Download Intelligent approaches to manage changes and disturbances in

Budapest University of Technology and Economics
Department of Manufacturing Engineering
Intelligent approaches to manage changes and
disturbances in manufacturing systems
Ph.D. Thesis
Botond Kádár
Supervisor: Prof. László Monostori
Budapest University of Technology and Economics
and
Computer and Automation Research Institute,
Hungarian Academy of Sciences
Budapest, December 2001
B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
Results of the research
Taking the course of research into consideration, the results are classified in two different
sections. The first section includes theses 1, 2, and 3 and addresses the subject of change
management on resource and cell levels. The second section is devoted to intelligent
techniques for managing complexity, changes and disturbances on the shop floor control
level of manufacturing systems. Theses 4, 5, and 6 relate to this part of research.
After each thesis the main references are listed that detail the results of the specific thesis.
The sections of the dissertation relevant for the thesis are also indicated here (e.g. D3.2).
Hybrid intelligent approaches to manage changes and disturbances
on resource and cell levels
Thesis 1.
Based on comparative examinations regarding knowledge representation, acquisition,
processing and modification aspects, I proposed a new hierarchically structured hybrid
AI system for manufacturing applications combining sub-symbolic and symbolic
knowledge representation techniques. Taking the reaction time, information processing
and functional requirements of the developed conceptual model into consideration, I set
limits to the potential application areas of symbolic knowledge representation
approaches in the field of manufacturing control [1, 2, 3, 4, 5], (D 3.2).
Thesis 2.
By applying an Expert System Shell (ES shell) environment, I developed the symbolic
module of the proposed control and monitoring system. The symbolic module manages
the Artificial Neural Network (ANN) based sub-system on the lower level and controls
the overall manufacturing process, as well. Through the bidirectional DDE-based
message interface between the symbolic and sub-symbolic modules in the HYBEXP
system, I established the tight-coupling connection between these two modules which
rely on two different knowledge representation models [2, 5, 6, 8, 9, 12], (D 3.3).
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
Thesis 3.
Cell controller
ES
• initialisations
• diagnostic
• tuning
• control
• initialisations
• reconfiguration
status report
prompt
interventions
ANN-based
real-time
monitoring
Machine controller
real-time requirements
symbolic knowledge representation
Production
database
signals
Machine tool,
cutting process
Figure 1. Model of cell control support by expert system
Taking the cell level requirements into consideration, I suggested the expansion of the
hierarchically structured hybrid AI system for manufacturing applications (Figure 1)
- successfully applied on machine level - to cell level. Taking the advantage of the
flexibility provided by HYBEXP (Thesis 2), I supplemented the symbolic module of
the system with databases for cutting tools and for production orders related to the cell.
Improving the symbolic part in this way, I demonstrated that, beyond the monitoring
function possibilities, in the possession of tool and production order information, the
implemented system is applicable for reactive control of the whole cell [7, 9, 10], (D
3.4).
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
Heterarchically structured, distributed intelligent techniques for managing
changes and disturbances on the shop floor control level of production systems
Thesis 4
4.1. After a deep examination of the problem domain, I suggested a new, agent-based
manufacturing system model applying the heterarchical control concept. As a
minimal set, the proposed model includes two main agent types, namely, order and
resource agents. Based on the definition provided in the software technology, I
proposed a new generic manufacturing agent architecture that served as the root
concept for the agents in the developed system (Figure 2). Agent-based
manufacturing systems based on the developed model includes a new,
market-based control algorithm allowing dynamic production order management
and real-time scheduling [15, 16, 18, , 23, 24, , 32], (D 4.7).
Information I/O
Message passing
Blackboard
Hybrid
Communication
library
Local
management
Local DB
Learning
module
Plans, Goals
Knowledge Behaviour
Prewired actions
base
Action selection
...
Intelligent module
Device Driver
Actions
Perception
Material
processing
Figure 2. Generic manufacturing agent architecture
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
On the base of the above model, I developed a general, simulation-based,
object-oriented framework for modelling, evaluation and analysis of distributed
manufacturing systems.
4.2. In order to establish an effective agent communication in the manufacturing
system models developed with the help of the framework, I developed a contract
net protocol (CNET) [48] based message passing communication protocol and a
common interchange language applicable in discrete event simulation
environment [11, 13, 22, 28, 30, 34, 37, 38], (D 4.4 – D 4.7).
