International Journal of Engineering Research And Management (IJERM) ISSN : 2349- 2058, Volume-1, Issue-1, April 2014
New eras of “intelligent manufacturing systems” Dhaval B. Upadhyay
Abstract Today, there are lots of changes occur in the manufacturing sector and it is important to transform the actual production systems to intelligent manufacturing systems to face the problems of the 21st century’s manufacturing industry. The proposed new paradigms of IMS are fractal, bionic and holonic manufacturing systems due to the characteristics of greater flexibility, efficiency and intelligence. A new approach with this new paradigms is problem solving through decomposition and agent base solution is seen as one of the promising in responding to the needs of modern companies. Researchers have attempted to apply multi-agent base technology to manufacturing enterprise integration, enterprise collaboration, manufacturing process planning, scheduling and shop floor control. This paper provides a review on the recent achievements in these areas and underlying principles of the construction of an IMS are concisely presented. Index Terms— Distributed manufacturing systems (DMS), multi-agent system (MAS), intelligent manufacturing system (IMS) Fractal Manufacturing Systems (FMS), Holonic manufacturing systems (HMS), Bionic manufacturing system (BMS)
I. INTRODUCTION In the past few years the economic priorities of manufacturing system have been shifted from low cost standard product without compromising on consistency and quality, to the use of modern industrial manufacturing facilities with a “production on demand” concept. That concept has been adopted in order to meet the challenges and take advantage of the opportunities of economic globalization. The changing business environment need to collaborate beyond geographic boundaries supported by the rapid advancement of information technology associated with manufacturing technology. Current practices and newly observed trends lead to the development of new ways of thinking, managing and organizing in corporations, where autonomy, decentralization and distribution ar e key
characteristics. Computer supported manufacturing system though efficient under intensive and repetitive processes, has been characterized by monolithic structure, centralized control, limited flexibility and adaptability, properties that no longer represent an added value to the new concept of manufacturing enterprise. Distributed manufacturing systems (DMS) were proposed as the next evolutionary step from traditional CIM architectures. DMS consist of disjoint components, in other words a system is distributed due to being physically broken up into separate components. Besides distribution basic properties of the overall and of each composing entity, include autonomy, decentralization, dynamism, flexibility and adaptability. To have a flexible and robust intelligent manufacturing system it is necessary to have an intelligent control system that makes an efficient use of the flexibility [1] and multi-agent based system is the technology that can deal with the control and supervision of intelligent Mechatronics components. The use of multi-agent based software in operation and control of distributed systems can offer distributed intelligent control functions and cover the mechatronic and production specifications. Agent-based approaches offer many advantages for distributed manufacturing process planning and scheduling systems like modularity, reconfigurability, scalability, robustness and fault recovery. The manufacturing systems reliability and flexibility will depend fundamentally by the reliability and flexibility of the embedded control system [2]. In this paper, underlying principles of the construction of an IMS are presented. The objective is to give a general idea of the complexity of the future manufacturing systems, The organization of this paper is as follows, Section II explain the requirements for intelligent manufacturing systems. Section III reviews the new paradigms for IMS, the fractal, bionic and holonic manufacturing systems. Section IV gives the concept of software agent and the importance of the multi-agent system (MAS) approaches in the future manufacturing processes. Section V shows the advantages and challenges of the multi-agent system. Section VI presents the paper conclusions..
Manuscript received April 19, 2014 Dhaval B. Upadhyay, Mechanical Engineering Department, Dr. J.N.Mehta government polytechnic Amreli, Gujarat, India.
