- Email: [email protected]
Modular control system with intelligent scheduling
Computers in Industry 36 Ž1998. 75–81
Modular control system with intelligent scheduling Ari Heikkila¨ ) , Heikki Koivo
1
Helsinki UniÕersity of Technology, Control Engineering Laboratory, Otakaari 5, FIN-02150 Espoo, Finland
Abstract A modular control system of a flexible manufacturing cell based on microcomputers and local area network is presented. Decentralisation and modularisation as architectural strengths of advanced Shop Floor Control-software ŽSFC. are emphasised. The system implementation with corresponding control modules is described. An expert routing and scheduling system using rule-based simulation is presented. The use of simulation module tightly connected to the manufacturing process to increase the system throughput is suggested. q 1998 Elsevier Science B.V. All rights reserved. Keywords: Manufacturing system control; Modularity; Decentralised control; Expert system scheduling
1. Introduction An automatic, flexible manufacturing cell with vision has been constructed to serve research, demonstration and education of FMC technology in the Control Engineering Laboratory of the HUT. First the cell was provided with event-based control algorithms w1,2x. Next a dynamic programming algorithm, which minimises the weighted idle times of the machining stations, was applied to reach an optimal control w3x. Because of the limited flexibility and the heavy computation needed for the optimisation research of alternative control, methods using expert system technology was studied w4,5x. Expert systems were also applied to detect faults in the operation of the system and its components, providing the system with automatic recoveries. The early control systems were based on centralised control using a dedicated cell controller. The
) 1
Corresponding author. E-mail: [email protected] E-mail: [email protected]
recent architecture of the control system is modular, decentralised via local area network and uses sophisticated features of modern PC-systems. The current research effort is mainly directed to the development of control algorithms and methods for flexible manufacturing cells and systems, the pilot-cell acting as a test bed for such methods. An essential part of the development is expert systems for routing and scheduling, which receive production tasks and control the system operation in an optimal way.
2. Modular control architecture Shop Floor Control ŽSFC. w6x is a software framework for developing and building control systems for factory automation. The development of software started in the late 1980s at the Technical Research Centre of Finland ŽVITrAutomation.. SFC provides a domain to build control software that is modular and decentralised via local area network. It runs on PCs and is available for OSr2 Warp and Windows NT operating systems.
0166-3615r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved. PII S 0 1 6 6 - 3 6 1 5 Ž 9 7 . 0 0 1 0 0 - 0
76
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
Some of the main advanced features are: Ø Modularity Ø Decentralisation based on LAN Ø Easiness to build and configure control applications Ø Universal user interface Ø Use of advanced features provided by OSr2 and NT The basic structure of SFC is called Virtual OperativerOrganisational Unit ŽVOU.. It is an extension of the Virtual Manufacturing Device ŽVMD. defined by Manufacturing Message Specification ŽMMS. of Manufacturing Automation Protocol ŽMAP.. Using VOU-structure operations can be grouped into operational units. VOU takes care of the communication between its modules, i.e., OSr2 or NT programs, and communication with other VOUs. The communication is initialised and determined with the help of parameters of specific connection lists. The VOU structure works as an object bus of the control system based on TCPrIP-communication protocol. Each PC in the network can have one or several VOU-units, each one taking care of, for example: Ø resource allocation Ø time-out control Ø event inventory Ø reporting functions Ø internal communication Ø communication between VOUs As an essential feature all the control functions and operations must first be described before they are interpreted and handled at the VOU-level. The descriptions include, among all the name of the service, the format of the service parameters, and the link to the physical server Ži.e., program name.. Using VOU-manageable description mechanisms the control system descriptions can be advantageously differentiated from the system implementation. The system becomes modular, when software modules can be collected and grouped into operational units. The system implementation is transparent to the user. The system can easily be changed, even in real-time, for instance according to the load in the computers or in the case of disturbances or system failures. SFC includes a simple, easy to configure, and universal graphical user interface. The operations describe to the user interface how they and their parameters will be represented. The user interface
module consists of a set of presentation agents, which make it possible to build and manage the user-interface without actual programming. There can be several independent user-interfaces, each of which can operate transparently over the network.