Thesis 5
Approaches to enhance the performance of distributed manufacturing systems by
expanding the adaptive characteristics of the agents
5.1. By applying the developed simulation-based, object-oriented framework for the
modelling, evaluation and analysis of distributed manufacturing systems (Thesis
4), and by improving the rule set of the resource agent, I built a new resource
agent empowered with ability of local cost-factor adaptation [36, 37], (D 4.8.).
5.2. As a new alternative to direct the system towards global optima, I suggested the
introduction of system level agents for improving global performance parameters.
(The order agent that is responsible for the order management and task distribution
in the simulation framework can be considered a system level agent like this.) In
order to provide adaptivity for the order agent, I proposed new time-based
parameters for order processing [36, 37], (D 4.8.).
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
Thesis 6
Dynamic combination of hierarchical and heterarchical components in
manufacturing systems exposed to changes and disturbances
Taking the mutual complementary features of the hierarchical and heterarchical control
approaches into account, I proposed a conceptual model for the temporary and/or
permanent collaboration of the two different control techniques in one integrated
system. I suggested the introduction of new scheduling agent(s) addressing the
scheduling function of a group of agents or the whole manufacturing system. The
dynamic combination of the hierarchical and heterarchical control concepts can be
considered as one of the first steps towards the implementation of holonic
manufacturing systems with smooth transition between the hierarchical and
heterarchical modes of functioning [19, 21, 24, 25, 30, 31, 32, 35], (D 4.9).
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
1. Publications related to the thesis
[1]
Monostori, L.; Kádár, B.; Egresits, Cs.: Coupling expert systems and artificial neural networks:
A hybrid AI solution and its application in manufacturing, Proceedings of microCAD'95,
International Computer Science Conference, Sect. H: Information Technology in Mechanical
Engineering, February 23, 1995, Miskolc, Hungary, pp. 69-72.
[2]
Barschdorff, D.; Monostori, L.; Wöstenkühler, G.W.; Egresits, Cs.; Kádár, B.: Approaches to
coupling connectionist and expert systems in intelligent manufacturing, Preprints of the Second
International Workshop on Learning in Intelligent Manufacturing Systems, April 20-21, 1995,
Budapest, Hungary, pp. 591-608.
[3]
Kádár, B.; Egresits, Cs.; Monostori, L.: HYBEXP: A hierarchically coupled hybrid AI approach
to production control, Proceedings of SOR'95, 3rd Symposium on Operation Research in
Slovenia, September 1-2, 1995, Portoroz, Slovenia, pp. 94-101.
[4]
Monostori, L.; Kádár, B.; Egresits, Cs.: Application of hybrid AI techniques in manufacturing,
Proceedings of CAMP'95, CAD/CAM and Multimedia, September 12-14, 1995, Budapest,
Hungary, pp. 52-62.
[5]
Monostori, L.; Kádár, B.; Egresits, Cs.: Virtual manufacturing using a hierarchically coupled
hybrid AI system, Preprints of the 3rd IFAC/IFIP/IFORS Workshop on Intelligent
Manufacturing Systems - IMS'95, Vol. 2, October 24-26, 1995, Bucharest, Romania, pp. 369374.
[6]
Monostori, L.; Kádár, B.; Egresits, Cs.: Virtual manufacturing by coupling connectionist and
expert systems, Proceedings of the 6th International DAAAM Symposium "Intelligent
Manufacturing Systems", October 26-28, 1995, Krakow, Poland, pp. 231-232.
[7]
Kádár, B.; Markos, S.; Monostori, L.: Knowledge based monitoring and management of
manufacturing cells, Preprints of the DYCOMANS's Workshop II on Management and Control:
Tools in Action, May 15-17, 1996, Algarve, Portugal, pp. 83-88.
[8]
Monostori, L.; Egresits, Cs.; Kádár, B.: Hybrid AI solutions and their application in
manufacturing, Proceedings of the IEA/AIE-96, The Ninth International Conference on
Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, June 4-7,
1996, Fukuoka, Japan, Gordon and Breach Publishers, pp. 469-478.
[9]
Monostori, L.; Egresits, Cs.; Kádár, B.: Hybrid AI approaches to intelligent manufacturing,
Preprints of the 13th IFAC World Congress, June 30 - July 5, 1996, San Francisco, California,
USA, Vol. B.: pp. 61-66.
