A multi-agent framework for capability-based reconfiguration of industrial assembly systems

Antzoulatos, Nikolas; Castro, Elkin; Silva, Lavindra de; Rocha, Andre Dionisio; Ratchev, Svetan; Barata, José

doi:10.1080/00207543.2016.1243268

Cited by 29 publications

(18 citation statements)

References 23 publications

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“…There is also a strand of work on dynamically adding and removing production resources from a production line (i.e., 'plug and produce' systems), and on aggregating resource operations (or 'capabilities') to form composite operations at multiple levels of abstraction [9,15,12,29,34]. These are then matched against the desired product which is also specified as a sequence of composite operations.…”

Section: State Of the Artmentioning

confidence: 99%

“…In contrast, our approach is not concerned with aggregation of operations: we assume that resource operations are specified at the same level of abstraction as the product specification, or that the transformation from high-level product specifications to low-level operations has already taken place. Moreover, in the body of work above, it is implicitly assumed either that production resources can service any order of operations in the product specification, e.g., parts can be transferred from any resource to any other resource (this is achieved via a shuttle conveyor system in [9] and a crane in [12]), or that separate software simulations will be run to check whether the order of operations and the routing of parts suggested by the generated controller are actually executable on the production system layout (as in [34]). In contrast, in our approach, the layout of the production system is modelled explicitly, and the order of operations and routing of parts specified by the controller is guaranteed to be executable by the production system.…”

Section: State Of the Artmentioning

confidence: 99%

“…A number of governmentbacked initiatives have arisen in recent years to support this trend, including the German Industrie 4.0 initiative [4,37], EU EFFRA Roadmap [2], US DMDII Strategic Plan [5], and the recent Japanese 'Connected Manufacturing' Industrial Value Chain Initiative [3]. Academic research in the area includes reconfigurable manufacturing systems [38,22] with 'plug and produce' technologies [47,9], holonic manufacturing [39,41,10], organic computing [44,14], and the related evolvable production systems and evolvable assembly systems [46,45,47,16,48], among others.…”

Section: Introductionmentioning

confidence: 99%

“…As in [11], the data structures manipulated by the algorithms are specified in a textual encoding using the ISO-standard EBNF (Extended Backus-Naur Form) [1] notation. To evaluate the scalability of topology generation, we performed experiments to determine the time required to generate the topology as the number of assembly resources is increased in models of two practical production facilities: the SMC Pneumatics HAS-200 training platform described in [16], and the Precision Assembly Demonstrator (PAD) described in [11,9]. Our results suggest that topology generation is feasible for small to medium sized transformable assembly systems.…”

Section: Introductionmentioning

confidence: 99%

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Synthesising process controllers from formal models of transformable assembly systems

Silva¹,

Felli²,

Sanderson³

et al. 2019

Robotics and Computer-Integrated Manufacturing

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When producing complex and highly customisable products in low volumes (or in 'batch sizes of one'), automation of production systems is critical for competitiveness and profitability in high labour-cost economies. To facilitate batch-size-of-one production, 'topology generation', 'realisability', and 'control' algorithms have been developed as part of the Evolvable Assembly Systems (EAS) project. The topology generation algorithm computes all the possible sequences of parallel activities that assembly resources can perform on parts and is run offline whenever the layout of the production facility changes, whereas realisability checking and controller generation are performed at run-time to check whether a production facility with a given set of assembly resources can assemble a desired product, and how the product should be assembled, e.g., which resources to use, and when. Generated controllers are output in Business to Manufacturing Markup Language (B2MML). Taken together, the algorithms thus represent a step toward a complete path from the formal specification of an assembly system and the products to be assembled, to the automated synthesis of executable process plans. This paper presents each algorithm in sufficient detail to allow their reimplementation by other researchers. Topology generation is the most expensive step in the approach. A preliminary experimental evaluation of the scalability of topology generation is presented, which suggests that, for small to medium sized production facilities, the time required for recomputing the topology is sufficiently small not to preclude frequent factory transformations, e.g., the addition of new resources.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synthesising process controllers from formal models of transformable assembly systems

Silva¹,

Felli²,

Sanderson³

et al. 2019

Robotics and Computer-Integrated Manufacturing

Self Cite

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“…A hierarchical collaborative system with agent technology had been developed by the authors to increase the efficiency of RMSs. Antzoulatos et al (2017) presented an agent framework for industrial assembly systems in order to deal with frequent changing resources and its capabilities and product specifications.…”

Section: The Concept Of Agent Technology In Manufacturing Systemsmentioning

confidence: 99%

Analysis of critical drivers affecting implementation of agent technology in a manufacturing system

et al. 2018

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Technological advancement in the manufacturing system in current scenario is inevitable due to today's customer-driven and volatile nature of the market. Implementation of agent technology in a manufacturing system increases flexibility which handles uncertainty generated due to advance technology. Therefore, in this paper, the critical drivers affecting implementation of agent technology are identified and the relationships among them are analysed for a case study of a manufacturing system in an Indian steering wheel manufacturing company. Interpretive structural modelling (ISM) is used to provide binary relationships among identified critical drivers (CDs), while MICMAC approach describes sensitive analysis of driving and dependence behaviour of CDs. The classification of the drivers affecting agent technology and their relationships according to ISM-MICMAC approach provides importance to this study. A structural model is developed for providing rank to the identified critical drivers, and driving-dependent power diagram is presented for analysing the behaviour of different critical drivers with respect to others. The identification of the most influential CDs that lead to increase the effect of other drivers is the major finding of this study. Finally, the implication of this research for the industries is also described.

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Towards a Cloud-Based Analytics Framework for Assembly Systems

Terrazas

Silva

Ratchev

2018

Precision Assembly in the Digital Age

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Advanced digitalization together with the rise of cloud technologies is a key enabler for a fundamental paradigm shift known as Industry 4.0 which proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation and optimization of factories. With the democratization of sensors, assembly systems can now be sensorized and the data generated by these devices can be exploited, for instance, to monitor their utilization, operations and maintenance. However, analyzing the vast amount of generated data is resource demanding both in terms of computing power and network bandwidth, especially when dealing with real-time changes to product, process and resource domains. This paper presents a novel cloud-based analytics framework for the management and analysis of assembly systems. It brings together standard open source technologies and the exploitation of cloud computing which as a whole can be adapted to and deployed on different cloud providers, thereby reducing infrastructure costs, minimizing deployment difficulty and providing on-demand access to virtually infinite computing power, storage and network resources.

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A multi-agent framework for capability-based reconfiguration of industrial assembly systems

Cited by 29 publications

References 23 publications

Synthesising process controllers from formal models of transformable assembly systems

Synthesising process controllers from formal models of transformable assembly systems

Analysis of critical drivers affecting implementation of agent technology in a manufacturing system

Towards a Cloud-Based Analytics Framework for Assembly Systems

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