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Interactive Manufacturing: Human Aspects for Biological Manufacturing Systems
Interactive Manufacturing: Human Aspects for Biological Manufacturing Systems 1
Kanji Ueda’ (2),Jari Vaario2, Nobutada Fujii’ Department of Mechanical Engineering, Kobe University, Kobe, Japan * N l T Human Interface Laboratory, Yokosuka, Japan Received on January 5,1998
Abstract Interactive manufacturing is a new idea to cope with the difficulties caused by growing complexity of manufacturingactivities. By interaction among humans such as designers, manufactures and consumers, and artifacts throughout artifact life-cycle each participant would iteratively improve. After classifying the problem difficulties in terms of incompleteness of the environment description and system specification, this study focuses on human interaction in production domain based on Biological Manufacturing Systems. This paper shows how the virtual space combined with self-organization enables the human participation, and discusses the effectiveness of interactive manufacturingto solve the difficulties, by demonstrating an example using industry data.
Kevwords : ManufacturingSystems, Interactive System, Human
1. I n t r o d u c t i o n The new manufacturingera[5] is faced with complexity and dynamics which are increasingly arising from such factors as diversification of culture, individualization of lifestyle, globalization of activity, and growing consideration of the natural environment[I]. The increasing complexity[101 and dynamics bring about practical and theoretical difficulties in all the domains of artifactual activities, from the planning phase up to post sales: nonlinear phenomena, uncertain data and knowledge, the combinatorial explosion of possible states, the frame problem, etc. are some notable examples of the difficulties. The most essential point is how to realize an artifactual system that achieves its purpose in unpredictable conditions. It is not easy to approach to such problems by existing principles, then, new ideas have been proposedfor the next era[3], such as Fractal, Holonic and Biological Manufacturing Systems.
Based on biologically-inspiredideas, such as self-growth, self-organization, adaptation and evolution, Biological Manufacturing System (EMS) [6-91aims to deal with the non-predeterministic changes in manufacturing environments. In connection with new fields of computer science such as Evolutionary Computation and Artificial Life, BMS has been developed, being involved in many joint projects such as IMS program and CAM-I. In this study, the concept of “interactive manufacturing” is proposed as in the more general BMS approach to aim to cope with the difficulties. By interaction among humans such as designers, manufacturers and consumers - and artifacts throughout the artifact life-cycle each participant would iteratively improved. This paper first classifies the problemdifficulties into levels in terms of incompleteness of the environment description and of system specification, with indicating biological approaches to solving the difficulties. Then, it focuses on the human interaction in the production domain, and shows how virtual reality techniques combined with self-organization
Annals of the ClRP Vol. 47/1/1998
can be used to create a control system in the virtual world, that would not be feasible by existing technologies in the real world. There will be shown howthis system augments the capabilities of human participation. Finally, a case study will demonstrate this first step towards interactive manufacturing, 2. Classification of Problem D i f f i c u l t i e s Generally, an artifactual system is made with some purpose, and it is related to a certain environment in which the system should work. Now, the central question is how one should determine the system’s structure, so as to express its function, i.e., the satisfaction of the purpose under theconstraints of a certain environment. The main concern here is when and whether completeness of the information could be achieved on the description of the environment and in the specification of the purpose of the system. With respect to incompleteness of information of the environment and/or the specification, the difficulties can be classified into three classes as shown in Figure 1, by schematically indicating the problem description and the methods of approach.
Class I: Complete problem Both the information on environment and the specification are fully given, then the problem is completely described, however, it may be difficult to optimize. Class II: Incomplete Environment Problem The information on the environment is incomplete, while the specification is complete, then the problem is not completely described, therefore it is difficult to cope with the dynamic properties of unknown environment. Class I II: Incomplete specification problem Both the environment description and the specification are incomplete, then the problem is not completely described. In addition, the problem starts with an ambiguous purpose, so that human interaction becomes significant.
