An Ontology-based Engineering system to support aircraft manufacturing system design

Arista, Rebeca; Zheng, Xiaochen; Lu, Jinzhi; Mas, Fernando

doi:10.1016/j.jmsy.2023.02.012

Cited by 36 publications

(4 citation statements)

References 27 publications

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“…Several of the methods are captured in the standard ISO/IEC/IEEE 15288 (2023). However, these methods have not yet been fully adopted by the manufacturing engineering community (Arista et al, 2023). Stark et al (2017) state: "Today's manufacturing system design processes and architecture are still based on traditional engineering methods and can hardly cope with increased system complexity".…”

Section: Frame Of Referencementioning

confidence: 99%

A proposed framework using systems engineering to design human-centric manufacturing systems for novel products to reduce complexity and risk

Hagström,

Bergsjö

2024

Proc. Des. Soc.

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The environment for powertrain production system engineers is changing radically. This initial prescriptive study proposes a systems engineering framework based on two previous case studies which are under review for publication concerning design of battery plants. The framework was developed based on ISO/IEC/IEEE 15288 standard using Concept of Operations and Model-Based Systems Engineering in a workshop setting, with a focus on visualisation to understand the practical and emotional needs of the humans in the system. The framework was validated by twelve senior project members.

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Section: Frame Of Referencementioning

confidence: 99%

A proposed framework using systems engineering to design human-centric manufacturing systems for novel products to reduce complexity and risk

Hagström,

Bergsjö

2024

Proc. Des. Soc.

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“…Thereby, the orchestration of the interactions between subsystems and processes is enabled (Wagg et al, 2020). Semantic technology enables the verification of existing metadata, knowledge inference, and the creation of new knowledge via rule-based reasoners, thus providing cognitive capabilities for CDT-type systems (Zheng et al, 2021;Arista et al, 2023). Additionally, ontologies can be used to describe model interfaces for simulations used in digital twins.…”

Section: Digital Twinsmentioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

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“…Regardless of a firm size or market power, applying knowledge representation mechanisms will help to manage complexity and drive productivity gains. The early works' potential of ontological relationships and axioms to support reasoning has been a subject of renewed interest in Industry 4.0 research for an automated team, product, and process composition [2][3][4][5], resource management for aerospace manufacturing [6], aircraft manufacturing system design [7], Reconfigurable Manufacturing System design [8], and supporting demanddriven collaborations in low volume high variability manufacturing [9].…”

Section: Thematic Backgroundmentioning

confidence: 99%

Investigating multi-level ontology to support manufacturing during demand fluctuation

Kazantsev,

Niewiadomski,

Martínez-Arellano

et al. 2023

IET Conf. Proc.

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Responding to demand fluctuations represents a challenge for manufacturers, as they often face resource limitations and cannot assess all potential solutions. This paper presents a low-cost semantic engineering solution (ontology) to coordinate potential responses to disruption at the supply chain, factory and firm levels. The research method uses three cycles of design science research to develop a multi-level ontology and assess its potential to coordinate responses. The feedback from companies positions ontology as a solution for stress-testing next generation manufacturing systems. Using semantic engineering, companies can assess the available responses before disruptions, plan potential responses, and support their decision-making during demand fluctuation.

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An Ontology-based Engineering system to support aircraft manufacturing system design

Cited by 36 publications

References 27 publications

A proposed framework using systems engineering to design human-centric manufacturing systems for novel products to reduce complexity and risk

A proposed framework using systems engineering to design human-centric manufacturing systems for novel products to reduce complexity and risk

Knowledge engineering for wind energy

Investigating multi-level ontology to support manufacturing during demand fluctuation

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