Digital Twin-Driven Approach for Smart Industrial Product Design

Abadi, Mohammed; Abadi, Chaimae; Abadi, Asmae; Ben-Azza, Hussain

doi:10.1007/978-3-031-07969-6_20

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…The abo v e procedure can be implemented using Tensorflow (Abadi et al 2016 ), a freely available software framework for machine learning. It provides us with the tools and resources needed to develop and implement various machine-learning algorithms and models.…”

Section: Neural Networkmentioning

confidence: 99%

Modelling force-free neutron star magnetospheres using physics-informed neural networks

Urbán

Stefanou

Dehman

et al. 2023

Monthly Notices of the Royal Astronomical Society

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Using Physics-Informed Neural Networks (PINNs) to solve a specific boundary value problem is becoming more popular as an alternative to traditional methods. However, depending on the specific problem, they could be computationally expensive and potentially less accurate. The functionality of PINNs for real-world physical problems can significantly improve if they become more flexible and adaptable. To address this, our work explores the idea of training a PINN for general boundary conditions and source terms expressed through a limited number of coefficients, introduced as additional inputs in the network. Although this process increases the dimensionality and is computationally costly, using the trained network to evaluate new general solutions is much faster. Our results indicate that PINN solutions are relatively accurate, reliable, and well-behaved. We applied this idea to the astrophysical scenario of the magnetic field evolution in the interior of a neutron star connected to a force-free magnetosphere. Solving this problem through a global simulation in the entire domain is expensive due to the elliptic solver’s needs for the exterior solution. The computational cost with a PINN was more than an order of magnitude lower than the similar case solved with a finite difference scheme, arguably at the cost of accuracy. These results pave the way for the future extension to 3D of this (or a similar) problem, where generalised boundary conditions are very costly to implement.

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Section: Neural Networkmentioning

confidence: 99%

Modelling force-free neutron star magnetospheres using physics-informed neural networks

Urbán

Stefanou

Dehman

et al. 2023

Monthly Notices of the Royal Astronomical Society

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“…This justifies the proposition of the digital twin's standard structure given by Michael Grieves and John Vickers. In fact, they proposed to generalize the structure (physical entity, virtual counterpart, connection between physical and virtual spaces) on digital twins [8]. Afterwards, this structure was extended, and thus, two other elements (services and digital twin data model) were added [9] to make the structure more complete and efficient.…”

Section: Related Workmentioning

confidence: 99%

A Smart Decision Making System for the Optimization of Manufacturing Systems Maintenance using Digital Twins and Ontologies

Abadi¹,

Abadi²,

Abadi³

et al. 2022

IJACSA

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Now-a-days manufacturing processes are becoming more and more complex which constantly complicate the management of their life cycle. Although, in order to survive and maintain a good position in the competitive industrial context, industrials have understood that they must optimize the whole life cycle of their manufacturing processes. The maintenance constitutes one of the key processes indispensable to ensure the proper functioning and to optimize the lifetime of machines and production lines, and thus to optimize quality and production costs. Therefore, its automation and optimization represent until now a center of interest for researches and manufacturers, especially those related to the integration of artificial intelligence tools in the industry. In this context, several new concepts and technologies have emerged, particularly in the context of industry 4.0. One of these new concepts is digital twins, which has become a promising direction to optimize manufacturing processes lifecycle. However, the implementation of this technology faces several complex problems related to the interoperability between physical entities and their virtual counterparts, as well as to the logical reasoning between the different elements constituting the digital twin. It is in this context that an approach based on digital twins and ontologies is proposed. The originality of this paper lies in two important points: the first is the exploitation of the expressiveness and reasoning capabilities of ontologies to solve cyber-physical interoperability problems at the digital twin level, while the second is the automation of the whole maintenance process and its decision making key points using the inference potentialities of ontologies. The applicability and effectiveness of the proposed approach is validated through an industrial case of study.

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Ensuring the Connection Between Physical and Virtual Models in the Context of Digital Twins

Abadi

et al. 2023

Lecture Notes in Networks and Systems

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Digital Twin-Driven Approach for Smart Industrial Product Design

Cited by 6 publications

References 22 publications

Modelling force-free neutron star magnetospheres using physics-informed neural networks

Modelling force-free neutron star magnetospheres using physics-informed neural networks

A Smart Decision Making System for the Optimization of Manufacturing Systems Maintenance using Digital Twins and Ontologies

Ensuring the Connection Between Physical and Virtual Models in the Context of Digital Twins

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