389
It is not easy to solve the above mentioned problems by traditional approaches such as Operational Research, Symbolic Artificial Intelligence and Knowledge-based Engineering. However, the recent, significant developments in biologically inspired methods (evolutionary computation, self-organization methods, behaviour-based approaches, reinforcement learning, etc.) and their applications in Artificial Life, Multi Agent Systems and Complex Adaptive Systems should offer efficient and adaptive solutions to our problems as well.
purpose
too many combinations difficult to oDtimize .
complete information en vtro nment
r.
c 0 -
Biological Approaches: Evolutionary Computation (GA, GP, ES, etc.)
(a) Class 1: Complete problem
k
complete weafication
incomplete information
f unknown
e nvironme nt
complete specification
dynamic environment
determined function to satisfy purpose
adapting to environment Biological Approaches; Reinforcement Learning Adaptive Strategy Behaviour-based Self-organbation, etc
(b) Class II: Incomplete environment problem purpose?
f
9x9
adapting to envronment Biological Approaches:
unknown environment
Selfqefemnce Co-evolution, etc. (c) Class 111: Incomplete specification problem
Fig. 1 Classification of problem difficulties and feasible biological approaches Previous work by the authors has shown the effectiveness of the BMS framework in solving manufacturing problems. For Class I problem, Genetic Algorithms (GA) are capable to find optimum. A new GAwith neutral mutation has been developed to solve J.ob-Shop Scheduling Problem with high performance [2]. For Class II problem, learning and adaptation principles are effective. For instance, a BMS model [7]empowered with the idea of the unification of biological information with system elements such as products and manufacturing cells and with the selforganization of the M o l e system has effectively solved the dynamic reconfiguration problem of a floor level
390
system: the system has adapted itself both to changes in product demands and to malfunctioning of cells. Also, path finding task of autonomous mobile robot in dynamic environment has been successfully solved by developing new Reinforcement Learning principle called InstanceBased Classifier Generator [4]. Class Ill, however, has not been investigated, so that interactive approach based on EMS will be shown in the following sections.
3. Concept of Interactive Manufacturing In most stages of design, planning, scheduling, control etc., manufacturers often encounter cases when they have to arrive at feasible solutions and even to execute them within some limited time frame and by using no more than limited resources of information. Dynamically, in parallel with the process, we are supposed to generate deficient information in such cases. Such cases belong to Class Ill. To deal with Class Ill problem, the authors propose here “Interactive Manufacturing” based on BMS. As shown in Figure 2(b), humans such as designers, manufactures and consumers, and artifacts “interact” throughout artifact’s entire life-cycle, then each participant would iteratively improved. Such a concept would replace the traditional linear approach (Figure 2 (a)) to an artifact that starts with a designer, move into manufacturing and is then sold to and used by a consumer, later to be thrown away. manufacturer design-@ designer artifact
0-0- - Fa-- Fo
manufacturer consumer
*Y$? consumer
c-)local
interaction (b) Interactive Manufacturing (a) Lnear Manufacturing Fig. 2 From linear to interactive manufacturing In order to realize the concept of BMS based Interactive Manufacturing, at least three issues are to be examined; (1) how to embed biological information into artifacts, (2) how to implement local interaction among system elements, and (3) how to include human participation and howto coordinate their demands towards the system. The use of virtual space provides a remarkable advantage for realizing these issues. Computation in virtual space provides the possibility to use such methods that would be otherwise impossible. For example, the inclusion of genetic-like information for the products is easy to implement in virtual products, although the current technology does not yet enable this in the real products. Also the bottomup computation is difficult to implement with current technologies in the real systems. However, with virtual techniques these could be easily implemented and embedded to control the real systems. As an example, an abstract attraction field simulation in virtual space is used here to control real systems, or, the virtual reality implementation to support the interactive work. 4. Interactive Work in a Common Virtual S p a c e The concept of interactive manufacturing provides a tool for the integrationof different participants to work together in a single space. The provided additional information consists of the real time visualization of the actions of other participants, and the possibility to adjust one’s own actions to these. This kind of interactive work in a
common workspace enables a continuous learning of each participants while observing the others' actions. This kind of interactive work is thought to support human based emergence of new ideas and concepts. 4.1 lntearation in Production Domain Figure 3 illustrates the interactive integration in the
production domain, where the following participants are considered: a product designer, who is responsiblefor the product information, a production engineer, who is responsible for the production resources, and a sales engineer, who is responsible for giving the production quantities. Each type of participant could have multiple representatives accessing simultaneously.