3. The HUT FMC implementation 3.1. Operation of the manufacturing system The flexible manufacturing cell Žsee Fig. 1.. consists of three working stations, a CNC milling station, drilling station and a measuring station. The raw material arrives in the cell randomly located on a conveyor belt, from which the parts are picked up and transferred to the input storage by an industrial robot. The flexibility of robot gripping is obtained with the help of a vision system developed in the laboratory w7x. The raw objects, picked from the input storage, go through several machining operations according to the process plan, after which the finished products are transferred to the output storage. The robot transferring material between the stations and storage is a common resource, the allocation of which becomes important concerning the system efficiency. The machining jobs take such a long time compared with the robot movements, so that the robot does not become a bottle neck of the system. The machining stations have been equipped with measuring instruments and switches for the fault diagnosis and error recovery, the drilling station being the best example of that. 3.2. The control software For demonstrations, the control system of the flexible manufacturing cell has been decentralised on two PCs, both running separately configured, dedicated user-interfaces. A third PC has been connected to the system to run an extra user interface of its own. Even though certain control modules are available for OSr2 only Žfor instance the expert scheduler., most of the operations can be run selectively on both OSr2 and NT environments inter-operably. For simplicity, a generally used one machine configuration of the control system is described more in
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
77
Fig. 1. The flexible manufacturing cell with vision.
detail. The system is implemented using seven VOUs, which take care of the following tasks. VOU 1 2 3 4 5 6 7
Operation milling station control drilling station control measuring station control robot control, storage inventoryrcontrol expert scheduler control supervisor graphical user interface
Some common features can be found in the control tasks of any kind of machining stations. There are several resources, such as tools, programs, raw materials, etc. which are needed to start the machining job. To complete the job, not only must certain control signals or commands be sent to the CNCcontrol unit, but also monitoring or even measuring is required to acquire necessary feedback from the
process. To build the interfaces to the machining stations require quite often complicated tailored solutions. A modular approach to the control is one way to minimise this unwanted and time consuming design and programming effort. In the laboratory FMS the controls of the two machining stations and measuring station were designed to have three control layers: work leÕel control, deÕice dependent station control, and signal leÕel monitoring. The work level control modules were developed to act as general servers, being identically the same for all stations. They take care of the preparations and arrangements for machining jobs Žprogram and tool management, etc.. by calling the station control services in uniform ways. They are also connected to the control modules of the material handling system Žrobot. and take care of the communication needed. The device control modules include the implementations of the device dependent control func-
78
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
tions. They take care of sending start and stop commands to the control units of the machining stations, manage the communication during program loading, etc. The signal monitoring layers, besides offering direct access to some measuring and control signals, monitor the system events and receive various acknowledgements from the control devices. The control of the material handling system is closely connected with the routing and scheduling system. In the case of the laboratory cell all material transfers are performed by the industrial robot. The scheduling system produces a task list for the robot to execute. The length of the list depends on the amount and feeding order of the parts to be manufactured, and naturally the way they are routed trough the system.
A module called control superÕisor receives the task lists of robot transfers and makes certain that they are executed in the requested order. It also supervises, that the manufacturing operations proceed correctly, and receives messages if failures and disturbances occur. The robot has a dedicated work control module analogous to the machining stations. It communicates with the control supervisor and receives tasks one by one from it. Much like the machining stations, the robot also has the device control and signal monitoring layers separated from the work server in order to reach more general and portable control implementation. Besides the services used under automatic operation, the graphical user-interface offers an access to
Fig. 2. A screen dump from the user interface of the FMS with the main window Žup left., and two subwindows describing the milling station Ždown left. and the drilling station Ždown right.. A command dialogue for the simulator is located left to the main window.
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
79
control all the devices of the system separately using the services obtained through special control fields of the interface. It also offers several functions to take care of the production control and order management. More detailed views of, for instance, the machining stations, can be achieved by calling the sub window of the target Žsee Fig. 2..
