2D linear finite element simulation of laser metal heating for digital twins

Montoya-Zapata, Diego; Rodríguez, Juan Manuel Corchado; Moreno, Aitor; Posada, Jorge; Ruiz-Salguero, Oscar

doi:10.1051/smdo/2021011

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The definition of a DT within AM is still controversial [ 37 ], with some researchers defining it as a virtual system that mimics a physical system [ 38 – 41 ], while others adopt a simulation-centric approach [ 42 – 45 ]. The physical behavior of FDM involves heat transfer, materials phase changes, and machine mechanics that complicate the development of DTs [ 46 , 47 ].…”

Section: Related Workmentioning

confidence: 99%

A digital twin ecosystem for additive manufacturing using a real-time development platform

Pantelidakis

Mykoniatis

Liu

et al. 2022

Int J Adv Manuf Technol

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Additive manufacturing is often used in rapid prototyping and manufacturing, allowing the creation of lighter, more complex designs that are difficult or too expensive to build using traditional manufacturing methods. This work considers the implementation of a novel digital twin ecosystem that can be used for testing, process monitoring, and remote management of an additive manufacturing–fused deposition modeling machine in a simulated virtual environment. The digital twin ecosystem is comprised of two approaches. One approach is data-driven by an open-source 3D printer web controller application that is used to capture its status and key parameters. The other approach is data-driven by externally mounted sensors to approximate the actual behavior of the 3D printer and achieve accurate synchronization between the physical and virtual 3D printers. We evaluate the sensor-data-driven approach against the web controller approach, which is considered to be the ground truth. We achieve near-real-time synchronization between the physical machine and its digital counterpart and have validated the digital twin in terms of position, temperature, and run duration. Our digital twin ecosystem is cost-efficient, reliable, replicable, and hence can be utilized to provide legacy equipment with digital twin capabilities, collect historical data, and generate analytics.

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Section: Related Workmentioning

confidence: 99%

A digital twin ecosystem for additive manufacturing using a real-time development platform

Pantelidakis

Mykoniatis

Liu

et al. 2022

Int J Adv Manuf Technol

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show abstract

“…In the area of additive manufacturing, very recent research articles and reviews show that the state of the art is still focusing on reduced order modelling. The obvious final aim is to control, in real-time, the 3D printing process (Afazov et al, 2022;Wei et al, 2021;Li and Polydorides, 2023;Montoya-Zapata et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A physics-driven and machine learning-based digital twinning approach to transient thermal systems

Di Meglio,

Massarotti,

Nithiarasu

2024

HFF

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Purpose In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system. Design/methodology/approach To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop. Findings The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems. Originality/value The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

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Digital twins in additive manufacturing: a state-of-the-art review

Shen,

2024

Int J Adv Manuf Technol

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With the development of Industry 4.0, additive manufacturing will be widely used to produce customized components. However, it is rather time-consuming and expensive to produce components with sound structure and good mechanical properties using additive manufacturing by a trial-and-error approach. To obtain optimal process conditions, numerous experiments are needed to optimize the process variables within given machines and processes. Digital twins (DT) are defined as a digital representation of a production system or service or just an active unique product characterized by certain properties or conditions. They are the potential solution to assist in overcoming many issues in additive manufacturing, in order to improve part quality and shorten the time to qualify products. The DT system could be very helpful to understand, analyze and improve the product, service system or production. However, the development of genuine DT is still impeded due to lots of factors, such as the lack of a thorough understanding of the DT concept, framework, and development methods. Moreover, the linkage between existing brownfield systems and their data are under development. This paper aims to summarize the current status and issues in DT for additive manufacturing, in order to provide more references for subsequent research on DT systems.

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2D linear finite element simulation of laser metal heating for digital twins

Cited by 6 publications

References 19 publications

A digital twin ecosystem for additive manufacturing using a real-time development platform

A digital twin ecosystem for additive manufacturing using a real-time development platform

A physics-driven and machine learning-based digital twinning approach to transient thermal systems

Digital twins in additive manufacturing: a state-of-the-art review

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