selected here to provide the interface to the virtual space: all interfaces are similar. Although all interfaces are similar, each user has hidher own view to the common virtual space. The interface provides tools for selecting the preferred information. Global dynamics matching
n
I-[ I-[
atttacticn sensithity Virtual Space ,.~
LaCal
I.,"
dymmics inferprded
interprded
self-modifies
Local dynamics
sef-modfie
Y"
Manufactunrg Cell
Trmsp&er with
. . . . . . . . . . Model . . . . . . a. rohct . . . . Capbiltes
Prablem
4-
-----
* Requirements
Fig. 4 Concept of self-organization with a continuous simulation of attractions and sensitivities.
4 w view of the output Fig. 3 Interactive integration in a common virtual space 4.2 Coordination bv Self-Organization The core of the system is the method how to control and coordinate multiple access in the single workspace. This is based on the method used in self-organization principle [8].The self-organization simulator is capable of modeling the behavior of multiple entities moving and effecting each other in the same space. Each entity can locally generate an attraction field that is sensed by another entity. The attraction-sensing pairs could change in time by simple local rules. By giving different meanings forthe entities the simulator could be used to model self-organization of various application.
For example, many manufacturing problems could be roughly described as a matching problem between requirements (of products to be processed) and capabilities (of machines to process). The sensitivities could be assigned to requirements (products needing to be processed) and the attractions to capabilities (attracting products to be processed) as shown in Figure 4. After this, the simulator repeatedly calculates the attraction forces and moves entities accordingly. The method would result in a stable state, if the attraction fields are stable. However, already by using only simple on-off timing interesting dynamics is achieved, and from the application point of view, a particular task could be accomplished. This continuous field calculation is used also for coordinating the multi-user interactions in the virtual space. As far as the accessed entity is not same the coordination is trivial: the new attraction forces are calculated as soon as a new position is given. If two users are accessing the same entity, a competition situation would arise. However, this could be avoided by using simple locking mechanism: two users could not access the same entity at the same time. A simple approach is
5 . An Example of I n t e r a c t i o n s The following example illustrates the above system with a virtual reality interface. The data are taken from a real factory environment. The virtual reality interface was developed for factory animation [ 9 ] ,and is extended here to demonstrate the interactive manufacturing. This case study is drilling holes on printed circuit boards with a high volume of several types of products and a great variety of machines with different capabilities. The numbers of classes are 2 and 4 by required accuracy, and board size, respectively. The number of machines is 44, and transporters are 5. The production system consists of several machines and products that will be delivered to the
Takes a new p b Queue of Jcbs Picks up Ma to a Myhine to be pr~cessed a finisped job \
/
\
1
'.
