An AAS Modeling Tool for Capability-Based Engineering of Flexible Production Lines

Huang, Yining; Dhouib, Saadia; Malenfant, Jacques

doi:10.1109/iecon48115.2021.9589329

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Two methods are used to classify them: one regards the stage in manufacturing deployment they contribute to, and the other relies on the technologies behind their feasibility-checking frameworks. First, factory operators can perform feasibility-checking practices at design, pre-production, or production stages [11]. This paper groups the design and pre-production stages as the engineering stage, meaning that they are all before the production stage.…”

Section: Related Workmentioning

confidence: 99%

Manufacturing 4.0: Checking the Feasibility of a Work Cell Using Asset Administration Shell and Physics-Based Three-Dimensional Digital Twins

Nguyen,

Huang,

Keith

et al. 2024

Machines

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Feasibility checking is a step in manufacturing system engineering for verifying the normalization and effectiveness of a manufacturing system associated with a specific configuration of resources and processes. It enables factory operators to predict problems before operational time, thus preventing equipment and machinery accidents and reducing labor waste in physically organizing the shop floor. In Industry 4.0, feasibility checking becomes even more critical since emerging challenges, such as mass personalization, require reconfiguring work cells quickly and flexibly on demand. Regarding this need, digital twin technologies have emerged as an ideal candidate for practicing feasibility checking. Indeed, they are tools used to implement digital representations of manufacturing entities that can constitute a digital environment and context. Factory operators can test a manufacturing process within a digital environment in different contexts before the execution with physical resources. This approach currently receives significant attention from the manufacturing community; however, there is still a lack of sharing experiences to implement it. Thus, this paper contributes a methodology to engineer a digital environment and context for a manufacturing work cell using AAS digital twins and physics-based 3D digital twins technologies. Technically, this methodology is a specific case of N-DTs, a general methodology for engineering heterogeneous digital twins. The product assembly line case study, also presented in this paper, is a successful experiment applying the above contributions. The two methodologies and the case study can be helpful references for both public and private sectors to deploy their feasibility-checking frameworks and deal with heterogeneous digital twins in general.

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Section: Related Workmentioning

confidence: 99%

Manufacturing 4.0: Checking the Feasibility of a Work Cell Using Asset Administration Shell and Physics-Based Three-Dimensional Digital Twins

Nguyen,

Huang,

Keith

et al. 2024

Machines

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“…A robotic cell (LocalSEA) use case is now presented to demonstrate the entire process of capability checking. The AAS modeling of this example has already been described in [3]. In this scenario, a new product has been designed and the system architect wants to configure the production line with the help of Papyrus4Manufacturing toolset.…”

Section: Robotic Cell Use Casementioning

confidence: 99%

“…Besides well-known challenges in the supervisory control of complex cyber-physical systems, providing a comprehensive representation of produces, production plans and plant resources in an interoperable way among heterogeneous equipment represents major challenges. To address these challenges, this vision of future flexible plants is currently supported by technical solutions such as (1) the digital twin technology to design structural and behavioral models used at runtime, (2) interoperability standards, such as the Asset Administration Shell (AAS) [2] promoted by I4.0 as a standard interface to digitally represent all of I4.0 elements as well as (3) capabilitybased engineering (CBE) [3] which aims at representing and reasoning about production activities.…”

Section: Introductionmentioning

confidence: 99%

“…This article follows up our previous work [7], where we mainly introduced how to annotate semantic meaning to AAS models, including a specific mapping between the MaRCO [8] ontology concepts and AAS meta-models. The focus of this article is to demonstrate our ontology-based implementation of the automatic AAS capability checking process in "Papyrus for Manufacturing" toolset [3]. The resulting process enables the semantic interoperability of AAS models, hence a concrete realization of capability-based engineering.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework

Huang

Dhouib

Medinacelli

et al. 2023

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

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Industry 4.0 currently prepares a major shift towards extreme flexibility into production lines management. Digital Twins are one of the key enabling technologies for Industry 4.0. However, the interoperability gap among digital representation of Industry 4.0 assets is still one of the obstacles to the development and adoption of digital twins. If the Asset Administration Shell (AAS), the standard proposed to represent the I4.0 components, caters for syntactic interoperability, a more semantic kind of interoperability is deeply needed to develop flexible and adaptable production lines. In our work, we overcome the limitation of current syntactic-only resource matching algorithms by implementing semantic interoperability based on ontologies i.e., by transforming AAS-based plant models into MaRCO (Manufacturing Resource Capability Ontology) instances and then query the expanded ontology to find the needed resources. This article presents this ontology-based approach as the first step towards the design and implementation of an automated I4.0 flexible plant supervision and control system based on modeldriven engineering (MDE) within the "Papyrus for Manufacturing" toolset. We show how an MDE approach can aggregate around digital twin modeling tools from the Papyrus platform both I4.0 technologies and AI (Knowledge Representation and Reasoning) tools. Our platform aligns modeling and ontological elements to get the best of both worlds. This method has two main advantages: (1) to provide semantic descriptions for digital twin models, (2) to complement model-driven engineering tools with automated reasoning. This paper showcases this approach through a robotic cell use case.

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“…The practical significance of AAS is large, as it can be used to transform a factory into an easily re-configurable manufacturing system (also known as plug and produce) [ 223 , 224 ] that is flexible [ 225 ] and is capable of dynamically orchestrating and allocating resources [ 226 ]. It can also have significant implications for preventive maintenance [ 227 ], product customization, and the design and development of upgradable production lines and order-controlled production.…”

Section: Realizing the Promise Of Industry 40 Through The Digitizatio...mentioning

confidence: 99%

A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

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In a dynamic and rapidly changing world, customers’ often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities that represent a significant change in current production capabilities, models, and practices. Using the semi-narrative research methodology in concert with the snowballing approach, the authors reviewed the open literature to understand the organizing principles behind the most common smart manufacturing paradigms with a view to developing a creative reference that articulates their shared characteristics and features under a collective lingua franca, viz., Factory of the Future. Serving as a review article and a reference monograph, the paper details the meanings, characteristics, technological framework, and applications of the modern factory and its various connotations. Amongst other objectives, it characterizes the next-generation factory and provides an overview of reference architectures/models that guide their structured development and deployment. Three advanced communication technologies capable of advancing the goals of the Factory of the Future and rapidly scaling advancements in the field are discussed. It was established that next-generation factories would be data rich environments. The realization of their ultimate value would depend on the ability of stakeholders to develop the appropriate infrastructure to extract, store, and process data to support decision making and process optimization.

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An AAS Modeling Tool for Capability-Based Engineering of Flexible Production Lines

Cited by 5 publications

References 12 publications

Manufacturing 4.0: Checking the Feasibility of a Work Cell Using Asset Administration Shell and Physics-Based Three-Dimensional Digital Twins

Manufacturing 4.0: Checking the Feasibility of a Work Cell Using Asset Administration Shell and Physics-Based Three-Dimensional Digital Twins

Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework

A Primer on the Factories of the Future

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