Virtual approach to holonic control of the tyre manufacturing system by Marko Jovanović

Journal of Manufacturing Systems 33 (2014) 116–128

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Journal of Manufacturing Systems journal homepage: www.elsevier.com/locate/jmansys

Technical paper

Virtual approach to holonic control of the tyre-manufacturing system Marko Jovanovic´ a,∗ , Samo Zupan b , Marko Starbek c , Ivan Prebil b a

University of Ljubljana, Faculty of Mechanical Engineering, Chair of Modelling in Engineering Sciences and Medicine, Slovenia University of Ljubljana, Faculty of Mechanical Engineering, Chair of Modelling in Engineering Sciences and Medicine, Slovenia c University of Ljubljana, Faculty of Mechanical Engineering, Chair of Cybernetics, Mechatronic and Production Engineering, Slovenia b

a r t i c l e

i n f o

Article history: Received 25 January 2013 Received in revised form 19 May 2013 Accepted 9 July 2013 Available online 16 August 2013 Keywords: Tyre manufacturing Holonic manufacturing system Virtual environment UML JADE

a b s t r a c t Tyre manufacturers aiming to remain competitive in complex modern markets must promptly adjust to the changes within the production environment. With traditional tyre-manufacturing systems, a slow response during optimization of the manufacturing process and low-level adaptability to system disturbances is evident. The presented approach to virtual holonic control of the tyre-manufacturing system enables dynamic response in the event of new optimization demands, decrease of the impact of disturbances on system productivity and smaller future investments in the manufacturing equipment. The developed virtual manufacturing environment enables analysis of the manufacturing process, visualization of operations, management of simulation parameters of the operations and analysis of the control system behaviour within a virtual manufacturing system prior to the implementation of the suitable control approach into a real-life manufacturing system. This helps to avoid failures in the manufacturing system due to potential disadvantages of the manufacturing system structure or lack of coordination between control parameters. Evaluation of the holonic control approach implementation within the virtual tyre-manufacturing system is performed based on simulation tests for various scenarios, whereby system operation in stable and unstable condition is taken into account. At the end, analysis results are presented. By means of the virtual tyre-manufacturing system, optimization requirements in stable condition of the holonic control system can be achieved in real time compared to the optimization which is applied in the conventional control approach. In the event of a disturbance, the holonic control approach increases productivity compared to the conventional control approach. © 2013 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

1. Introduction Tyre manufacture takes place in 18 EU countries with approximately 90 plants. In Europe there are 7 of 10 world leading tyre manufacturers and they contribute to 59% of world production. In 2009, tyre manufacture in Europe decreased by 30% compared to year 2007 due to recession. According to the European Tyre and Rubber Manufacturers’ Association – ETRMA [1], in the same period sales of passenger car tyres decreased by 10%, while the sales of cargo vehicle tyres decreased by as much as 25%. Data for year 2010 and 2011 show an increase of production in both branches. Increased complexity in environment of global economic competition is a vital characteristic of today’s tyre manufacture and results in changes within the manufacturing system and

∗ Corresponding author at: University of Ljubljana, Faculty of Mechanical Engineering, Chair of Modelling in Engineering Sciences and Medicine, Aˇskerˇceva c. 6, 1000 Ljubljana, Slovenia. Tel.: +386 1 4771 190; fax: +386 1 4771 178. E-mail addresses: msjovanovic@gmail.com, marko.jovanovic@fs.uni-lj.si ´ (M. Jovanovic).

manufacturing process. More than one billion tyres of various types are manufactured each year for different vehicle types. Various sizes of individual tyre types additionally increase complexity of manufacturing processes. Tyre manufacturers aiming to remain competitive in such market conditions must promptly adjust to the changes within the production environment and market demands in regard to high quality and competitive prices. The tyre manufacturing process usually consists of five operations: mixing of raw materials for the manufacture of tyre components, preparation of components, “green”-tyre manufacturing, vulcanization and final build-up. Tyre components are assembled in the “green”-tyre manufacturing operation to produce a “green” or uncured tyre. The “green”-tyre manufacturing operation is performed inside the “green”-tyre manufacturing system. After that, “green” tyres are loaded into presses and cured (vulcanized). After final build-up, the tyre manufacturing process is completed. This paper will focus on the “green”-tyre manufacturing operation. Traditional tyre-manufacturing systems have a centralized control structure, whereby central control unit is applied to the control of the manufacturing process. With such systems, a slow response

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Table 1 Basic characteristics of control structures. Structure of manufacturing system control

Control characteristic

Advantages

Disadvantages

Centralized structure

Central control unit

Good optimization results in standard manufacturing systems with deterministic manufacturing process

Hierarchical structure

Multiple control units on multiple hierarchical levels

Distributed control process; better system efﬁciency

Modiﬁed hierarchical structure

Interactions between control units on the same hierarchical level Hierarchical connection Client-Server is non-existent

Promotes the responsiveness to system disturbances

Low-quality response to system disturbances and low-level adaptability of control system to manufacturing system modiﬁcation Does not promote the responsiveness to disturbances compared to centralized structure Complex communication protocols between structure entities