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New eras of “intelligent manufacturing systems” REQUIREMENTS FOR INTELLIGENT MANUFACTURING SYSTEMS
In 21st century environment of manufacturing enterprises are frequently changing, new technologies are continuously emerging, and competition is globally increasing. Therefore manufacturing strategies should be shift to support global competitiveness, new product innovation and rapid market responsiveness. The next generation manufacturing systems will thus be more strongly time-oriented or highly responsive, while still focusing on cost and quality. Such manufacturing systems will need to satisfy a number of fundamental requirements: 1) Full integration of heterogeneous software and hardware systems within an enterprise, a virtual enterprise, or across a supply chain; 2) Open system architecture to accommodate new subsystems (software or hardware) or dismantle existing subsystems. 3) Efficient and effective communication and cooperation among departments within an enterprise and among enterprises; 4) Embodiment of human factors into manufacturing systems; 5) Quick response to external order changes and unexpected disturbances from both internal and external manufacturing environments; NEW PARADIGMS FOR IMS When Yosikava [3] initiated the intelligent manufacturing system (IMS) project [4], holonic manufacturing was only one of a set of paradigms for IMS. The other new paradigms IMS are the fractal and bionic manufacturing systems. The paradigms can be distinguished by their source of origin- e.g. mathematics for the fractal manufacturing, nature for bionic manufacturing and philosophical theory on the creation and evolution of complex adaptive systems for holonic manufacturing. All new paradigms envisage a solution to the same problem; allow manufacturing systems to efficiently survive and adapt to a fast changing environment A. Fractal Manufacturing Systems (FMS) Fractal manufacturing [15] concept is originated from the Theory of Fractal Geometry proposed by Benoit B. Mandelbrot [16] which provides tools for analyzing and describing geometric objects in multidimensional spaces. The word fractal comes from a Latin word ‘fractus’, which means broken or fragmented. The Fractal manufacturing can be described by following Characteristics of internal features of the fractals.
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1) 2) 3) 4) 5) 6) 7) 8)
Inheritance of self- similar fractals Autonomy of goals Dynamic restructuring of fractals High autonomy of units Setting of individual goals Adaptability through vitality Network of communication and cooperation Fractal navigation to acess situation
In addition to the above characteristics, there is a need for the fractal entities to function as a coherent whole. This is achieved through a process of participation and coordination among the fractals, supported by an inheritance mechanism to ensure consistency of the goals. In fact, according to Strauss and Hummel [7], fractals are always structured bottom-up, building fractals of a higher order. Units at a higher level always assume only those responsibilities in the process which cannot be fulfilled in the lower order fractals. This principle guarantees teamwork among the fractals and also forces distribution of power and ability. During operation, as shown in Figure 1, cooperation between fractal entities is characterized by high individual dynamics and maximum ability to adapt and react to the influences of their respective environments. This ability is called vitality, and is used to record and evaluate those variables internal to the fractals that are affected by the environment. This information is used to measure for change against the characteristics of six specific levels of the work environment: cultural, strategic, socio-informal, financial, informational and technological.
FIGURE 1. Operation of Fractal Entities [5]
The Fractal Factory has a flexible and efficient information and navigation system. Fractals navigate in the sense of constantly checking target areas, reassessing their position and progress, and correcting if necessary. Thus, relevant organizational structures will be continuously optimized and adapted by each individual fractal in the light of any changes. www.ijerm.com
International Journal of Engineering Research And Management (IJERM) ISSN : 2349- 2058, Volume-1, Issue-1, April 2014 B. Holonic manufacturing system (HMS) The word “holon” was introduced by Arthur Koestler to mean in the same time ‘whole’ and ‘part’. A holon is an identifiable part of a system that has unique identity, yet is made up of sub-ordinate part and in term is part of a larger whole. Holonic manufacturing systems (HMS) consist of autonomous, intelligent, flexible, distributed, co-operative agents of holons. Every system can be considered a holon, from a particle to the universe. Even at a non physical level everything can be identified as part of something and can be viewed as having parts of its own [22]. In other words, any unit in a system or organization is made of by basic units (e.g. a biological organ is constituted by cells) and at the same time are a part from a whole (e.g. anorgan as part of a body). In Fig. 2 is schematically represented the concept of holonic system.
FIGURE 2. A HOLONIC SYSTEM [6]
The HMS consortium developed the following list of definitions to help understand and guide the translation of holonic concepts into a manufacturing setting: 1) Holon: An autonomous and co-operative building block of a manufacturing system for transforming, transporting, storing and/or validating information and physical objects. The holon consists of an information processing part and often a physical processing part. A holon can be part of another holon. 2) Autonomy: The capability of an entity to create and control the execution of its own plans and/or strategies. 3) Co-operation: A process whereby a set of entities develops mutually acceptable plans and executes these plans. 4) Holarchy: A system of holons that can co-operate to achieve a goal or objective. The holarchy defines the basic rules for co-operation of the holons and thereby limits their autonomy. 5) Holonic manufacturing system: A holarchy that integrates the entire range of manufacturing activities from order booking through design, production, and marketing to realize the agile manufacturing enterprise.