/. Collectin queue \ \ Transporter Malfunction Malfunct'on of finished iobs \ Over View of Production Floor bttraction Field L,,,-,l
distance1 I Attraction and Repulsion Fields
I
I Generation LLocal- Interaction -of attraction - -fields between
I
a Machine and Transporters
Fig. 5 Production floor layout and local interaction fields
391
available machine directed by the self-organization simulator. Figure 5 shows the factory layout with also indicating local interaction fields. Each drilling machine creates attraction fields according to its capabilities, and each transporter becomes sensitive for a particular attraction field according to carrying job, and moves to inputloutput buffer of machine. The priority of matching between product requirement and machine capabilities is designed by using larger attraction field than that for secondary matches. Repulsion field is introducedto avoid collision with and between transporters.
dynamic environment. In Figure 6 the interface to the production engineer is shown. The production engineer is capable of rearranging the shop-floor layout. The figure shows how a machine is selected and moved. Figure 7 shows a typical result of the interaction of the production engineer. Adapting to the position change of the machine '10' in process, a transporter followingly moves to new location of the machine by self-organization. 6. Conclusion Difficulties arising from the increasing complexity of manufacturingactivities have been categorized into three classes: complete problem, incomplete environment problem and incomplete specification problem. In order to deal with the third, within the framework of BMS Interactive Manufacturinghas been proposed, h e r e humans such as designers, manufacturers and consumers, and artifacts interact with each others, so that each participant would be iteratively improved. Dynamic human interactions have become feasible through a common virtual space with the aid of self-organization and by virtually implementing biological properties to system elements. Although the example shown here uses real factory data for printed circuit boards, it is limited to a specific production domain. However, it suggests that the concept of interactive manufacturing can be extended to the entire life-cycle of artifacts.
Acknowledsment This study has been supported in part by IMS/NGMS Project and "Methodology of Emergent Synthesis" Project in Research for the Future Program of the Japan Society for the Promotion of Science. References
Fig. 6 An example of the production engineer access to change the position of a machine
Fig. 7 A behaviour of a transporter adapting to changing position of machine "10"by production engineer's interaction The system provides an user interface for a production engineer to change the layout of the machines, increase the number of transporters, and to manipulate each individual machine. On the other hand, the product designer is capable of changing the products that fits better to the existing production system. Consumer's intention is feasibly reflected through this system as well. All these operations could be adjusted to the requested production quantities and qualities. A further merit of the system that it makes reactive scheduling in a very
392
[I] Alting, L., Legarth, J.B., 1995, Life cycle Engineering and Design, Annals of the CIRP, 44/2: 569-580. [2] Gohtoh, T., Ohkura, K. and Ueda, K., 1996, An
Application of Genetic Algorithm with Neutral Mutations to Job Shop Scheduling Problems, Advances In Production Management Systems, IFIP: 563-568. [3] Monostori, L., Markus, A., Van Brussel, H. and Westkampfer, E.,1996, Machine Learning Approaches to Manufacturing, Annals of the CIRP, 4512: 675-712. [4] Nakamura, Y., Ohkura, K., and Ueda, K., 1997, Instance-Based Reinforcement Learning for Robot Path Finding in Continuous Space, Proc. of IEEE Intl. Conf. Systems, Man and Cybernetics, Orlando: 1229-1234. [5] Peklenik, J., 1997, Structural and Operational Complexity of Future ManufacturingSystems, Proc. of 29th CIRP Intl. Seminar on Manufacturing, Osaka: 1-8. [6] Ueda, K. and Ohkura, K., 1995, A Biological Approach to Complexity in ManufacturingSystems, Proc. of 27th CIRP IntlSeminar on Manufacturing, Ann Arbor: 69-78. [7] Ueda, K., Vaario, J. and Ohkura, K., 1997, Modelling of Biological Manufacturing Systems for Dynamic Configuration, Annals of the CIRP, 46/1: 343-346. [8] Vaario, J., Ueda, K., 1996, Biological Concept of SelfOrganization in Flexible Automation System, Advances in Production Management Systems, IFIP: 33-38. [9] Vaario, J. Fuji, N., Scheffter D., Mezger, M., and Ueda, K., 1997, Factory Animation by Self-organization Principles, Proc. of Intl. Conf. on Virtual Systems and MultiMedia, IEEE, Geneva: 235-242. [lo] Wiendahl, H.P., Scholtissek,p., 1994, Management and Control of Complexity in Manufacturing, Annals of the CIRP, 4312: 533-540.