Heterarchical structure

High-quality response to system disturbances and high-level adaptability of control system to manufacturing system re-conﬁguration

is evident during optimization of the manufacturing process and in the event of any system disturbances. Up-to-date research studies show that optimization of the manufacturing process and its adaptability to system disturbances can be achieved simultaneously by means of modern control approaches. Control structures of tyre-manufacturing systems must, therefore, have the ability of dynamic response in the event of new optimization demands and occurrence of any system disturbances. Manufacturing systems with such characteristics enable sufficient exploitation of production capacities, decrease of the impact of disturbances on system productivity and smaller investments in manufacturing equipment. 2. Background The structure of a manufacturing control system includes a set of rules and guidelines which are developed as software environments and which enable control of complex manufacturing systems [2]. There are four basic types of control structures: centralized, hierarchical, modified hierarchical and heterarchical. In his research, Dilts [3] presented the evolution of these control structures. Basic characteristics of control structures are shown in Table 1. Traditional manufacturing systems and their control structures are very inadaptable and have a low-quality response to dynamic changes in the system since they lack the required characteristics of responsiveness, flexibility and robustness. Disturbances in manufacturing systems lead to deviations from the initial plans and decrease in productivity due to machine operating delays. The most significant control structures in traditional approaches to manufacturing system control are the following: COSIMA (COntrol Systems for Integrated MAnufacturing) [4], CHAMP (Chalmers Architecture and Manufacturing for flexible Production) [5], and FACT (Factory Activity ConTrol model) [6]. Duffie and Piper [7] were the first to implement a non-hierarchical control approach within the manufacturing cell. Based on test results, they came to a conclusion that the heterarchical approach offers the following advantages: increased tolerance to system disturbances, increased possibility of system modification and decreased costs of control software development. 2.1. Agent control of manufacturing systems In order to eliminate disadvantages of traditional approaches to manufacturing control, new approaches are implemented in

Poor optimization results due to local nature of decision making process

manufacturing control systems based on the agent control structure. The main characteristics of agent control systems are decentralized architecture and parallel control of activities by means of autonomous entities also known as agents. There is no universal definition of an agent. However, an agent can be defined as follows: “An autonomous component that represents physical or logical object in the system, capable to act in order to achieve its goals, and being able to interact with other agents, when it does not possess knowledge and skills to reach its objectives alone” [8]. Communication among agents is based on utilization of the same communication language and mutual protocols. Communication and cooperation among agents help to solve complex problems on the system level which exceed the capacity of a single agent. In the field of agent research, many studies have been performed which focus on design of agent systems and methodology of agent system implementation into manufacturing systems [9–13]. Bussmann and Schild [9] implemented agent-based approach to the control of flexible production system within DimlerChrysler. manAge framework developed by Heikkila [10] has been tested in the MASCADA project [14], in an electronics manufacturing demonstration test system. Wang [11] developed a generic agent-based intelligent control system for real-time distributed manufacturing environments. Park [12] presented an autonomous manufacturing system based on swarm and cognitive agents in order to adapt to disturbances. The number of real-life implementations is low due to the heterarchical approach which does not include the required hierarchy. In such systems, agents function as entirely independent entities, thereby making the production goal achievement more difficult. 2.2. Holonic control of manufacturing systems The strategy of holonic manufacturing systems is based on the concept which was developed for living organisms and social organizations by A. Koestler [15]. In accordance with this strategy, complex systems have a hierarchical structure and consist of holons which function as a unit and part of another unit i.e. holon at the same time. The HMS consortium [16] defined a holon as an autonomous and cooperative part of a manufacturing system which has the function of transforming, transferring, storing and evaluating information and physical objects. The basic difference between a holon and an agent is that a holon can include not only a software component i.e. an agent but also a machine which is integrated with a software component.

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A holonic manufacturing system is a hierarchical group of holons within which manufacturing activities of a manufacturing system are performed based on mutual interaction of holons. The basic idea of the holonic approach is maintaining the hierarchical control structure, while at the same time autonomy is given to entities in the control structure of a manufacturing system. The holonic approach thereby combines the advantages of both the hierarchical and heterarchical approach [17] and presents a transition between a hierarchical and heterarchical system. Depending on the need, a holon can function either as part of the hierarchical structure or as part of the heterarchical structure. Hierarchy within decentralized manufacturing systems is necessary due to distributed integration of various levels of a manufacturing system as well as solving conflicts between autonomous entities and preserving the coherence of goals in a manufacturing system [18]. In a manufacturing environment, the main purpose of holonic control is distributed performance of operations and cooperation among independent entities in the system structure, which is contrary to the algorithm of operations in centralized control structures [19]. Cooperation between independent entities in the system structure can be introduced by means of coordination mechanisms. For the modelling of coordination mechanisms, UML (Unified Modelling Language) is suitable [20]. OMG (Object Management Group) [21] defines the Unified Modelling Language as a symbol language for labelling, demonstration and development of software system components. The UML sequence diagram is a dynamic diagram that shows interactions among system objects arranged in a time sequence with the purpose of achieving set goals. It has predetermined semantics, syntax and application rules [22]. The UML activity diagram is another important diagram to describe dynamic aspects of the system. Activity diagrams show the workflow from one activity to another. The activity can be described as an operation of the system. Comparison of the characteristics of the holonic and traditional control approach, shown in Table 2, shows the basic differences in the structure which lead to a completely different functioning of both systems. In the field of manufacturing, the holonic approach has been applied in several studies [23–34]. Heikkila [24] was among the first researchers who focused on the holonic approach to manufacturing control for the model of manufacturing robot cells. Brussel [25] developed a holonic structure of manufacturing systems named PROSA (Product-Resource-Order-Staff Architecture), which consists of three holon types: product holon, order holon and resource holon. The PROSA structure can be complemented with staff holons which include expert knowledge that could enable preservation of coherence of the whole system’s goals. Leitao and Restivo [26] developed the ADACOR structure (ADAptive holonic COntrol aRchitecture for distributed manufacturing systems), which includes autonomous and cooperative holons similar to the PROSA structure. However, they also included a supervisor holon for supervision of operational holons under their coordination domains. This type of a holon enables establishment of stable hierarchy within distributed systems. The aforementioned structure introduces adaptive control of the manufacturing process and combines optimization of manufacture with agile reactions to system disturbances. PROSA and ADACOR present reference structures for future development of holonic control systems since they determine the method of structure development, terminology within the structure, entities and their functions in the structure. In literature, a few real-life implementations of holonic manufacturing systems are presented [27–29]. Bal [34] upgraded the holonic control approach with a system of a virtual environment in which control of system operation is possible even in the development phase.