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Feasible applications of Holonic Manufacturing concepts have been tested for machining, assembling, transport and continuous manufacturing. A case on HMS has been carried out in a medium size die-casting manufacturing plant, Sahin Metal, located in Istanbul, Turkey[9]. The company uses a typical die-casting manufacturing machine system for the flow-line type manufacturing of several light-alloy automotive variants and various aluminium-alloy parts, all exported to Europe. Through the virtual factory, a hol oni c c ont r ol a r c hi t e c t ur e f or flow-line manufacturing system has been implemented for dynamic scheduling of machines in the medium size factory in order to achieve robustness necessary to handle the disturbances. Four types of holons were used in the case, the product, part, operator, and supervisory holons. C. Bionic manufacturing system (BMS) Bionic manufacturing is a new concept to solve a series of manufacturing process problems through the learning process of biological system's structure, function, and control mechanisms [10]. A cell is the basic unit which comprises all other parts of a biological system. Cells act as building blocks to make up the hierarchical layers in organisms. The stability of the internal chemical environment of a cell is maintained by means of the regulating of enzymes. A second level regulation is done through hormones that are secreted by cells and transported in body fluids to other parts where they exert a specific physiological a c t i on. BMS draws parallels with such biological features, for example, production units on the shop floor can be compared to cells in biology. Like enzymes, Like enzymes, coordinators may act to preserve the harmony. Also, regulatory schemes similar to hormones may include policies or strategies that have a longer term effect on the environment, for example changes to shop-floor practices. Even centralized control may be applicable to urgently react to certain contingencies. Figure 3 represent the Similarity of Biological and Manufacturing Structures. A case on BMS was on the design of line-less production systems (LPS) in one factory [11]. In this case, the plant has selected 20 kinds of products and all the products could dispatch onto the floor. Each AGV has a car chassis on itself, and it disseminates welding requirements by generating attraction fields. The LPS consists of AGVs and welding robots. Each robot has welding capabilities, and moves to an AGV according to the sensed attraction fields. In the LPS, all production elements can move freely on the production floor using self-organization in order to adapt to fluctuations such as the diversity of production demands and the malfunction of machines.
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New eras of “intelligent manufacturing systems”
Figure 3. Similarity of Biological and Manufacturing Structures [5]
AGENT AND MULTI-AGENT SYSTEMS
most important common properties of computational agents are as follows: 1) Agents act on behalf of their designer or the user they represent in order to meet a particular purpose. 2) Agents are autonomous in the sense that they control both their internal state and behavior in the environment. 3) Agents exhibit some kind of intelligence, from applying fixed rules to reasoning, planning and learning capabilities. 4) Agents interact with their environment, and in a community, with other agents. 5) Agents are ideally adaptive, i.e., capable of tailoring their behavior to the changes of the environment without the intervention of their designer
A. What is agent?
An agent is a real or virtual entity able to act on itself and on surrounding world, generally populated by other agents. Its behavior is based on its observation, knowledge and interaction with the world of other agents. An agent has capabilities of perception and partial representation of the environment, can communicate with other agents, can reproduce child agents, and have own objectives and autonomous behavior. According to Jennings and Wooldridge’s [12] ‘‘an agent is a computer system situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives.’’ Under the context intelligent manufacturing system, we can define an agent as a software system that communicates and cooperates with other software systems to solve a complex problem that is beyond of the capability of each individual software system. An autonomous agent should be able to act without the direct intervention of human beings or other agents, and should have control over its own actions and internal states. B. Basic properties of agents An agent operates in an environment from which it is clearly separated (Figure 4). Hence, an agent (1) makes observations about its environment, (2) has its own knowledge and beliefs about its environment, (3) has preferences regarding the states of the environment, and finally, (4) initiates and executes actions to change the environment. Agents operate typically in environments that are only partly known, observable and predictable. Autonomous agents have the opportunity and ability to make decisions of their own. Rational agents act in the manner most appropriate for the situation at hand and do the best they can do for themselves. Hence, they maximize their expected utility given their own local goals and knowledge. The
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Figure 4. The agent and its environment [13].