4. Expert system for routing and scheduling Simulation is a well-known method to widen the knowledge of complex flexible manufacturing systems under production disturbances and different control objectives. To provide a basis for gathering a knowledge base for intelligent scheduling of FMS, a rule-based simulation module for flexible manufacturing was first written in OPS83 w4,5x for DOS environment. The simulator was connected with the control system of the cell and it used real-time data of the manufacturing. The system developed was tested by means of various simulations and test runs with the pilot cell w3x. The system was rewritten and redesigned w8x in RAL ŽRule-Extended Algorithm Language. expert system tool w9x, while SFC-software and OSr2 were selected as development environment. Also, commercial discrete event simulators had been tested earlier. The need for higher flexibility and intelligence in routing, possibility to connect to real-time data, and easy integration to SFC were the main reasons to develop a dedicated simulator. The simulator can be used on-line during the manufacturing to test alternative control strategies. Also, if a new ruleset version works well with the simulator, it can be directly taken into real-time control also. The expert system module consists of operational units presented in Fig. 3. After the starting initialisations, the system reads the states of the manufacturing system and its components using communication services of SFC-software. Using the status data the system creates RAL working memory elements ŽWMEs. to describe the essential operational units of the manufacturing system. WMEs are also created to describe the system eÕents, such like machining jobs, robot transfers, etc. The event elements are provided with attributes describing the lengths of the various operations. These attributes are updated in the course
Fig. 3. The parts of the expert system.
of time and managed by the simulation clock. Some additional WMEs are also used to control the simulation and especially the routing and scheduling of the products. The routing and scheduling, which uses the simulation model of the system, are controlled by the scheduling rules. The rules are operationally grouped into rulesets, each of which having a context, a goal to work for. The rulesets can roughly be divided into two groups, one taking care of the simulation, the other containing the intelligence of the routing and scheduling. The simulation rulesets use the process data and simulate the operations of the manufacturing system with sufficient accuracy. The simulation produces information, how efficient the system was, in the form of flow times, utility ratios of the machining stations, etc. This information is used along with the simulation in scheduling. Some assumptions and limitations have been applied to the scheduling problem. The raw objects go through one or several machining stations where the corresponding machining jobs are performed. Not only the amount of the jobs but also their precedence can vary. The order of the phases can alternatively be fixed, partly fixed or can be selected quite freely. Each job can be done at least at one machining station, but the possibility of several available resources can exist. Each part can also access the same machining station more than once if needed. The lengths of the jobs in each machining station are also known beforehand. The previous information has
80
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
been coded as the manufacturing specification for each product type and it defines the possible routes the part can proceed in the system. Because there may be opposite material flows in the manufacturing system, intermediate storage is used to avoid deadlocks. Rules to detect and avoid different kinds of system deadlocks are used in the scheduling rulesets. The routing system receives a list of products to be manufactured in the system. It offers alternative routing methods to carry out the production task. It reports the efficiency of each method in form of flow times and machine utilisations to support the selection of the best way to carry out the production task. An event-based control, ruleset trying to maximise the utility ratio of certain bottle-neck station and ruleset minimising the flow time of a given product has been under development and testing Žsee Fig. 2.. Each control mentioned includes deadlock detection and management, as well as some recovery routines to handle production failures.
w3x
w4x
w5x
w6x
w7x
w8x
w9x
5. Conclusion This paper described a flexible manufacturing cell provided with a modular, decentralised control system based on microcomputers and local area network. A rule based simulator was tightly connected to the manufacturing system and integrated with routing and scheduling functions for production control. Not only is the pilot system used to demonstrate flexible manufacturing systems technology, but it was also used as a test bed when developing different control methods and algorithms to reach an optimal behaviour. The modularity of the system architecture makes it especially easy to use more than one scheduling modules in collaboration to solve scheduling problems.