Fig. 1. The ﬂow of operations in the entire tyre-manufacturing system.

2.3. Virtual manufacturing systems Virtual manufacture is referred to as the use of information technology and computer simulation to model real-world manufacturing processes for the purpose of analysing and understanding them [34]. Virtual manufacture does not result in material or products, but rather in information on products and technical parameters of the manufacture. Virtual manufacture enables analysis of the manufacturing process and control system behaviour within a virtual manufacturing system prior to the implementation of the manufacturing process and suitable control approach into a real-life manufacturing system. This helps to avoid direct implementation of the control system into the manufacturing system, which may cause failures in the manufacturing system due to potential disadvantages of the manufacturing system structure or lack of coordination between control parameters. Virtual manufacture enables feasibility evaluation of the production process plan in the manufacturing system, evaluation of manufacturing system design as well as optimization of the manufacturing process. In literature, many concepts of virtual manufacturing systems can be found which are applied in various ﬁelds of manufacture [34–36]. Bal and Hashenipour [34] have developed a virtual plant approach to die casting and studied the implementation of a holonic manufacturing system into a virtual environment. 3. Description of the “green”-tyre manufacturing system The ﬂow of operations in the entire tyre-manufacturing system is shown in Fig. 1. With the “green”-tyre manufacturing system, it is possible to lay and wind onto the drums 11 different raw material components, which are in the form of belts and beads. The “green”-tyre manufacturing system is intended for winding tyre components, individual belts and the tread onto the winding and shaping drum. The “green”-tyre manufacturing system enables manufacture of a complete “green” tyre without intermediate transport. It consists of three main machining groups: the main machine, the right side (the

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Table 2 Characteristics of the traditional and holonic control approach.

Control system Relations among entities in structure Decision-making mechanism Communication in structure

Traditional control approach

Holonic control approach

Static, centralized or hierarchical Client-server relations Centralized, with top to bottom approach “One to many” entities in structure

Dynamic Holon-holon relations Decentralized, with bottom-up approach “Many to many” entities in structure

Fig. 2. Plan view of the “green”-tyre manufacturing system.

right server) and the left side (the left server). Each of these main groups consists of manufacturing modules, which enable letting off, transport, cutting, laying, winding and shaping of individual components. The manufacturing module consists of many manufacturing components i.e. machines. Plan view of the “green”-tyre manufacturing system is presented in Fig. 2. Structure of time within tyre-manufacturing system is presented in Fig. 3. Operation stages performed within the “green”-tyre manufacturing system is presented in Fig. 4. The right side of the “green”-tyre manufacturing system (the right server) is intended to wind the following components: inner liner, ply, sidewall and chipper. The left side of the “green”-tyre manufacturing system (the left server) is intended to wind the following components: breaker 1, breaker 2, breaker 3, breaker 4, breaker wedge, shoulder wedge, tread. It is possible to lay all four belts on the breaker server. The laying trays for breaker 1 and breaker 4 are on the lower level and for breaker 2 and breaker 3 are on the upper level (Fig. 5). Breaker 1 is applied on the lower side of the drum as well as breaker 4. Breaker 2 and breaker 3 are applied on the upper side of the drum. With the servers for the shoulder wedge and breaker wedge, it is possible to apply the shoulder wedge and the breaker wedge. There is also the tread server on the left side of the “green”-tyre manufacturing system which is intended to apply the tread to the drum. The tread is applied as the last belt. Roller conveyors, though not directly productive, play a signiﬁcant role in the integration of manufacturing components into the “green”-tyre manufacturing system. 4. Approach The structure of the virtual manufacturing environment which includes a model of the virtual “green”-tyre manufacturing system and a model of holonic control is shown in Fig. 6. The virtual manufacturing environment enables communication between the virtual model of the manufacturing system and the holonic control system. For each machine of the “green”-tyre

Fig. 3. Structure of time within tyre-manufacturing system (adapted from [37]). Note: p – a number of “green” tyres to be manufactured within the order, te – processing time of a “green”-tyre unit.

manufacturing system, an operational holon consisting of physical and software components will be developed in accordance with the ADACOR control structure. The physical component of the holon within the virtual manufacturing system is a virtual model of the real-life machine. The software component of the holon is an autonomous agent which during the process of control and manufacture optimization enables communication and cooperation with other holons in the system. The software component of the holon functions as a supervisor of the virtual model of the manufacturing component. The structure of the operational holon of the manufacturing component are shown in Fig. 7. Modelling of the virtual manufacturing system includes integration of data on available manufacturing capacities, ﬂow of the operations, materials and information about the real-life manufacturing system. The 3D model of the “green”-tyre manufacturing system is developed based on the existing CAD documentation. Modelling of the virtual “green”-tyre manufacturing system will be performed by means of the SolidWorks software. The interface between the virtual model of the manufacturing component and the software component of the holon will be developed by means of the LabVIEW software. The interface will be developed as a logic software component which transforms the operational parameters of the control system into control signals for execution of operations in the virtual manufacturing environment.