Kanji Ueda’ (2),Jari Vaario2, Nobutada Fujii’ Department of Mechanical Engineering, Kobe University, Kobe, Japan * N l T Human Interface Laboratory, Yokosuka, Japan Received on January 5,1998
Abstract Interactive manufacturing is a new idea to cope with the difficulties caused by growing complexity of manufacturingactivities. By interaction among humans such as designers, manufactures and consumers, and artifacts throughout artifact life-cycle each participant would iteratively improve. After classifying the problem difficulties in terms of incompleteness of the environment description and system specification, this study focuses on human interaction in production domain based on Biological Manufacturing Systems. This paper shows how the virtual space combined with self-organization enables the human participation, and discusses the effectiveness of interactive manufacturingto solve the difficulties, by demonstrating an example using industry data.
Kevwords : ManufacturingSystems, Interactive System, Human
1. I n t r o d u c t i o n The new manufacturingera[5] is faced with complexity and dynamics which are increasingly arising from such factors as diversification of culture, individualization of lifestyle, globalization of activity, and growing consideration of the natural environment[I]. The increasing complexity[101 and dynamics bring about practical and theoretical difficulties in all the domains of artifactual activities, from the planning phase up to post sales: nonlinear phenomena, uncertain data and knowledge, the combinatorial explosion of possible states, the frame problem, etc. are some notable examples of the difficulties. The most essential point is how to realize an artifactual system that achieves its purpose in unpredictable conditions. It is not easy to approach to such problems by existing principles, then, new ideas have been proposedfor the next era[3], such as Fractal, Holonic and Biological Manufacturing Systems.
Based on biologically-inspiredideas, such as self-growth, self-organization, adaptation and evolution, Biological Manufacturing System (EMS) [6-91aims to deal with the non-predeterministic changes in manufacturing environments. In connection with new fields of computer science such as Evolutionary Computation and Artificial Life, BMS has been developed, being involved in many joint projects such as IMS program and CAM-I. In this study, the concept of “interactive manufacturing” is proposed as in the more general BMS approach to aim to cope with the difficulties. By interaction among humans such as designers, manufacturers and consumers - and artifacts throughout the artifact life-cycle each participant would iteratively improved. This paper first classifies the problemdifficulties into levels in terms of incompleteness of the environment description and of system specification, with indicating biological approaches to solving the difficulties. Then, it focuses on the human interaction in the production domain, and shows how virtual reality techniques combined with self-organization
Annals of the ClRP Vol. 47/1/1998
can be used to create a control system in the virtual world, that would not be feasible by existing technologies in the real world. There will be shown howthis system augments the capabilities of human participation. Finally, a case study will demonstrate this first step towards interactive manufacturing, 2. Classification of Problem D i f f i c u l t i e s Generally, an artifactual system is made with some purpose, and it is related to a certain environment in which the system should work. Now, the central question is how one should determine the system’s structure, so as to express its function, i.e., the satisfaction of the purpose under theconstraints of a certain environment. The main concern here is when and whether completeness of the information could be achieved on the description of the environment and in the specification of the purpose of the system. With respect to incompleteness of information of the environment and/or the specification, the difficulties can be classified into three classes as shown in Figure 1, by schematically indicating the problem description and the methods of approach.
Class I: Complete problem Both the information on environment and the specification are fully given, then the problem is completely described, however, it may be difficult to optimize. Class II: Incomplete Environment Problem The information on the environment is incomplete, while the specification is complete, then the problem is not completely described, therefore it is difficult to cope with the dynamic properties of unknown environment. Class I II: Incomplete specification problem Both the environment description and the specification are incomplete, then the problem is not completely described. In addition, the problem starts with an ambiguous purpose, so that human interaction becomes significant.