[10] Kádár, B.; Markos, S.; Monostori, L.: Knowledge based reactive management of manufacturing
cells, Proceedings of the Conference on Integration in Manufacturing, Galway, Ireland, October
2-4, 1996, in: Advances in Design and Manufacturing: IT and Manufacturing Partnerships:
Delivering the Promise, Edited by Browne, J.; Haendler Mas, R.; Hlodverson, O., pp. 197-205.
[11] Kádár, B.; Monostori, L.; Szelke, E.: An object oriented framework for developing distributed
manufacturing architectures, Proceedings of the Second World Congress on Intelligent
Manufacturing Processes and Systems, June 10-13, 1997, Budapest, Hungary, Springer, pp.
548-554.
[12] Barschdorff, D.; Monostori, L.; Wöstenkühler, G.W.; Egresits, Cs.; Kádár, B.: Approaches to
coupling connectionist and expert systems in intelligent manufacturing, Computers in Industry,
Special Issue on Learning in Intelligent Manufacturing Systems, Vol. 33, No. 1, 1997, pp. 5-15.
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
[13] Kádár, B.; Monostori, L.: Simulation of agent based manufacturing architectures, Proceedings
of the Advanced Summer Institute 1997, ASI’97, Life Cycle Approaches to Production Systems,
Management, Control, Supervision, July 14-18, 1997, Budapest, Hungary.
[14] Monostori, L.; Kádár, B.: Distributed manufacturing, Extended enterprises, ERCIM News
(Journal of the European Consortium for Informatics and Mathematics), No. 30, July 1997, pp.
35-36.
[15] Monostori, L.; Szelke, E.; Kádár, B.: Intelligent techniques for management of changes and
disturbances in manufacturing, Proceedings of the CIRP International Symposium: Advanced
Design and Manufacture in the Global Manufacturing Era, August 21-22, 1997, Hong Kong,
Vol. 1, pp. 67-75.
[16] Kádár, B.; Monostori, L.: A simulation framework for development and evaluation of agent
based manufacturing architectures, Preprints of the DYCOMANS's Workshop IV on Control
and Management in Computer Integrated Systems, September 25-28, 1997, Zakopane, Poland,
pp. 59-65.
[17] Monostori, L.; Kádár, B.: Development trends of production management, Chapter in:
Information technology for technology management; Information systems, Ed.: Erdélyi, F., book
supported by the PHARE-TDQM Programme of the European Union, (in Hungarian), Vol. II,
pp. 505-527.
[18] Kádár, B.; Monostori, L.; Szelke, E.: An object oriented framework for developing distributed
manufacturing architectures, Journal of Intelligent Manufacturing, Vol. 9, No. 2, April 1998,
Special Issue on Agent Based Manufacturing, Chapman & Hall, pp. 173-180
[19] Bongaerts, L.; Monostori, L.; McFarlane, D.; Kádár, B.: Hierarchy in distributed shop floor
control, Proceedings of the First International Workshop on Intelligent Manufacturing
Systems, IMS Europe 1988, April 15-17, 1998, Lausanne, Switzerland, pp. 97 - 113.
[20] Monostori, L.; Hornyák, J.; Kádár, B.: Novel approaches to production planning and control,
Proceedings of the First International Workshop on Intelligent Manufacturing Systems, IMS
Europe 1988, April 15-17, 1998, Lausanne, Switzerland, pp. 115 - 132.
[21] Monostori, L.; Kádár, B.: Agent based architectures for mastering changes and disturbances in
manufacturing, Lecture Notes in Artificial Intelligence, 1416, Tasks and Methods in Applied
Artificial Intelligence, IEA/AIE-98, 11th International Conference on Industrial & Engineering
Applications of Artificial Intelligence & Expert Systems, June 1-4, 1998, Benicassim, Castellon,
Spain, Springer, Vol. II, pp. 755 - 764.
[22] Kádár B.; Monostori, L.: Agent based control of novel and traditional production systems,
Proceedings of ICME98, CIRP International Seminar on Intelligent Computation in
Manufacturing Engineering, July 1-3, 1998, Capri, Italy, pp. 31 - 38. (key-note paper)
[23] Monostori, L.; Kádár, B.; Hornyák, J.: Management of changes and disturbances in production
systems, Preprints of the LSS’98, 8th IFAC/IFORS/IMACS/IFIP Symposium on Large-scale
systems: Theory and Applications, July 15-17, 1998, Patras, Greece, Vol. I, pp. 313 - 318.
[24] Monostori, L.; Kádár, B.; Hornyák, J.: Approaches to manage changes and uncertainties in
manufacturing, CIRP Annals, Vol. 47, No. 1, 1998, pp. 365 - 368.