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Fig. 4. Operation stages within the “green”-tyre manufacturing system. Note: The “green”-tyre manufacturing system allows execution of only one operation stage at a time.

• synchronized operation of all components of the virtual manufacturing system, • realization of the optimization criterion in a stable manufacturing environment, and • establishment of optimal system operation in the event of disturbances. In order to achieve the set objectives, various mechanisms of inter-holon coordination are to be developed.

4.1. Structure of the holonic control system and description of holons

Fig. 5. Virtual model of the left side of the “green”-tyre manufacturing system.

The “green”-tyre manufacturing system is a modular multi-line manufacturing system wherein the “green”-tyre manufacturing operation is performed based on a series of separate operational stages (Fig. 4). Thus far, only very few research activities have been performed in the field of holonic control and optimization of line manufacturing systems. With the exception of Bal [34], other researchers have mainly focused on the control of assembly manufacturing lines [28–30], automated lines for material handling [19] and control of robotized manufacturing cells [32,38]. Development of the virtual control environment for tyre manufacture includes the following: • design of the holonic control system structure and design of holons, • design of mechanisms of inter-holon coordination, and • conduct of holonic control of the manufacturing process by means of simulation. It is presumed that the presented approach to the analysis of the “green”-tyre manufacturing system and the implemented control system will enable a better understanding, easy upgrade and modification of mechanical characteristics and system operations. Modification of mechanical characteristics of the manufacturing system will include replacement of mechanical and drive-unit components necessary to obtain the manufacturing operation parameters. An approach to integration of agent-based holons and manufacturing capacities into the virtual “green”-tyre manufacturing system will be presented, which will enable the following:

The holonic structure is present at all levels of the production environment, from the lowest control levels of machine cooperation to the highest organizational levels of the manufacturing system. Development of the holonic control system includes development and implementation of each individual holon of the control structure, which is developed by means of object-oriented programming. In the development of the holonic control approach, the JADE platform (Java Agent Development Environment) [39] is applied. It is an open-source platform which includes the Java Class Library. The library enables development of agents by means of attributes required for a speciﬁc application as well as development of characteristic behaviour types which enable sending and receiving of messages in accordance with the FIPA protocol [40]. The structure of the holonic control system which includes four different types of holons: product holon, planning holon, control holons and operational holons (Fig. 6) is developed based on the ADACOR structure [25]. The operator holon is developed as user interface which enables determination of the optimization criterion and allows beginning of the optimization process in the “green”-tyre manufacturing system. The user interface is applied by experts with information on orders based on which values of the optimization criterion are deﬁned. The planning holon, product holon and operator holon are expert holons which are part of the organizational level of a structure. The tyre-manufacturing system enables manufacture of many types of tyres. Each tyre type is presented by means of the product holon, which includes technical and technological characteristics of a product as well as the material of components forming a tyre, their input dimensions and mutual position, and precision of the geometrical “green” tyre model. The planning holon together with the product holon and the main control holon develops the sequence of operation stages on the manufacturing system level depending on the order and tyre type.

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Fig. 6. Structure of the virtual “green”-tyre manufacturing environment.

and technological characteristics of the machine and data on values of operational and drive-unit parameters obtained in various stages of the manufacturing operation. 4.2. Communication mechanisms of inter-holon coordination Each holon within the control structure of the “green”tyre manufacturing system includes functions and relations of

Fig. 7. Structure of the operational holon.

The main control holon, which is at the highest level of the control structure, defines the sequence of operation stages for each module. The control holon of the module defines the sequence of operation phases for each machine within the module. The operational holon presents a machine within the “green”-tyre manufacturing system. The operational holon defines operational parameters (the position, velocity and acceleration) of executive mechanical components of the machine mechanism by means of which process control is performed. Operational holons form holon groups which present manufacturing modules. Groups are formed within the main control holon based on technical and technological characteristics and mutual constraints between individual manufacturing components. Each holon group or manufacturing module includes the control holon. Within the software component of operational holons, a numerical method is developed for each mechanical component of machine mechanisms which determines basic characteristics of mechanical components (Fig. 8) based on which calculation of drive-unit parameters can be performed and applicability of components and mechanisms in the manufacturing operation verified. The operational holon shown in Fig. 7 includes not only a software component but also a communication module for communication with other holons and a database with data on technical

Fig. 8. Basic numerical methods of mechanism components for calculation of driveunit parameters within the Feed Motion Conveyor.