389
It is not easy to solve the above mentioned problems by traditional approaches such as Operational Research, Symbolic Artificial Intelligence and Knowledge-based Engineering. However, the recent, significant developments in biologically inspired methods (evolutionary computation, self-organization methods, behaviour-based approaches, reinforcement learning, etc.) and their applications in Artificial Life, Multi Agent Systems and Complex Adaptive Systems should offer efficient and adaptive solutions to our problems as well.
purpose
too many combinations difficult to oDtimize .
complete information en vtro nment
r.
c 0 -
Biological Approaches: Evolutionary Computation (GA, GP, ES, etc.)
(a) Class 1: Complete problem
k
complete weafication
incomplete information
f unknown
e nvironme nt
complete specification
dynamic environment
determined function to satisfy purpose
adapting to environment Biological Approaches; Reinforcement Learning Adaptive Strategy Behaviour-based Self-organbation, etc
(b) Class II: Incomplete environment problem purpose?
f
9x9
adapting to envronment Biological Approaches:
unknown environment
Selfqefemnce Co-evolution, etc. (c) Class 111: Incomplete specification problem
Fig. 1 Classification of problem difficulties and feasible biological approaches Previous work by the authors has shown the effectiveness of the BMS framework in solving manufacturing problems. For Class I problem, Genetic Algorithms (GA) are capable to find optimum. A new GAwith neutral mutation has been developed to solve J.ob-Shop Scheduling Problem with high performance [2]. For Class II problem, learning and adaptation principles are effective. For instance, a BMS model [7]empowered with the idea of the unification of biological information with system elements such as products and manufacturing cells and with the selforganization of the M o l e system has effectively solved the dynamic reconfiguration problem of a floor level
390
system: the system has adapted itself both to changes in product demands and to malfunctioning of cells. Also, path finding task of autonomous mobile robot in dynamic environment has been successfully solved by developing new Reinforcement Learning principle called InstanceBased Classifier Generator [4]. Class Ill, however, has not been investigated, so that interactive approach based on EMS will be shown in the following sections.
3. Concept of Interactive Manufacturing In most stages of design, planning, scheduling, control etc., manufacturers often encounter cases when they have to arrive at feasible solutions and even to execute them within some limited time frame and by using no more than limited resources of information. Dynamically, in parallel with the process, we are supposed to generate deficient information in such cases. Such cases belong to Class Ill. To deal with Class Ill problem, the authors propose here “Interactive Manufacturing” based on BMS. As shown in Figure 2(b), humans such as designers, manufactures and consumers, and artifacts “interact” throughout artifact’s entire life-cycle, then each participant would iteratively improved. Such a concept would replace the traditional linear approach (Figure 2 (a)) to an artifact that starts with a designer, move into manufacturing and is then sold to and used by a consumer, later to be thrown away. manufacturer design-@ designer artifact
0-0- - Fa-- Fo
manufacturer consumer
*Y$? consumer
c-)local
interaction (b) Interactive Manufacturing (a) Lnear Manufacturing Fig. 2 From linear to interactive manufacturing In order to realize the concept of BMS based Interactive Manufacturing, at least three issues are to be examined; (1) how to embed biological information into artifacts, (2) how to implement local interaction among system elements, and (3) how to include human participation and howto coordinate their demands towards the system. The use of virtual space provides a remarkable advantage for realizing these issues. Computation in virtual space provides the possibility to use such methods that would be otherwise impossible. For example, the inclusion of genetic-like information for the products is easy to implement in virtual products, although the current technology does not yet enable this in the real products. Also the bottomup computation is difficult to implement with current technologies in the real systems. However, with virtual techniques these could be easily implemented and embedded to control the real systems. As an example, an abstract attraction field simulation in virtual space is used here to control real systems, or, the virtual reality implementation to support the interactive work. 4. Interactive Work in a Common Virtual S p a c e The concept of interactive manufacturing provides a tool for the integrationof different participants to work together in a single space. The provided additional information consists of the real time visualization of the actions of other participants, and the possibility to adjust one’s own actions to these. This kind of interactive work in a
common workspace enables a continuous learning of each participants while observing the others' actions. This kind of interactive work is thought to support human based emergence of new ideas and concepts. 4.1 lntearation in Production Domain Figure 3 illustrates the interactive integration in the
production domain, where the following participants are considered: a product designer, who is responsiblefor the product information, a production engineer, who is responsible for the production resources, and a sales engineer, who is responsible for giving the production quantities. Each type of participant could have multiple representatives accessing simultaneously.