References w1x A.J. Niemi, T. Rantala, R. Ylinen, E. Posti, H. Lehto, R. Kuoppala, Automatic laboratory scale production cell with vision, Proceedings of the 14th International Symposium on Industrial Robots, IFS Publ. and Mekan., Gothenburg, Sweden, 2–4 October 1984, pp. 183–188. w2x A.J. Niemi, R. Ylinen, A. Heikkila, ¨ Automatic prototype FMC
with vision, Proceedings of the 18th International Symposium on Industrial Robots, IFS Publ. and Springer, Lausanne, Switzerland, 26–28 April 1988, pp. 379–386. A.J. Niemi, A. Heikkila, ¨ R. Ylinen, T. Virtanen, Automatic FMC with vision as test bed for control methods, Int. J. Adv. Manufact. Technol. 7 Ž6. Ž1992. 353–359. A. Heikkila, ¨ A.J. Niemi, R. Ylinen, T. Virtanen, Rule based simulation and scheduling system for flexible manufacturing, Proceedings robotikdagar, Linkoping University of Technol¨ ogy, Linkoping, Sweden, 30–31 May 1991, 11 pp. ¨ A. Heikkila, ¨ A.J. Niemi, R. Ylinen, T. Virtanen, Expert scheduling system for flexible manufacturing, Problemi masinostrojenia i automatizatsii wEng Automat.x 4–5 Ž1992. 10–19. R. Majapuro, A. Hovi, A modular software architecture for manufacturing control, Proceedings of the 3rd IEEE Conference on Control Applications, Glasgow, UK, Vol. 3, 24–26 August 1994, pp. 1745–1750. A.J. Niemi, P. Malinen, K. Koskinen, Digitally implemented sensing and control functions for standard industrial robot, Proceedings of 7th International Symposium on Industrial Robots, JIRA, Tokyo, Japan, 19–21 October 1977, pp. 487– 495. A. Heikkila, ¨ T. Virtanen, R. Majapuro, Modular control system with expert scheduling for flexible manufacture, Proceedings of the 3rd IEEE Conference on Control Applications, Glasgow, UK, Vol. 3, 24–26 August 1994, pp. 1751–1755. RAL Language Guide, Production Systems Technologies, May 1991.
Ari Heikkila¨ was born in Kouvola, Finland. He received his MSc in Control Engineering and Automation in 1988 from the Electrical Engineering Department of Helsinki University of Technology ŽHUT.. Heikkila¨ has been working at the Control Engineering Laboratory of HUT in research projects specialising in control of flexible manufacturing, expert system control, fault diagnosis, and production control. He worked in 1995 as a visiting engineer at the Laboratory of Manufacturing and Productivity of Massachusetts Institute of Technology, and he is currently a research scientist at VTT Automation. Heikki N. Koivo was born in Vaasa, Finland. He received his BSEE degree in 1967 from Purdue University, West Lafayette, IN, and his MSc degree in electrical engineering and PhD degree in control sciences from the University of Minnesota, Minneapolis, MN, in 1969 and 1971, respectively. In 1971 he joined the Department of Electrical Engineering of the University of Toronto, Ontario, Canada as a Visiting Assistant Professor, and he served there as an Assistant Professor from 1972 to 1975. From 1975 to 1979, he was an Associate Professor in applied mathematics, and from 1979 to 1984 in control engineering at Tampere University of Technology, Tampere, Finland. From 1984 to 1995 he was
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81 Professor in Automation Technology. In 1986 he was the Chairman of the Department of Electrical Engineering. Since 1995 he has been Professor in Automation Technology at the Helsinki University of Technology. His research interests include adaptive and learning control including neural networks and fuzzy control, mechatronics and computer control of processes. He has authored about two hundred publications in these areas. Dr. Koivo is a
81
member of the Editorial Board of the Journal of Adaptive Control and Signal Processing, Journal of Intelligent and Fuzzy Systems and Intelligent Automation and Soft Computing. He is also Associate Editor of the IEEE Transactions on Robotics and Automation. He is a Fellow of The Finnish Academy of Technical Sciences. He is the Finnish representative in the Council of European Union Control Association.