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communication which are performed in agreement with other holons. Communication relations among holons are defined based on a set of messages. Holon functions and its relations with other holons determine the coordination mechanism within the manufacturing control process. The holonic control system within the “green”-tyre manufacturing system does not have an explicitly defined control structure but temporary control structures are defined based on the established coordination mechanism. The process of the “green”-tyre manufacturing control is initiated when a product i.e. a tyre type is defined in the control structure. Within the product holon which is an expert holon, technical and technological characteristics of a product are defined and include the material of tyre components, their input dimensions and mutual position as well as precision of the geometrical tyre model. Fig. 9 shows the coordination mechanism of distributed manufacture control in stable condition of the manufacturing system operation wherein no optimization requirements or system disturbances are present. Based on the established coordination mechanism, the control structure of the holonic control system is hierarchical in this example, which means operation holons in the control structure have a low-level of autonomy. Therefore, for determination of operational parameters of the machines, guidelines of higher hierarchy levels are required. The planning holon is an expert holon which in cooperation with the product holon and the main control holon defines the sequence

Fig. 9. Coordination mechanism of distributed manufacturing control in stable condition of the manufacturing system operation.

of operation stages. In the process of deﬁning the sequence of operation stages, holons performs the following tasks: determination of the sequence of operation phases within operation stages, assignation of operation phases to the machines, description of

Fig. 10. UML sequence diagram of inter-holon cooperation within the coordination mechanism of control in stable condition of the “green”-tyre manufacturing system operation.

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Fig. 11. Coordination mechanism of inter-holon cooperation in the hierarchical cycle of the optimization process in stable condition of the manufacturing system operation.

Fig. 12. Coordination mechanism of inter-holon cooperation in the heterarchical cycle of the optimization process in stable condition of the manufacturing system operation.

operation phases and determination the initial times value of operation phases. The product holon informs the planning holon about technical and technological characteristics of the tyre type to be manufactured (1). On the other hand, the planning holon obtains information on technical and technological characteristics of the manufacturing system from the main control holon, which has insight into all available manufacturing modules and machines in the manufacturing system (2). The main control holon defines the sequence of operation stages for each module of the manufacturing system (3) and monitors the operation stages realization. Based on the sequence of operation stages for each module obtained from the main control holon (4), module control holons define the sequence of operation phases (5) i.e. the operational plan for each machine within the module and monitor the operation phases realization. Thereby, operational plan is not calculated in advance, but arises from contingent interaction. Operational holons define operational parameters for each operation phase. Based on the communication between operational holons within the same module, the coordination mechanism takes

Fig. 13. Algorithm of inter-holon cooperation in unstable condition of the “green”tyre manufacturing operation. Note:OP – operational parameters of the operation phase which a machine has to perform, i – information from sensors, MP – actual parameters of the operation phase, comparison – comparison due to disturbance detection, cooperation – operational holon in which the disturbance has occurred cooperates with other operational holons with the purpose of transferring the operation phase to another machine which can perform that operation phase, select - select an appropriate operation holon from the group of operational holons which have the ability to perform the operation phase, send – operational holon sends operational parameters of the operation phase to the selected operational holon, ␶–time necessary for elimination of the failure impact on productivity, Tz–time necessary for replacement of damaged machine components, Tp–time necessary for preparation of operation phases on alternative machines.

into account technical and technological constraints among functionally connected operation phases. Operational parameters (6) which are sent to the virtual model of the manufacturing machine are positions, velocities and accelerations of executive mechanical components of the machine. Simulation results of the virtual model of the manufacturing system are simulated values of operational parameters (7). Simulated values of operational parameters are veriﬁed based on the numerical methods of machine mechanisms (Fig. 8). Thereby, the following drive-unit parameters of the machine are obtained: maximum rotational speed, maximum acceleration, load and mechanisms inertia, constant torque, acceleration torque, peak torque and root mean squared torque. The model of holon cooperation within the coordination mechanism of control in stable condition of the tyre-manufacturing system operation is shown in UML sequence diagram in Fig. 10. The formal modelling of structural and behavioural speciﬁcation of the control system is developed with UML sequence diagram in order

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Fig. 14. Simulation results for the scenario of manufacturing system operation in unstable condition.

to simplify the understanding and to get the comprehensive view of the system functionality. Messages communicated among holons within coordination mechanisms of control are basic messages of JADE ACL (Agent Communication Language), which is in accordance with the FIPA standards. The INFORM message enables distribution of information requested by other holons, the QUERY message sends questions, the REQUEST message sends requests for performance of specific methods, while the PROPOSE message sends proposals for performance of specific tasks. Answers to QUERY and REQUEST messages are sent via REPLY messages, while answers to a PROPOSAL are sent via AGREE and REFUSE messages. The message type name is placed above the arrowed line, while methods are placed on the right side of the arrowed line. In our approach, simplified encoding of the message content was used which treats the content of the message as a string whose meaning is depended on the application. In the future work our aim is to develop an application-specific ontology by extending the class Ontology predefined in JADE and adding a set of schemas describing the structure of concepts, agent actions and predicates that are allowed to compose the content of messages. When an optimization request for decrease of processing time of a “green”-tyre unit in the “green”-tyre manufacturing system occurs within the expert operator holon, a new coordination mechanism of production control in the control structure of the manufacturing system is established and the optimization process is initiated. Based on the established coordination mechanism, at the beginning of the optimization process the control structure of the holonic control system is hierarchical. Operational holons in the control structure still have a low level of autonomy and require guidelines of higher levels of hierarchy for determination of operational parameters. The model of inter-holon cooperation within the coordination mechanism of control in the hierarchical optimization process in accordance with requested optimization criterion is shown in Fig. 11. The planning holon defines optimized processing time of a “green”-tyre unit (2) on the basis of the optimization criterion, which is communicated by the operator holon (1). The main control holon defines the optimized times of operation stages for each

