Model based design of knowledge bases in multi agent systems for enabling automatic reconfiguration capabilities of material flow modules

Regulin, Daniel; Schütz, Daniel; Aicher, Thomas; Vogel‐Heuser, Birgit

doi:10.1109/coase.2016.7743371

Cited by 22 publications

(13 citation statements)

References 25 publications

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“…Their number could be given by an equation in [20] modules) as well as adaptability during engineering (MAS with exchange-/adaptable knowledge base in form of a discrete and continuous plant model). The works of Fischer [32] and Regulin et al [37] present MAS control approaches enhancing the flexibility and reconfigurability of material flow systems (MFS). All of them focus on the flexibility and dependability features.…”

Section: Related Workmentioning

confidence: 99%

“…RA is very similar to the MFS architecture from Fischer (see Figs. 1 and 2), and also to another work of MFS in [37]. A sub-agent module from Fischer, called Application agent, encompasses the system behavior.…”

Section: Resource Access Common Functionmentioning

confidence: 99%

“…References of the works are: Badr [58]; Brehm et al [59]; Cruz et al [31]; Fischer [32]; Folmer [48]; Legat [53]; Lüder et al [16]; M. Hoffmann [41]; Nieße A. [35]; P. Hofmann [52]; Pech [42]; Rauscher [51]; Regulin et al [37]; Rehberger [19]; Ryashentseva [38]; Schütz [28]; Theiss [39]; Ulewicz [7]; Vogel-Heuser et al [40] and Wannagat [27] the bottom of the diabolo. Direct and indirect communication ways are enabled in the bottom of the cone of the diabolo.…”

Section: Automation Levels and Features Of Sub-agents Patternsmentioning

confidence: 99%

See 2 more Smart Citations

Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns

Salazar

Ryashentseva

Lüder

et al. 2019

Int J Adv Manuf Technol

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The growing complexity of production systems requires appropriate control architectures that allow flexible adaptation during their runtime. Although cyber-physical production systems (CPPS) provide the means to cope with complexity and flexibility, the migration with existing control systems is still a challenge. The term CPPS denotes a mechatronic system (physical world) coupled with software entities and digital information (cyber part), both enabling the smart factory concept for the Industry 4.0 (I4.0) paradigm. In this regard, design patterns could help developers to build their software with common solutions for manufacturing control derived from experiences. We provide a description and comparison of the already existing multi-agent systems (MAS) design patterns, which were collected and classified by introducing two classification criteria to support MAS developers. The applicability of these criteria is shown in the case of specific example architectures from the lower and higher control levels. The authors, together with experts from the German Agent Systems committee FA 5.15, gathered more than twenty MAS patterns, evaluated, and compared four selected patterns with the presented criteria and terminology. The main contribution is a CPPS architecture that fulfills requirements related to the era of smart factories, as well as the Reference Architectural Model I4.0 (RAMI 4.0). The conclusions indicate that agent-based patterns greatly benefit the CPPS design. In addition, it is shown that manufacturing based on MAS is a good way to address complex requests of the CPPS development.

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Section: Related Workmentioning

confidence: 99%

Section: Resource Access Common Functionmentioning

confidence: 99%

Section: Automation Levels and Features Of Sub-agents Patternsmentioning

confidence: 99%

See 1 more Smart Citation

Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns

Salazar

Ryashentseva

Lüder

et al. 2019

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…AlSafi [19] proposes an approach based on reconfiguration software agents that allows manufacturing systems to adapt to changes in the manufacturing requirements and the environment by generating an alternative of a new feasible configuration. An approach of designing reconfigurable systems using a multiagent system is described in [20]. A model-based oriented approach that supports the adaptation processes based on a run-time transformation of the system architecture is proposed in [21].…”

Section: Configuration Management and System Adaptabilitymentioning

confidence: 99%

Model-Based System Reconfiguration: A Descriptive Study of Current Industrial Challenges

Qasim

Janković

Olaru

et al. 2018

Complex Systems Design &Amp; Management

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System Reconfiguration is essential in management of complex systems because it allows companies better flexibility and adaptability. System evolutions have to be managed in order to ensure system effectivity and efficiency through its whole lifecycle, in particular when it comes to complex systems that have decades of development and up to hundreds of years of usage. System Reconfiguration can be considered and deployed in different lifecycle phases. Two significant phases are considered for configuration management and System Reconfiguration: design-time-allowing system performances by modifying the architecture in early stages-and run-time-allowing optimization of performances during the in-service operations. This paper gives an overview of a field research being initiated and currently undergoing to capture the strengths and the shortages in the current industrial landscape. It also discusses possible future management strategies with regard to identified issues and challenges.

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“…Moreover, a more flexible logistic concept for the material flow among the machines and assembly stations is needed. Material flow systems (MFS), which cover the varying transportation routes related to the required product configuration and evolution can be very complex with the need to be constantly adapted (Regulin, Schütz, Aicher, & Vogel-Heuser, 2016). Further challenge relies on adopting assembly equipment that can switch between different production tasks ensuring process accuracy through auto-calibration.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based cyber-physical systems for assembly automation

Merdan¹,

Hoebert²,

List³

et al. 2019

Production & Manufacturing Research

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Current assembly systems are handling the increased requirements for mass customization with difficulties and need to be updated with new approaches and technologies. Cyber-Physical Systems (CPS) auto-configuration is regarded as an important asset towards automation components, which autonomously embed themselves into the system. In this context, knowledge-based technologies pave the way for highly flexible and reconfigurable CPS. This paper introduces and demonstrates a model-driven engineering approach for automatically configuring the control layer of a CPS based on knowledge representation of the environment and component capabilities. The approach encompasses a control architecture that is tested in two industrial use cases. The first case employs a configuration infrastructure for control software based on IEC 61499 to automatically configure the hardwarenear control layer of a CPS within an assembly line. The second case is concerned with autonomously generating assembly plans, which are then transformed into actions that an industrial robot sequentially executes.

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Model based design of knowledge bases in multi agent systems for enabling automatic reconfiguration capabilities of material flow modules

Cited by 22 publications

References 25 publications

Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns

Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns

Model-Based System Reconfiguration: A Descriptive Study of Current Industrial Challenges

Knowledge-based cyber-physical systems for assembly automation

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