An ontology-based, general-purpose and Industry 4.0-ready architecture for supporting the smart operator (Part I – Mixed reality case)

Longo, Francesco; Mirabelli, G.; Nicoletti, Letizia

doi:10.1016/j.jmsy.2022.08.002

Cited by 19 publications

(6 citation statements)

References 66 publications

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“…These enhance system scalability in development, ensuring adaptability and feasibility to changing failure scenarios. Operation DT systems are no longer restricted to PC-based clients, but also operating on webs 54 , or VR/AR devices 98 , 99 . Cross-platform compatibility enables DTs to run on multiple operating systems and hardware.…”

Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

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Digital twin-driven prognostics and health management for industrial assets

Xiao,

Zhong,

Bao

et al. 2024

Sci Rep

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As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

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Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

“…DT systems are no longer restricted to PC-based clients, but also operating on webs 54 , or VR/AR devices 98 , 99 . Cross-platform compatibility enables DTs to run on multiple operating systems and hardware.…”

Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

Digital twin-driven prognostics and health management for industrial assets

Xiao,

Zhong,

Bao

et al. 2024

Sci Rep

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“…The conducted evaluation shows, that OntoPhaco has the potential to improve the learning experience of students and facilitate the development of VR training in the future. There is also an example of ontology-based, generalpurpose and Industry 4.0-ready architecture to use with systems supporting factory workers that use mixed reality [17]. In the paper, authors describe a general ontology, that is capable of structuring knowledge to enable interoperability and standardization between such systems.…”

Section: Approachmentioning

confidence: 99%

Knowledge-Based Creation of Industrial VR Training Scenarios

Sobociński,

Flotyński,

Śliwicki

et al. 2023

Annals of Computer Science and Information Systems

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The application of virtual reality (VR) for building training systems has grown in popularity across diverse fields. This trend is particularly prevalent in Industry 4.0, where many real-world training scenarios can be expensive or pose potential dangers to trainees. The most important aspect of professional training is domain-specific knowledge, which can be expressed using the semantic web approach. This approach facilitates complex queries and reasoning against the representation of training scenarios, which can be useful for educational purposes. However, current methods and tools for creating VR training systems do not utilize semantic knowledge representation, making it difficult for domain experts without IT expertise to create, modify, and manage training scenarios. To address this issue, we propose an ontology-based representation and a method of modeling VR training scenarios. We demonstrate our approach by modeling VR training scenarios for Industry 4.0 in the field of the production of household equipment. The domain knowledge used represents training activities, potential errors, and equipment failures in a way comprehensible to domain experts.

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“…For instance, Ref. [10] proposed a general-purpose and Industry 4.0-based architecture to enable smart factories, with a focus on the smart operator. Furthermore, some other attempts, such as [11,12], offer interesting perspectives in providing guidelines to future implementations.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

Goecks,

Habekost,

Coruzzolo

et al. 2024

ASI

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Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems.

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An ontology-based, general-purpose and Industry 4.0-ready architecture for supporting the smart operator (Part I – Mixed reality case)

Cited by 19 publications

References 66 publications

Digital twin-driven prognostics and health management for industrial assets

Digital twin-driven prognostics and health management for industrial assets

Knowledge-Based Creation of Industrial VR Training Scenarios

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

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