VRML) and Java Script for VRML, (iii) the complete machine model can be viewed, simulated and analysed using only a VRML compliant web browser. Other methods like, AutomationML where also investigated along with VRML to be used for the virtual engineering tool. AutomationML is a data exchange format used to facilitate exchange of engineering data between different partners and across different phases of a project lifecycle, whereas VRML along with the above capabilities also offers real-time capabilities which can be implemented using Java script programming. To meet the needs of agile manufacturing, collaborative automation systems are needed capable of supporting not only real-time control requirements but also the business needs, supply-chain integration requirements and lifecycle support needs of each application. This paper has focused on the provision of a framework for the distributed and reconfigurable engineering support of the automation system lifecycle for use by the supply chain partners, e.g., end users and machine-builders. As part of this research, an engineering workflow is designed to support the modular approach and its integration into engineering processes of our partners (car manufacturer and OEM) is under trial. An approach was also proposed to specification and implementation of a virtual prototyping environment intended to support the design of reconfigurable manufacturing systems.
6.
ACKNOWLEDGMENT
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