C. Multi-agent systems A multi-agent system (MAS) means a system in which the key abstraction used is that of an agent. It is a loosely coupled network of problem solvers that work together to solve problems that are beyond their individual capabilities The agents may have only a partial model of their environment and may possess a limited set of means for the acquisition and integration of new knowledge into their models and for pushing the system's state towards their own goals. The knowledge of two agents, referring to the same things, is not necessarily commensurate and may have different representations. No closed-system assumption has to be maintained: the MAS is submerged into and interacts with its environment, which is not described completely by formal means. Whenever novel kinds of interaction with the environment may occur, the MAS should be open and able to evolve. In a community an agent has to coordinate its actions with those of the other agents; i.e., to take the effects of other agents' actions into account when deciding what to do. Coordination models provide both media (such as channels, blackboards, pheromones, market, etc.) and
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International Journal of Engineering Research And Management (IJERM) ISSN : 2349- 2058, Volume-1, Issue-1, April 2014 rules for managing the interactions and dependencies of agents. Coordination requires some regulated flow of information between the agent and its surrounding environment, in other words, communication. Note that in a MAS coordination is possible both by indirect communication via the environment, or by direct information exchange between specific agents. In any case, communication needs some languages with syntax and semantics, at least partially known for each communicating agent. Collaboration means carrying out concerted activities so as to achieve some shared goals. For instance, in a scheduling domain machine agents may agree on executing each task of a job with the aim of completing an order by the given due date. The shared goal (completing an order) can be achieved only if all agents commit themselves to carrying out the actions they have agreed upon. In general, meeting high-level objectives and satisfying system-wide constraints need cooperation in a multi agent system where agents are self-interested and autonomous. The overall operation of a multi-agent system is affected by an organization that is imposed on the individual agents. Even though there may be no global control or centralized data and the computations are asynchronous, some organizational rules always exist. The organization determines the “sphere� of the activity of agents, as well as their potential interactions (see Figure 5).
Figure 5. Generic scheme of a multi-agent system [14].
between Conventional vs. Multi-agent manufacturing control system. Some agents usually referred in several papers are concisely described below [20], [21]: 1) Order agent, represents an order to be accomplished by the production system. 2) Process planning agent, plan of the several processing phases to produce a work piece of an order. 3) Process scheduling agent, minimize the production time and costs from process planning. 4) Coordinator and Supervisor agent coordinate and supervise the actions between different agents imposing the correct execution of the rules established in the system. 5) Resource agents, have the responsibility of manage different resources. For example work piece agent manages the processing state of the work piece, the transport agent decides autonomously in which direction a work piece is forwarded inside the production system, and the machine agent, controls the machine.
Figure 6. Conventional vs. Multi-agent manufacturing control system [17]
D. Use of Multi-Agent Systems (MAS) in a IMS Techniques from Artificial Intelligence have already been used in Intelligent Manufacturing for more than two decades. However, the recent developments in multi-agent systems have brought new and interesting possibilities. Therefore, researchers have been trying to apply agent technology to manufacturing enterprise integration, enterprise collaboration, manufacturing process planning, scheduling and shop floor control, materials handling and inventory management, as well as to the implementation of new kinds of manufacturing systems such as Holonic, fractal or bionic Manufacturing Systems. Figure 6 illustrate comparison
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ADVANTAGES AND CHALLENGES OF MULTI AGENT SYSTEMS
A. Advantages: There are many advantages [18] [19], provided by the characteristics of MAS related to: 1) Technological and application needs: Multi agent system offer a promising and innovative way to understand, manage, and use distributed, large-scale, dynamic, open, and heterogeneous compounding system.
