Alignment of the product lifecycle management federated interoperability framework with internet of things and virtual manufacturing

Tchoffa, David; Figay, Nicolas; Ghodous, Parisa; Panetto, Hervé; Mhamedi, Abderrahman El

doi:10.1016/j.compind.2021.103466

Cited by 15 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its suitability for Industry 4.0 was already suggested 49 and adopted in digital interoperability representations. 50 Our model uses three layers, namely, the physical and technology layer (green), the application layer with digital solutions (blue), and the business layer (yellow), representing the main processes and business services. The physical layer (bottomright block) represents the infrastructure located inside the factory.…”

Section: System Architecturementioning

confidence: 99%

Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering

Barata

Cardoso

Cunha

2023

Systems Engineering

View full text Add to dashboard Cite

After mass production and then mass customization, the time is almost ripe for mass personalization. The goal is to offer unique products designed for the needs of each customer. However, production in larger series of products also has its advantages, and the promise of “lot size one” is still far from being the norm in several sectors of the economy. As a result of an action research project in a small household ceramic producer, this paper explores the potential of a hybrid strategy. Augmented digital engineering is adopted to (1) ensure customer participation along the entire product design lifecycle, (2) maintain the benefits of modularization and low cost, (3) minimize the waste of time and materials during product design, and (4) seek a minimum trade‐off between customer desires and engineering strategy. For theory, our work describes Industry 4.0 technology's role in achieving individual customer interaction and value co‐creation in hybrid strategies of mass customization and mass personalization. For practice, we present an example of technological architecture to implement augmented digital engineering in Industry 4.0, accessible to scenarios of hand‐intensive work and creative design processes.

show abstract

Section: System Architecturementioning

confidence: 99%

Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering

Barata

Cardoso

Cunha

2023

Systems Engineering

View full text Add to dashboard Cite

show abstract

“…Some Industry 4.0 tools like IoTs, Big data analytics, Blockchain, and web services support the adoption of PLM (Ghode et al, 2023). The application of IoT along with PLM would capitalize on this formerly unexploited area of improvements, like IoT allowing the collection and use of real-life product operating conditions data, mitigating the disconnection between real-life conditions and design specifications (Tchoffa et al, 2021). The data generated by this means is very high in terms of volume, variety, and rate, so, the use of big data analytics is vital to handle it (Lim et al, 2020;Holligan et al, 2017).…”

Section: Cloud Product Lifecycle Management Systemmentioning

confidence: 99%

Framework for cloud product lifecycle management system: a case study of an automotive industry

Tanpure,

Yadav,

Jain

et al. 2023

BPMJ

View full text Add to dashboard Cite

PurposeProduct Lifecycle Management (PLM) systems have gained wide popularity for their role in manufacturing organizations for creating, managing and distributing product data. These systems are one of various enterprise systems which are required for smooth functioning and meeting the scaling up requirements organization. However, with introduction of cloud technology and other industry 4.0 initiatives, there has been focus on moving the on-premises IT application to the cloud. Such a move needs to be carried out by identifying and evaluating various challenges. This paper aims to discuss the aforementioned objective.Design/methodology/approachThe challenges identified through literature review have also been confirmed to be present via interview, system observation and documentation review through case study-based validation in an automotive component manufacturing industry.FindingsThe article identifies needs and challenges of cloud PLM systems and presents a generic framework for developing an approach for cloud PLM journey for an organization. The article also provides an approach for resolving the different challenges to realizing the designed system.Originality/valueThe simplified generic framework has been presented for use by industry professionals and researchers for designing cloud PLM systems that would fulfill expectations of different levels of stakeholders.

show abstract

“…19 Presently, federated learning is utilized as a key technology for edge level and light machine learning to get utilized with complex cyber-physical architectures. 20 In the current situation, AI categorization of COVID-19-related medical health data, such as x-ray pictures, is in great demand for effective screening or diagnosis. However, transferring high-resolution pictures from one resource site to another is challenging and risky.…”

Section: Related Workmentioning

confidence: 99%

“…Huong et al utilized the efficacy and efficiency of federated training in the domain of cyber security by detecting anomalies in cyber data 19 . Presently, federated learning is utilized as a key technology for edge level and light machine learning to get utilized with complex cyber‐physical architectures 20 . In the current situation, AI categorization of COVID‐19‐related medical health data, such as x‐ray pictures, is in great demand for effective screening or diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Privacy protected internet of unmanned aerial vehicles for disastrous site identification

Khullar

Singh

2022

Concurrency and Computation

View full text Add to dashboard Cite

Disastrous site identification through the internet of UAVs is a current research area that aims to improve data sharing by connecting servers to the internet. Internet-connected unmanned aerial vehicles (UAVs) for aerial image classification necessitated the sharing of large datasets between the number of connected flying machines. The artificial intelligence model training to predict images need to compromise data privacy, consume a lot of energy, and high requirement of data communication. This article aims to propose and implement federated deep learning trained aerial image classification for disastrous sites through the internet of UAVs. The proposed model provides privacy-preserving and resource efficient deep learning by sharing only models rather than large datasets of images. Multisystem client-server federated learning UAV architecture was implemented and comparatively analyzed on basis of parameters namely, training-testing accuracy, train-testing loss, RAM-CPU utilization.

show abstract

Alignment of the product lifecycle management federated interoperability framework with internet of things and virtual manufacturing

Cited by 15 publications

References 14 publications

Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering

Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering

Framework for cloud product lifecycle management system: a case study of an automotive industry

Privacy protected internet of unmanned aerial vehicles for disastrous site identification

Contact Info

Product

Resources

About