Modular control system with intelligent scheduling Ari Heikkila¨ ) , Heikki Koivo
1
Helsinki UniÕersity of Technology, Control Engineering Laboratory, Otakaari 5, FIN-02150 Espoo, Finland
Abstract A modular control system of a flexible manufacturing cell based on microcomputers and local area network is presented. Decentralisation and modularisation as architectural strengths of advanced Shop Floor Control-software ŽSFC. are emphasised. The system implementation with corresponding control modules is described. An expert routing and scheduling system using rule-based simulation is presented. The use of simulation module tightly connected to the manufacturing process to increase the system throughput is suggested. q 1998 Elsevier Science B.V. All rights reserved. Keywords: Manufacturing system control; Modularity; Decentralised control; Expert system scheduling
1. Introduction An automatic, flexible manufacturing cell with vision has been constructed to serve research, demonstration and education of FMC technology in the Control Engineering Laboratory of the HUT. First the cell was provided with event-based control algorithms w1,2x. Next a dynamic programming algorithm, which minimises the weighted idle times of the machining stations, was applied to reach an optimal control w3x. Because of the limited flexibility and the heavy computation needed for the optimisation research of alternative control, methods using expert system technology was studied w4,5x. Expert systems were also applied to detect faults in the operation of the system and its components, providing the system with automatic recoveries. The early control systems were based on centralised control using a dedicated cell controller. The
) 1
Corresponding author. E-mail: [email protected] E-mail: [email protected]
recent architecture of the control system is modular, decentralised via local area network and uses sophisticated features of modern PC-systems. The current research effort is mainly directed to the development of control algorithms and methods for flexible manufacturing cells and systems, the pilot-cell acting as a test bed for such methods. An essential part of the development is expert systems for routing and scheduling, which receive production tasks and control the system operation in an optimal way.
2. Modular control architecture Shop Floor Control ŽSFC. w6x is a software framework for developing and building control systems for factory automation. The development of software started in the late 1980s at the Technical Research Centre of Finland ŽVITrAutomation.. SFC provides a domain to build control software that is modular and decentralised via local area network. It runs on PCs and is available for OSr2 Warp and Windows NT operating systems.
0166-3615r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved. PII S 0 1 6 6 - 3 6 1 5 Ž 9 7 . 0 0 1 0 0 - 0
76
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
Some of the main advanced features are: Ø Modularity Ø Decentralisation based on LAN Ø Easiness to build and configure control applications Ø Universal user interface Ø Use of advanced features provided by OSr2 and NT The basic structure of SFC is called Virtual OperativerOrganisational Unit ŽVOU.. It is an extension of the Virtual Manufacturing Device ŽVMD. defined by Manufacturing Message Specification ŽMMS. of Manufacturing Automation Protocol ŽMAP.. Using VOU-structure operations can be grouped into operational units. VOU takes care of the communication between its modules, i.e., OSr2 or NT programs, and communication with other VOUs. The communication is initialised and determined with the help of parameters of specific connection lists. The VOU structure works as an object bus of the control system based on TCPrIP-communication protocol. Each PC in the network can have one or several VOU-units, each one taking care of, for example: Ø resource allocation Ø time-out control Ø event inventory Ø reporting functions Ø internal communication Ø communication between VOUs As an essential feature all the control functions and operations must first be described before they are interpreted and handled at the VOU-level. The descriptions include, among all the name of the service, the format of the service parameters, and the link to the physical server Ži.e., program name.. Using VOU-manageable description mechanisms the control system descriptions can be advantageously differentiated from the system implementation. The system becomes modular, when software modules can be collected and grouped into operational units. The system implementation is transparent to the user. The system can easily be changed, even in real-time, for instance according to the load in the computers or in the case of disturbances or system failures. SFC includes a simple, easy to configure, and universal graphical user interface. The operations describe to the user interface how they and their parameters will be represented. The user interface
module consists of a set of presentation agents, which make it possible to build and manage the user-interface without actual programming. There can be several independent user-interfaces, each of which can operate transparently over the network.