selected here to provide the interface to the virtual space: all interfaces are similar. Although all interfaces are similar, each user has hidher own view to the common virtual space. The interface provides tools for selecting the preferred information. Global dynamics matching
n
I-[ I-[
atttacticn sensithity Virtual Space ,.~
LaCal
I.,"
dymmics inferprded
interprded
self-modifies
Local dynamics
sef-modfie
Y"
Manufactunrg Cell
Trmsp&er with
. . . . . . . . . . Model . . . . . . a. rohct . . . . Capbiltes
Prablem
4-
-----
* Requirements
Fig. 4 Concept of self-organization with a continuous simulation of attractions and sensitivities.
4 w view of the output Fig. 3 Interactive integration in a common virtual space 4.2 Coordination bv Self-Organization The core of the system is the method how to control and coordinate multiple access in the single workspace. This is based on the method used in self-organization principle [8].The self-organization simulator is capable of modeling the behavior of multiple entities moving and effecting each other in the same space. Each entity can locally generate an attraction field that is sensed by another entity. The attraction-sensing pairs could change in time by simple local rules. By giving different meanings forthe entities the simulator could be used to model self-organization of various application.
For example, many manufacturing problems could be roughly described as a matching problem between requirements (of products to be processed) and capabilities (of machines to process). The sensitivities could be assigned to requirements (products needing to be processed) and the attractions to capabilities (attracting products to be processed) as shown in Figure 4. After this, the simulator repeatedly calculates the attraction forces and moves entities accordingly. The method would result in a stable state, if the attraction fields are stable. However, already by using only simple on-off timing interesting dynamics is achieved, and from the application point of view, a particular task could be accomplished. This continuous field calculation is used also for coordinating the multi-user interactions in the virtual space. As far as the accessed entity is not same the coordination is trivial: the new attraction forces are calculated as soon as a new position is given. If two users are accessing the same entity, a competition situation would arise. However, this could be avoided by using simple locking mechanism: two users could not access the same entity at the same time. A simple approach is
5 . An Example of I n t e r a c t i o n s The following example illustrates the above system with a virtual reality interface. The data are taken from a real factory environment. The virtual reality interface was developed for factory animation [ 9 ] ,and is extended here to demonstrate the interactive manufacturing. This case study is drilling holes on printed circuit boards with a high volume of several types of products and a great variety of machines with different capabilities. The numbers of classes are 2 and 4 by required accuracy, and board size, respectively. The number of machines is 44, and transporters are 5. The production system consists of several machines and products that will be delivered to the
Takes a new p b Queue of Jcbs Picks up Ma to a Myhine to be pr~cessed a finisped job \
/
\
1
'.
/. Collectin queue \ \ Transporter Malfunction Malfunct'on of finished iobs \ Over View of Production Floor bttraction Field L,,,-,l
distance1 I Attraction and Repulsion Fields
I
I Generation LLocal- Interaction -of attraction - -fields between
I
a Machine and Transporters
Fig. 5 Production floor layout and local interaction fields
391
available machine directed by the self-organization simulator. Figure 5 shows the factory layout with also indicating local interaction fields. Each drilling machine creates attraction fields according to its capabilities, and each transporter becomes sensitive for a particular attraction field according to carrying job, and moves to inputloutput buffer of machine. The priority of matching between product requirement and machine capabilities is designed by using larger attraction field than that for secondary matches. Repulsion field is introducedto avoid collision with and between transporters.
