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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). -2- 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). -3- 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 -4- 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.). -5- 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). -6- 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. -7- 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. -8- 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. -9- 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) - 10 - B. Kádár: Intelligent approaches to manage changes and disturbances in manufacturing systems 2. 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