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New eras of “intelligent manufacturing systems” 2) Natural view of intelligent systems: Intelligent and interaction are deeply and inevitably coupled, and multi agent systems reflect this insight. Natural intelligent systems, like human, do not function in isolation; they interact in various ways and at various levels. MAS provide insight and understanding about poorly understood interaction among natural, intelligent beings, as they organize themselves into various groups, committees, societies, and economies in order to achieve improvement. 3) Complexity management: There are 4 major techniques for dealing with size and complexity of enterprise information systems; modularity, distribution, abstraction, intelligence. The use of intelligent, distributed agents combine all four techniques 4) Speed-up and efficiency: Agents can operate asynchronously and in parallel, and this can result in an increased overall. 5) Robustness and reliability: The failure of one or several agents does not necessarily make the overall system useless, because other agents already available in the system may take over their pa r t . 6) Scalability and flexibility: The system can be adopted to an increased problem size by adding new agents, and this does not necessarily affect the operationality of other agents. 7) Costs: It may be much more cost-effective than a centralized system, since it could be composed of simple subsystems of low unit cost. 8) Development and reusability: Individual agents can be developed separately by specialists, the overall system can be tested and maintained more easily, and it may be possible to reconfigure and reuse agents in different application scenario. 9) Privacy: A centralized approach is not possible sometimes because system and data may belong to companies that for competitive reasons want to keep them private. B. Challenges: Although the advantages discuss above, there are still challenging questions to be answered such as[19], 1) How to enable agents to decompose their goals and tasks, to allocate sub goals and sub tasks to other agents, and to synthesize partial results and solutions? 2) How to enable agents to communicate? What type of communication languages and protocols to use? 3) How to enable agents to represent and reason about the actions, plans, and knowledge of other agents in order to appropriately interact with them?
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4) How to enable agents to recognize and reconcile disparate viewpoints and conflicts? How to synthesize views and results? 5) How to engineer and constraint practical multi agent system? How to design technology platforms and development methodologies for manufacturing system? 6) How to effectively balance local computation and communication? 7) How to enable agents to negotiate and contract? What negotiation and contract protocols should they used? 8) How to enable agents to form and dissolve organizational structures? 9) How to formally describe multi agent systems and the interaction among agents? How to make sure that they correctly specified? 10) How to realize ‘intelligent processes’ such as problem solving, planning, decision making, and learning in multi agent contexts? How to enable agents to collectively carry out such processes in a coherent way? CONCLUSIONS This paper presents the new eras and general idea of complexity of IMS. It dealt with basic understanding about the requirement for IMS and surveyed new paradigms of IMS like, fractal, bionic and holonic manufacturing system. The main characteristics to implement intelligent manufacturing systems based on autonomous agents are presented and concept and use of Multi-agent system is explain. Multi-agent systems can offer distributed intelligent control actions to create evolvable systems required on the flexible and distributed manufacturing systems already needed in ours days and essential for the future. At the end, advantages and challenges of the multi-agent system are discussed. REFERENCES [1] S. Bussmann and K. Schild, “An Agent-based Approach to the Control of Flexible Production Systems”, in Proc. 8th IEEE Int. Conf. on Emerging Technologies and Factory Automation, vol. 2, pp. 481-488, 2001. [2] Armando Colombo, Ronald Schoop and Ralf Neubert, “An Agent- Based Intelligent Control Platform for Industrial Holonic Manufacturing Systems”, IEEE Trans. Ind. Elect., vol. 53, no. 1, pp. 322-337, Feb. 2006. [3] Yoshikava H., 1993 Intelligent manufacturing systems: Technical cooperation that transcends cultural differences, Information infrastructure system for manufacturing, IFIP Transactions B-14, Amsterdam, Elsevier Science B.V., North Holland. [4] Hayashi H., 1993: The IMS International Collaborative Program, Proc. Of the 24th International Symposium on Industrial Robots, Japan Industrial Robot Association [5] A. Tharumarajah, A. j. Wells and L. Nemes, “Comparision of Emerging Manufacturing Concepts”, Proc. of the IEEE Int.