module in the “green”-tyre manufacturing system (3). The algorithm of determination of optimized times of operation stages for each module takes into account technical characteristics of the module and characteristics of the required operation stage quality, based on which it is determined if the module takes part in the optimization process. Based on the optimized time on the module level, the module control holon defines optimized times of operation phases for each machine in the module (4). The algorithm of determination of optimized times of operation phases for each machine takes into account technical and technological constraints among functionally connected phases performed by machines in the module as well as technical characteristics of each machine, based on which it is determined if the machine takes part in the optimization process. In accordance with the optimized times of operation phases, operational holons define optimized operational parameters of operation phases (5). If individual mechanical components, machine mechanisms or drive-units are not in accordance with optimized parameters, they are to be replaced with those that fulfil parameter criteria. Replacement of individual mechanical or drive-unit components is performed only when it is economically justified. The approach can also be applied for determination of characteristics of mechanical and drive components already in the phase of design of the tyre-manufacturing system. This enables optimal design of the “green”-tyre manufacturing system for various operation scenarios which are previously analyzed through simulation in the manufacturing system modelling phase. Subsequent hierarchical cycles in the algorithm of the optimization process are adapting to the state of manufacturing components in order to determine its optimal times and operational parameters of operation phases and to improve the precision of system optimization. When it is determined that subsequent optimization cycles would not improve the required precision of system optimization in accordance with the required criterion, the control structure of the system becomes heterarchical. Operational holons increase the level of autonomy and independently from higher levels of hierarchy, the guidelines of which are denied, define operational parameters of operation phases which are sent to the virtual model of the manufacturing system. The algorithm for independent

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Fig. 17. Time ﬂow of the process of eliminating the machine breakdown failure impact on system productivity Note:p–a number of “green” tyres to be manufactured within the order, p1 –a number of “green” tyres manufactured until the occurrence of failure, ␶–time necessary for elimination of the failure impact on productivity, te–processing time of a “green”tyre unit, te1 –optimized processing time of a “green”-tyre unit, Tz–time necessary for replacement of damaged machine components. Fig. 15. Time ﬂow of the process of eliminating the machine breakdown impact on productivity Note:p–a number of “green” tyres to be manufactured within the order, p1 –a number of “green” tyres manufactured until the occurrence of failure, ␶–time necessary for elimination of the failure impact on productivity, te–processing time of a “green”-tyre unit, te1 –optimized processing time of a “green”-tyre unit, p2 –a number of “green” tyres manufactured with alternative machines, Tz–time necessary for replacement of damaged machine components, Tp–time necessary for preparation of operation phases on alternative machines.

deﬁnition of operational parameters of the machine within operational holons is based on Newton Interpolation Polynomials. Interpolation knots of the Newton Interpolation Polynomials are operational parameters of the operational phases achieved in the previous optimization cycles. Interpolation knots are written in data bases of each operational holon. The model of inter-holon cooperation within the coordination mechanism of control in the heterarchical cycle of the optimization process in accordance with the required criterion is shown in Fig. 12. To achieve adaptability of the “green”-tyre manufacturing system in the event of system disturbances, it is necessary to ﬁrst detect the disturbance and its position in the manufacturing system and then implement a suitable algorithm for elimination of the disturbance impact on system productivity. Communication between the operational holons and the planning holon is a process of

continuous supervision of the “green”-tyre manufacturing operation based on which system disturbances are determined. It is not possible to write an algorithm of elimination of the disturbance impact for each scenario of the disturbance occurrence in the system. Since system disturbances can be predicted, the algorithm of elimination of the disturbance impact can be written based on the expected condition of the system after a disturbance has occurred. In our example, the following algorithm in the form of the UML activity diagram (Fig. 13) was developed for the occurrence of a machine breakdown in the system. When a disturbance occurs in the form of a machine-breakdown in the “green”-tyre manufacturing system the hierarchical structure of the control system changes to heterarchical, whereby a coordination mechanism of control in unstable condition is established with the purpose of eliminating the failure impact on system productivity. The mechanism of the control structure change is based on the increased level of autonomy of the operational holons in the structure which independently from the higher levels of hierarchy, the guidelines of which are denied, deﬁne the operational parameters of virtual model machines, the purpose of which is to eliminate the failure impact on system productivity. New operational parameters of operation phases of the machine are determined based on time

Fig. 16. Simulation results for the scenario of manufacturing system operation in unstable condition in the event of single failure.

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Table 3 Simulation results for manufacturing system operation in stable condition during optimization process for the optimization criterion for decrease of processing time of a “green”-tyre unit. Optimization criterion: decrease of processing time of a “green”-tyre unit Operation stage: Breaker 2 winding, Value of the optimization criterion for decrease of operation stage time: 10% Operation Description of operation phase phase

Modulesa

Machinesa

Time of an operation phase [s] Existing conventional control approach

10 15

Unwinding the belt; moving the belt to the cutting position; cutting the belt to deﬁned length Moving the belt to the winding position Winding the belt

Coordination mechanism Fig. 11 13.69

Coordination mechanism Fig. 12 13.39

BS 2

LO 2, SC 2, FMC 2

Coordination mechanism Fig. 9 14.96

BS 2

FMC 2, AC 2

18.5

18.41

16.80

16.41

BS 2, LD

FMC 2, AC 2, LD

14.96

13.69

13.39

48.5 0

48.33 0

44.18 8.59

43.19 10.64

Time of an operation stage [s] Percentage of achieved optimization criterion in real time [%] a

Holonic approach (simulation results)

Names of modules and machines are deﬁned in Fig. 5.