3. The HUT FMC implementation 3.1. Operation of the manufacturing system The flexible manufacturing cell Žsee Fig. 1.. consists of three working stations, a CNC milling station, drilling station and a measuring station. The raw material arrives in the cell randomly located on a conveyor belt, from which the parts are picked up and transferred to the input storage by an industrial robot. The flexibility of robot gripping is obtained with the help of a vision system developed in the laboratory w7x. The raw objects, picked from the input storage, go through several machining operations according to the process plan, after which the finished products are transferred to the output storage. The robot transferring material between the stations and storage is a common resource, the allocation of which becomes important concerning the system efficiency. The machining jobs take such a long time compared with the robot movements, so that the robot does not become a bottle neck of the system. The machining stations have been equipped with measuring instruments and switches for the fault diagnosis and error recovery, the drilling station being the best example of that. 3.2. The control software For demonstrations, the control system of the flexible manufacturing cell has been decentralised on two PCs, both running separately configured, dedicated user-interfaces. A third PC has been connected to the system to run an extra user interface of its own. Even though certain control modules are available for OSr2 only Žfor instance the expert scheduler., most of the operations can be run selectively on both OSr2 and NT environments inter-operably. For simplicity, a generally used one machine configuration of the control system is described more in
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
77
Fig. 1. The flexible manufacturing cell with vision.
detail. The system is implemented using seven VOUs, which take care of the following tasks. VOU 1 2 3 4 5 6 7
Operation milling station control drilling station control measuring station control robot control, storage inventoryrcontrol expert scheduler control supervisor graphical user interface
Some common features can be found in the control tasks of any kind of machining stations. There are several resources, such as tools, programs, raw materials, etc. which are needed to start the machining job. To complete the job, not only must certain control signals or commands be sent to the CNCcontrol unit, but also monitoring or even measuring is required to acquire necessary feedback from the
process. To build the interfaces to the machining stations require quite often complicated tailored solutions. A modular approach to the control is one way to minimise this unwanted and time consuming design and programming effort. In the laboratory FMS the controls of the two machining stations and measuring station were designed to have three control layers: work leÕel control, deÕice dependent station control, and signal leÕel monitoring. The work level control modules were developed to act as general servers, being identically the same for all stations. They take care of the preparations and arrangements for machining jobs Žprogram and tool management, etc.. by calling the station control services in uniform ways. They are also connected to the control modules of the material handling system Žrobot. and take care of the communication needed. The device control modules include the implementations of the device dependent control func-
78
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
tions. They take care of sending start and stop commands to the control units of the machining stations, manage the communication during program loading, etc. The signal monitoring layers, besides offering direct access to some measuring and control signals, monitor the system events and receive various acknowledgements from the control devices. The control of the material handling system is closely connected with the routing and scheduling system. In the case of the laboratory cell all material transfers are performed by the industrial robot. The scheduling system produces a task list for the robot to execute. The length of the list depends on the amount and feeding order of the parts to be manufactured, and naturally the way they are routed trough the system.
A module called control superÕisor receives the task lists of robot transfers and makes certain that they are executed in the requested order. It also supervises, that the manufacturing operations proceed correctly, and receives messages if failures and disturbances occur. The robot has a dedicated work control module analogous to the machining stations. It communicates with the control supervisor and receives tasks one by one from it. Much like the machining stations, the robot also has the device control and signal monitoring layers separated from the work server in order to reach more general and portable control implementation. Besides the services used under automatic operation, the graphical user-interface offers an access to
Fig. 2. A screen dump from the user interface of the FMS with the main window Žup left., and two subwindows describing the milling station Ždown left. and the drilling station Ždown right.. A command dialogue for the simulator is located left to the main window.
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
79
control all the devices of the system separately using the services obtained through special control fields of the interface. It also offers several functions to take care of the production control and order management. More detailed views of, for instance, the machining stations, can be achieved by calling the sub window of the target Žsee Fig. 2..
