Capability-Based Adaptation of Production Systems - Practical Case Study in TUT-Microfactory Environment

Järvenpää, Eeva; Lanz, Minna; Tuokko, Reijo

doi:10.4028/www.scientific.net/kem.572.245

Cited by 8 publications

(16 citation statements)

References 4 publications

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“…installation of new equipment), and dynamic adaptations performed "on-line" when the system is running, consisting of short-term changes to alter, for example, scheduling or routing. Static adaptations are normally planned by human experts or through automatic planning methods, while dynamic adaptations are carried out autonomously by self-organising systems able to react to changing environments (Järvenpää 2012;Westkämper 2006). A capabilitybased adaptation methodology that matches product requirements and available system capabilities is proposed by Järvenpää (2012) to support both human planning and reactive systems.…”

Section: Adaptationmentioning

confidence: 99%

A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations

Scrimieri

Ratchev

2014

J Control Autom Electr Syst

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We present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions.

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Section: Adaptationmentioning

confidence: 99%

A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations

Scrimieri

Ratchev

2014

J Control Autom Electr Syst

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“…The conceptual model of capabilities was initially presented in (Järvenpää et al 2011Järvenpää 2012). The main concepts of the capability model are presented in Fig.…”

Section: (D) Competency Questionsmentioning

confidence: 99%

The development of an ontology for describing the capabilities of manufacturing resources

et al. 2018

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Today's highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model's content and structure.

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“…Therefore, some artificial simplifications are made during the creation of the instantiated capability model. For more information about capability parameters, please refer to (Järvenpää 2012). Secondly, the capability modularization aims to provide reusability of the capabilities among different types of resources.…”

Section: Titlementioning

confidence: 99%

“…Crucial factor for adaptation planning are formal models which convey the needed up-todate product, process and system related information to the planning activities (Järvenpää, 2012). This paper presents an adaptation approach, which core lies on capability-based matching of product requirements and systems that are used to manufacture these.…”

Section: Introductionmentioning

confidence: 99%

Application of a capability-based adaptation methodology to a small-size production system

Järvenpää

Lanz

Tuokko

2016

IJMTM

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Capability-Based Adaptation of Production Systems - Practical Case Study in TUT-Microfactory Environment

Cited by 8 publications

References 4 publications

A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations

A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations

The development of an ontology for describing the capabilities of manufacturing resources

Application of a capability-based adaptation methodology to a small-size production system

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