dynamic environment. In Figure 6 the interface to the production engineer is shown. The production engineer is capable of rearranging the shop-floor layout. The figure shows how a machine is selected and moved. Figure 7 shows a typical result of the interaction of the production engineer. Adapting to the position change of the machine '10' in process, a transporter followingly moves to new location of the machine by self-organization. 6. Conclusion Difficulties arising from the increasing complexity of manufacturingactivities have been categorized into three classes: complete problem, incomplete environment problem and incomplete specification problem. In order to deal with the third, within the framework of BMS Interactive Manufacturinghas been proposed, h e r e humans such as designers, manufacturers and consumers, and artifacts interact with each others, so that each participant would be iteratively improved. Dynamic human interactions have become feasible through a common virtual space with the aid of self-organization and by virtually implementing biological properties to system elements. Although the example shown here uses real factory data for printed circuit boards, it is limited to a specific production domain. However, it suggests that the concept of interactive manufacturing can be extended to the entire life-cycle of artifacts.
Acknowledsment This study has been supported in part by IMS/NGMS Project and "Methodology of Emergent Synthesis" Project in Research for the Future Program of the Japan Society for the Promotion of Science. References
Fig. 6 An example of the production engineer access to change the position of a machine
Fig. 7 A behaviour of a transporter adapting to changing position of machine "10"by production engineer's interaction The system provides an user interface for a production engineer to change the layout of the machines, increase the number of transporters, and to manipulate each individual machine. On the other hand, the product designer is capable of changing the products that fits better to the existing production system. Consumer's intention is feasibly reflected through this system as well. All these operations could be adjusted to the requested production quantities and qualities. A further merit of the system that it makes reactive scheduling in a very
392
[I] Alting, L., Legarth, J.B., 1995, Life cycle Engineering and Design, Annals of the CIRP, 44/2: 569-580. [2] Gohtoh, T., Ohkura, K. and Ueda, K., 1996, An
Application of Genetic Algorithm with Neutral Mutations to Job Shop Scheduling Problems, Advances In Production Management Systems, IFIP: 563-568. [3] Monostori, L., Markus, A., Van Brussel, H. and Westkampfer, E.,1996, Machine Learning Approaches to Manufacturing, Annals of the CIRP, 4512: 675-712. [4] Nakamura, Y., Ohkura, K., and Ueda, K., 1997, Instance-Based Reinforcement Learning for Robot Path Finding in Continuous Space, Proc. of IEEE Intl. Conf. Systems, Man and Cybernetics, Orlando: 1229-1234. [5] Peklenik, J., 1997, Structural and Operational Complexity of Future ManufacturingSystems, Proc. of 29th CIRP Intl. Seminar on Manufacturing, Osaka: 1-8. [6] Ueda, K. and Ohkura, K., 1995, A Biological Approach to Complexity in ManufacturingSystems, Proc. of 27th CIRP IntlSeminar on Manufacturing, Ann Arbor: 69-78. [7] Ueda, K., Vaario, J. and Ohkura, K., 1997, Modelling of Biological Manufacturing Systems for Dynamic Configuration, Annals of the CIRP, 46/1: 343-346. [8] Vaario, J., Ueda, K., 1996, Biological Concept of SelfOrganization in Flexible Automation System, Advances in Production Management Systems, IFIP: 33-38. [9] Vaario, J. Fuji, N., Scheffter D., Mezger, M., and Ueda, K., 1997, Factory Animation by Self-organization Principles, Proc. of Intl. Conf. on Virtual Systems and MultiMedia, IEEE, Geneva: 235-242. [lo] Wiendahl, H.P., Scholtissek,p., 1994, Management and Control of Complexity in Manufacturing, Annals of the CIRP, 4312: 533-540.