4. Expert system for routing and scheduling Simulation is a well-known method to widen the knowledge of complex flexible manufacturing systems under production disturbances and different control objectives. To provide a basis for gathering a knowledge base for intelligent scheduling of FMS, a rule-based simulation module for flexible manufacturing was first written in OPS83 w4,5x for DOS environment. The simulator was connected with the control system of the cell and it used real-time data of the manufacturing. The system developed was tested by means of various simulations and test runs with the pilot cell w3x. The system was rewritten and redesigned w8x in RAL ŽRule-Extended Algorithm Language. expert system tool w9x, while SFC-software and OSr2 were selected as development environment. Also, commercial discrete event simulators had been tested earlier. The need for higher flexibility and intelligence in routing, possibility to connect to real-time data, and easy integration to SFC were the main reasons to develop a dedicated simulator. The simulator can be used on-line during the manufacturing to test alternative control strategies. Also, if a new ruleset version works well with the simulator, it can be directly taken into real-time control also. The expert system module consists of operational units presented in Fig. 3. After the starting initialisations, the system reads the states of the manufacturing system and its components using communication services of SFC-software. Using the status data the system creates RAL working memory elements ŽWMEs. to describe the essential operational units of the manufacturing system. WMEs are also created to describe the system eÕents, such like machining jobs, robot transfers, etc. The event elements are provided with attributes describing the lengths of the various operations. These attributes are updated in the course
Fig. 3. The parts of the expert system.
of time and managed by the simulation clock. Some additional WMEs are also used to control the simulation and especially the routing and scheduling of the products. The routing and scheduling, which uses the simulation model of the system, are controlled by the scheduling rules. The rules are operationally grouped into rulesets, each of which having a context, a goal to work for. The rulesets can roughly be divided into two groups, one taking care of the simulation, the other containing the intelligence of the routing and scheduling. The simulation rulesets use the process data and simulate the operations of the manufacturing system with sufficient accuracy. The simulation produces information, how efficient the system was, in the form of flow times, utility ratios of the machining stations, etc. This information is used along with the simulation in scheduling. Some assumptions and limitations have been applied to the scheduling problem. The raw objects go through one or several machining stations where the corresponding machining jobs are performed. Not only the amount of the jobs but also their precedence can vary. The order of the phases can alternatively be fixed, partly fixed or can be selected quite freely. Each job can be done at least at one machining station, but the possibility of several available resources can exist. Each part can also access the same machining station more than once if needed. The lengths of the jobs in each machining station are also known beforehand. The previous information has
80
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81
been coded as the manufacturing specification for each product type and it defines the possible routes the part can proceed in the system. Because there may be opposite material flows in the manufacturing system, intermediate storage is used to avoid deadlocks. Rules to detect and avoid different kinds of system deadlocks are used in the scheduling rulesets. The routing system receives a list of products to be manufactured in the system. It offers alternative routing methods to carry out the production task. It reports the efficiency of each method in form of flow times and machine utilisations to support the selection of the best way to carry out the production task. An event-based control, ruleset trying to maximise the utility ratio of certain bottle-neck station and ruleset minimising the flow time of a given product has been under development and testing Žsee Fig. 2.. Each control mentioned includes deadlock detection and management, as well as some recovery routines to handle production failures.
w3x
w4x
w5x
w6x
w7x
w8x
w9x
5. Conclusion This paper described a flexible manufacturing cell provided with a modular, decentralised control system based on microcomputers and local area network. A rule based simulator was tightly connected to the manufacturing system and integrated with routing and scheduling functions for production control. Not only is the pilot system used to demonstrate flexible manufacturing systems technology, but it was also used as a test bed when developing different control methods and algorithms to reach an optimal behaviour. The modularity of the system architecture makes it especially easy to use more than one scheduling modules in collaboration to solve scheduling problems.