Table 4 Simulation results for the scenario of manufacturing system operation in unstable condition when elimination of the failure impact is performed within operation stage of the module in which a failure has occurred. Machine breakdown: FMC 2, P = 150 “green” tyres/day, p = 150 “green” tyres, p1 = 50 “green” tyres, Tz = 3600s, Tp = 400s, t = Tp, p2 = 6 “green” tyres (Fig. 15) Elimination of the failure impact: within an operation stage of the module in which a failure has occurred. Operation stage: Breaker 2 winding Modules Operation Description of operation phase phase Primary

Machines Alternative

BS 2 Unwinding the belt; BS 3 moving the belt to the cutting position; cutting the belt to deﬁned length Moving the belt to the BS 2 BS 3 10 winding position 15 Winding the belt BS 2, LD BS 3, LD Time of an operation stage [s] Productivity on the system level [“green” tyres/day] Percentage of achieved productivity [%] 5

Primary

Flow time of an operation phase [s] Alternative

Existing conventional control approach (unstable condition)

Holonic approach (simulation results)

Coordination mechanism Fig. 13 13.96

LO 2, SC 2, FMC 2

LO 3, SC 3, FMC 3

Coordination mechanism Fig. 9 14.96

FMC 2, AC 2

FMC 3, AC 3

18.5

18.41

17.06

FMC 2, AC 2, LD

FMC 3, AC 3, LD

15 48.5 144 96

14.96 48.33 150 100

13.96 44.98 150 100

Table 5 Simulation results for the scenario of manufacturing system operation in unstable condition when elimination of the failure impact is performed within operation stages of the left side of the “green”-tyre manufacturing system. Machine breakdown: FMC 2, P = 150 “green” tyres/day, p = 150 “green” tyres, p1 = 50 “green” tyres, Tz = 3600s, Tp = 1200s, t = Tp, p2 = 4 “green” tyres (Fig. 15) Elimination of the failure impact: within operation stages of the left side of the “green”-tyre manufacturing system. Operation stage

Time of an operation stage [s] Existing conventional control approach (unstable condition)

Holonic approach (simulation results) Coordination mechanism Fig. 9

Shoulder wedge windinga Breaker 1 winding Breaker 2 winding Breaker wedge windinga Breaker 3 winding Breaker 4 winding Tread windinga Productivity on the system level [“green” tyres/day] Percentage of achieved productivity [%] a

45 38.5 48.5 35 48,5 38.5 20.5 144

44.85 38.28 48.33 34.8 48,33 38.28 20.28 150

Coordination mechanism Fig. 13 44.85 36.07 45.58 34.8 45,58 36.07 20.28 150

100

Optimization of the operation stage is not possible due to technological limitations related to the quality of the manufacturing process (see Fig. 4).

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t, which is the non-operational time of the manufacturing system, and time , which is necessary for elimination of the failure impact on productivity within the order. Based on the relation between time t and time , it is determined whether the process of failure impact elimination is performed within the operation stage of the module in which the failure has occurred, on the side of the “green”tyre manufacturing system where the failure occurs or within the entire “green”-tyre manufacturing system. The production supervision process in the “green”-tyre manufacturing system determines two conditions: the condition of normal operation and the condition with disturbances. Once a failure occurs, the manufacturing process is interrupted and a requests for cooperation are sent from the disturbed operational holon and functionally connected operational holons to operational holons of the machines which have the same or similar technical and technological characteristics and which are also functionally connected. This means that it is possible to move the entire operation stage to another module. If cooperation is performed among holons in the side of the “green”-tyre manufacturing system in which the failure has occurred, the other side of the manufacturing system which the failure has no impact on keeps the hierarchical control structure. In the event of transferring the operation phase to another machine, the operational holon which assumes the performance of the broken-down machine operation must change its sequence of operation phases. If the process of eliminating the impact of the machine breakdown on system productivity cannot be performed by cooperation among holons, a request for replacement of damaged machine components is sent to the planning holon. In the process of eliminating the failure impact, optimized operational parameters of operation phases remain active until complete elimination of the failure impact on system productivity, i.e. until the moment t + from the occurrence of the failure. After the time interval t + , the operational holons which have changed their operational parameters return to operational parameters prior to the occurrence of the disturbance and decrease the level of autonomy, which means the control system again takes over the hierarchical structure.

4.3. Simulation in virtual manufacturing system The aim of the virtual manufacturing system application is to improve the structure design of the “green”-tyre manufacturing system and increase reliability of implementation of the holonic control approach into a real-life manufacturing environment. Simulation within the virtual manufacturing system enables analysis of both the manufacturing system operation in various optimization scenarios and the process of eliminating the disturbance impact on productivity in an unstable manufacturing environment. This makes the manufacturing system proactive since, in the event of similar scenarios, the results of proactive simulations and the recovery action report are already available for use in a real-life manufacturing system. Presented is a 3D virtual model of the “green”-tyre manufacturing system which through simulation enables visual veriﬁcation of the impacts of the holonic control approach applied in the optimization process in accordance with the optimization criterion in stable operating condition as well as with adaptability to system disturbances. The holonic control structure is less predictable in comparison to the traditional centralized structure. However, a virtual manufacturing environment enables its analysis and better understanding. Evaluation of the holonic control approach implementation into the virtual “green”-tyre manufacturing system will be performed based on the analysis of results of quantitative manufacturing characteristics: productivity and processing time of a “green”-tyre unit.