[25] Monostori, L.; Szelke, E.; Kádár, B.: Management of changes and disturbances in
manufacturing systems, Annual Reviews in Control, Pergamon Press, Elsevier Science, Vol. 22,
1998, pp. 85-97.
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
[26] Kádár, B.; Monostori, L.: Holonic manufacturing systems: Manufacturing systems with
distributed intelligence, Chapter in: Practical Applications of Artificial Intelligence, Ed.: M.
Horváth, T. Szalay, Lecture notes, Gábor Dénes Technical High School, 1999, pp. 89-102. (in
Hungarian)
[27] Ilie Zudor, A.; Monostori, L.; Kádár, B.; Liszka, L.: Evaluation and comparison of novel
production paradigms and their control architecture, microCAD'99, International Conference
on Information Technology and Computer Science, February 23-25, 1999, Miskolc,
Hungary, Section I, pp. 151-156.
[28] Kádár, B.; Mikó, P.; Monostori, L.: Simulation and optimization of production and logistic
systems with the SIMPLE++ package, microCAD'99, International Conference on
Information Technology and Computer Science, February 23-25, 1999, Miskolc, Hungary,
Section I, pp. 109-114.
[29] Liszka, L.; Kádár, B.; Monostori, L.; Ilie Zudor, A.: Extended Enterprise - Basic concepts,
enabling information technologies, open questions, microCAD'99, International Conference
on Information Technology and Computer Science, February 23-25, 1999, Miskolc,
Hungary, Section G, pp. 41-46.
[30] Monostori, L.; Kádár, B.: Agent-based control of manufacturing systems, Proceedings of the
Second International Conference on Intelligent Processing and Manufacturing of Materials,
IPMM’99, July 10-15, 1999, Honolulu, Hawaii (invited session keynote lecture), Vol. 1, pp.
131-137.
[31] Monostori, L.; Kádár, B.: Holonic control of manufacturing systems, Preprints of the 1st IFAC
Workshop on Multi-Agent-Systems in Production, December 2-4, 1999, Vienna, Austria, pp.
109-114.
[32] Mezgár, I.; Monostori, L.; Kádár, B.; Egresits, Cs.: Knowledge-based hybrid techniques
combined with simulation: Application to robust manufacturing systems, Chapter 25 in the
book series: Knowledge-based Systems, Techniques and Applications, (Edited by C.R.
Leondes), Academic Press, 2000, pp. 755-790.
[33] Monostori, L.; Kádár, B.; Viharos, Zs.J.; Mezgár, I.; Stefán, P.: Combined use of simulation
and AI/machine learning techniques in designing manufacturing processes and systems,
Proceedings of the 2000 International CIRP Design Seminar on Design with Manufacturing:
Intelligent Design Concepts Methods and Algorithms, May 16-18, 2000, Haifa, Israel, pp.
199-204.
[34] Monostori, L.; Kádár, B.; Viharos, Zs.J.; Stefán, P.: AI and machine learning techniques
combined with simulation for designing and controlling manufacturing processes and
systems, Preprints of the IFAC Symposium on Manufacturing, Modeling, Management and
Supervision, MIM 2000, July 12-14, 2000, Patras, Greece, pp. 167-172.
[35] Bongaerts, L.; Monostori, L.; McFarlane, D.; Kádár, B.: Hierarchy in distributed shop floor
control, Computers in Industry, Elsevier, Special Issue on Intelligent Manufacturing
Systems, Vol. 43, No. 2, October 2000, pp. 123-137.
[36] Monostori, L.; Kádár, B.; Viharos, Zs.J.; Mezgár, I.; Stefán, P.: Combined use of simulation
and AI/machine learning techniques in designing manufacturing processes and systems,
Journal of Manufacturing Science and Production, Freund Publishing House Ltd., Tel Aviv,
Israel (in print)
[37] Kádár, B.; Monostori, L.: Approaches to increase the performance of agent-based production
systems, IEA/AIE-01, 14th International Conference on Industrial & Engineering Applications
of Artificial Intelligence & Expert Systems, Budapest, Hungary, June 4 - 7, 2001. pp. 612-621.
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
[38] Kádár, B.; Monostori, L.: Adaptive agents in distributed manufacturing systems, INCOM
2001, Sept. 20-22, 2001, Vienna, Austria (paper presented, proceedings in print)
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B. Kádár:
Intelligent approaches to manage changes and disturbances in manufacturing systems
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