References w1x A.J. Niemi, T. Rantala, R. Ylinen, E. Posti, H. Lehto, R. Kuoppala, Automatic laboratory scale production cell with vision, Proceedings of the 14th International Symposium on Industrial Robots, IFS Publ. and Mekan., Gothenburg, Sweden, 2–4 October 1984, pp. 183–188. w2x A.J. Niemi, R. Ylinen, A. Heikkila, ¨ Automatic prototype FMC
with vision, Proceedings of the 18th International Symposium on Industrial Robots, IFS Publ. and Springer, Lausanne, Switzerland, 26–28 April 1988, pp. 379–386. A.J. Niemi, A. Heikkila, ¨ R. Ylinen, T. Virtanen, Automatic FMC with vision as test bed for control methods, Int. J. Adv. Manufact. Technol. 7 Ž6. Ž1992. 353–359. A. Heikkila, ¨ A.J. Niemi, R. Ylinen, T. Virtanen, Rule based simulation and scheduling system for flexible manufacturing, Proceedings robotikdagar, Linkoping University of Technol¨ ogy, Linkoping, Sweden, 30–31 May 1991, 11 pp. ¨ A. Heikkila, ¨ A.J. Niemi, R. Ylinen, T. Virtanen, Expert scheduling system for flexible manufacturing, Problemi masinostrojenia i automatizatsii wEng Automat.x 4–5 Ž1992. 10–19. R. Majapuro, A. Hovi, A modular software architecture for manufacturing control, Proceedings of the 3rd IEEE Conference on Control Applications, Glasgow, UK, Vol. 3, 24–26 August 1994, pp. 1745–1750. A.J. Niemi, P. Malinen, K. Koskinen, Digitally implemented sensing and control functions for standard industrial robot, Proceedings of 7th International Symposium on Industrial Robots, JIRA, Tokyo, Japan, 19–21 October 1977, pp. 487– 495. A. Heikkila, ¨ T. Virtanen, R. Majapuro, Modular control system with expert scheduling for flexible manufacture, Proceedings of the 3rd IEEE Conference on Control Applications, Glasgow, UK, Vol. 3, 24–26 August 1994, pp. 1751–1755. RAL Language Guide, Production Systems Technologies, May 1991.
Ari Heikkila¨ was born in Kouvola, Finland. He received his MSc in Control Engineering and Automation in 1988 from the Electrical Engineering Department of Helsinki University of Technology ŽHUT.. Heikkila¨ has been working at the Control Engineering Laboratory of HUT in research projects specialising in control of flexible manufacturing, expert system control, fault diagnosis, and production control. He worked in 1995 as a visiting engineer at the Laboratory of Manufacturing and Productivity of Massachusetts Institute of Technology, and he is currently a research scientist at VTT Automation. Heikki N. Koivo was born in Vaasa, Finland. He received his BSEE degree in 1967 from Purdue University, West Lafayette, IN, and his MSc degree in electrical engineering and PhD degree in control sciences from the University of Minnesota, Minneapolis, MN, in 1969 and 1971, respectively. In 1971 he joined the Department of Electrical Engineering of the University of Toronto, Ontario, Canada as a Visiting Assistant Professor, and he served there as an Assistant Professor from 1972 to 1975. From 1975 to 1979, he was an Associate Professor in applied mathematics, and from 1979 to 1984 in control engineering at Tampere University of Technology, Tampere, Finland. From 1984 to 1995 he was
A. Heikkila, ¨ H. KoiÕor Computers in Industry 36 (1998) 75–81 Professor in Automation Technology. In 1986 he was the Chairman of the Department of Electrical Engineering. Since 1995 he has been Professor in Automation Technology at the Helsinki University of Technology. His research interests include adaptive and learning control including neural networks and fuzzy control, mechatronics and computer control of processes. He has authored about two hundred publications in these areas. Dr. Koivo is a
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member of the Editorial Board of the Journal of Adaptive Control and Signal Processing, Journal of Intelligent and Fuzzy Systems and Intelligent Automation and Soft Computing. He is also Associate Editor of the IEEE Transactions on Robotics and Automation. He is a Fellow of The Finnish Academy of Technical Sciences. He is the Finnish representative in the Council of European Union Control Association.