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Productivity is an indicator of the manufacturing system success and is defined as the number of manufactured units of a product within a certain time unit. Processing time of a “green”-tyre unit is a time necessary for manufacturing of a product unit. Evaluation of the holonic control approach implementation within the tyre-manufacturing system will be performed based on simulation tests for various scenarios, whereby system operation in stable and unstable condition is taken into account. For each scenario, manufacture of a single tyre type is taken under observation. Final evaluation of the holonic control approach implementation can be performed by comparing the results of quantitative manufacturing characteristics within the holonic control approach for various simulation scenarios in stable and unstable condition with the results obtained within the existing conventional control approach. 5. Results and discussion Simulation of the manufacturing process is performed as follows: (1) In the optimization process for a specific value of the criterion for decrease of processing time of a “green”-tyre unit in stable operating condition, comparison of various control structures of the holonic control approach is made (the results given in Table 3 are presented only for a single operation stage and not for the entire “green”-tyre manufacturing operation). (2) The holonic approach to “green”-tyre manufacturing control in the process of eliminating the machine breakdown impact on system productivity i.e. in unstable condition (Fig. 13) is compared to the existing conventional control method in unstable condition and holonic control approach in stable operating condition of the “green”-tyre manufacturing system (Fig. 9). Example 1. There is a possibility of transferring operational phases performed within a module in which a machine breakdown has occurred into another module. Simulation results for the scenario of manufacturing system operation in unstable condition when there is a possibility of transferring operational phases into another module are presented in tables (Tables 4 and 5) and diagrams (Fig. 14). Example 2. There is no possibility of transferring operation phases performed within a module in which a failure has occurred into another module. Simulation results for the scenario of manufacturing system operation in unstable condition when there is no possibility of transferring operation phases into another module are presented only in diagrams (Fig. 16). By means of the virtual “green”-tyre manufacturing system with a distributive method of control within the holonic control system, optimization requirements can be achieved in real time compared to the so-called offline method of optimization which is applied in the conventional control approach (Table 3). The offline method of optimization includes termination of the manufacturing process and reprogramming of the central control unit. In the event of failure on manufacturing system components, the holonic control approach offers better stability and possibility of eliminating the failure impact on system productivity. In the event of a single failure occurring during the order, the holonic control approach increases productivity by 4% compared to the conventional control approach (Tables 4 and 5). In the event of multiple failures, the advantage of applying the holonic control approach is of great significance. 6. Conclusions and future research The paper presents the holonic virtual control approach applied in the “green”-tyre manufacturing system. The approach included

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integration of a virtual model of the “green”-tyre manufacturing system with a holonic control platform for the purpose of holonic control both in the process of manufacture optimization in accordance with the optimization criterion in a stable manufacturing environment and in the process of eliminating the machine breakdown impact in an unstable manufacturing environment. The effects of the holonic control approach were tested in various scenarios of simulation analyses. The presented approach can be applied in other manufacturing environments with development of appropriate virtual models of the manufacturing system in the SolidWorks and LabView software and appropriate holonic control platform in JADE software. The presented approach enables holonic integration with other operations of the tyre-manufacturing process as well integration of the entire manufacturing process with other parts of the production environment of a plant. Integration of the control approach with the manufacturing process within the virtual manufacturing environment has enabled computer analysis of the manufacturing system operation by means of simulation. Future research will be focused on development of an environment for implementation of the holonic control approach into a real-life “green”-tyre manufacturing system. Control and optimization of the manufacturing process within the virtual manufacturing system will be performed in parallel with the real-life manufacturing process. Acknowledgements The authors wish to express the appreciation to the managers and staff of the company SMM PRODUCTION SYSTEMS Ltd. for their technical support. References [1] http://www.etrma.org [2] Babiceanu R, Chen FF. Development applications of holonic manufacturing systems: a survey. Journal of Intelligent Manufacturing 2006;17:111–31. [3] Dilts DM, Boyd NP, Whorms HH. The evolution of control architectures for automated manufacturing systems. Journal of Manufacturing Systems 1991;10:79–93. [4] Bauer A, Bowden R, Browne J, Duggan J, Lyons G. Shop floor control systems from design to implementation. London: Chapman & Hall; 1994. [5] Andersson N. On modelling and implementing shop floor control systems PhD Thesis. Gothenburg, Sweden: Department of Production Engineering, Chalmers University of Technology; 1997. [6] Arentsen AL. A generic architecture for factory activity control, PhD Thesis, laboratory of design production and management. Twente, The Netherlands: Faculty for Mechanical Engineering, University of Twente; 1995. [7] Duffie NA, Piper RS. Nonhierarchical control of manufacturing systems. Journal of Manufacturing Systems 1986;5:141. [8] Leitão P. Agent-based distributed manufacturing control: a state-of-the-art survey. Engineering Applications of Artificial Intelligence 2009;22:979–91. [9] Bussmann S, Schild K. An agent-based approach to the control of flexible production systems. In: Proceedings of the 8th IEEE International Conference on Emerging Technologies and Factory Automation. 2001. p. 481–8. [10] Heikkilä T, Kollingbaum M, Valckenaers P, Bluemink G-J. An agent architecture for manufacturing control: manAge. Computers in Industry 2001;46:315–31. [11] Wang L, Balasubramanian S, Norrie DH. Agent-based intelligent control system design for real-time distributed manufacturing environments. In: Working Notes of the ABM Workshop. 1998. p. 152–9.

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