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International Journal of Engineering Research And Management (IJERM) ISSN : 2349- 2058, Volume-1, Issue-1, April 2014 Conference on Systems, Man and Cybernetics, pp. 325-331, 11-14 October 1998. [6] A. Tharumarajah, A. j. Wells and L. Nemes, “Comparison of the bionic, fractal an holonic manufacturing concepts”, Int. Journal of Computer Integrated Manufacturing, vol. 9, no. 3, pp. 217-226, May-June 1996. [7] Strauss, R. E. and Hummel, T. 1995, The new industrial engineering revisited - information technology, business process reengineering, and lean management in the self-organizing “fractal company”. In Foo Say Wei (eds), Pr o c e e d in g s o f 1 9 9 5 I EEE An n u a l I n te r n a tio n a l En g in e e r in g Management Conference. Theme “Global Engineering Management: Emerging Trends in the Asia Pacific”, pp. 287- 292. [8] Jose Lastra and Ivan Delamer, “Semantic Web Services in Factory Automation: Fundamental Insights and Research RoadMap”, IEEE Trans. Industrial Informatics, vol. 2, no. 1, Feb. 2006. [9] M. Bal, M. Hashemipour, “Virtual factory approach for implementation of holonic control in industrial applications: A case study in die-casting industry”. Robotics and Computer-Integrated Manufacturing, 2009, Vol. 25, p p .5 7 0 - 5 8 1 . [10] Kanji Ueda, “Development of Biological Manufacturing Systems”, Journal of Japan Robot, 1997, Vol.15, No.6, p p .8 3 0 - 8 3 3 . [11] K. Ueda, I. Hatono, N. Fujii, J. Vaario, “Line-less Production System Using Self-Organization: A case study for BMS”, CIRP Annals-Manufacturing Technology, 2001, Vol.50, No.1, p p . 3 1 9 -3 2 2 . [12] N.R. Jennings, M.J. Wooldridge, Applications of intelligent agents, in: N.R. Jennings, M.J. Wooldridge (Eds.), Agent Technology: Foundations, Applications, and Markets, Springer, 1998, pp. 3–28. [13] Russel, S., Norvig, P., 1995, Artificial Intelligence: A Modern Approach, Prentice Hall. [14] Jennings, N.R., 2001, An Agent-Based Approach for Building Complex Software Systems, Communications of the ACM, 4 4 / 4 : 3 5 -4 1 . [15] Warnecke, H. J.: The Fractal Company. Springer-Verlag, Berlin, Germany (1992) [16] Mandelbrot, B.B.: The Fractal Geometry of Nature. San Francisco. W. H. Freeman (1982) [17] Institute for Manufacturing (IFM), University of Cambridge, http://www.ifm.eng.cam.ac.uk/ automation/research/distributed_intelligent.html [18] Huhns M.N., Stephens L.M., 1999 Multi agent systems and societies of agents, in Multi agent system, ed. Weiss, G., ISBN 0-262-23202-0. [19] Multi agent systems, A modern approach to distributed artificial intelligence, 1999, ed. Weiss, G., ISBN 0-262-23202-0. [20] Armando Colombo, Ronald Schoop and Ralf Neubert, “An Agent- Based Intelligent Control Platform for Industrial Holonic Manufacturing Systems”, IEEE Trans. Ind. Elect., vol. 53, no. 1, pp. 322-337, Feb. 2006. [21] S. Bussmann and K. Schild, “An Agent-based Approach to the Control of Flexible Production Systems”, in Proc. 8th IEEE Int. Conf. on Emerging Technologies and Factory Automation, vol. 2, pp. 481-488, 2001. [22] [Online] Avaiable :http://en.wikipedia.org/wiki/ Holons [23] V. Marík and D. McFarlane, “Industrial Adoption of Agent-Based Technologies”, Intelligent Systems, IEEE [see also IEEE Intelligent Systems and Their Applications], vol. 20, n o . 1 , p p . 2 7 -3 5 , 2 0 0 5 .
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D.B.Upadhyay has received M.E. in CAD/CAM from L.D. Engineering College Ahmadabad. He is Lecturer in Mechanical department of Dr. J.N.Mehta government polytechnic Amreli, Gujarat, India. His research interest includes intelligent manufacturing system and artificial intelligence.
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