Additive Manufacturing in the Nuclear Reactor Industry

Betzler, Benjamin R.

doi:10.1016/b978-0-12-819725-7.00106-9

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…AM for nuclear applications increased significantly in the late 2010s because of rapid progress in technology, particularly with methods using metals and ceramics [5]. The US Department of Energy Office of Nuclear Energy Transformational Challenge Reactor (TCR) Program integrated many of the abovementioned attributes to accelerate the deployment of AM technologies to industry.…”

Section: List Of Figuresmentioning

confidence: 99%

In-Situ Monitoring Assisted Large-Scale Additive Manufacturing of Mild Steel and 316L Alloys for Nuclear Application

Nag,

Paramanathan,

List III

et al. 2023

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Section: List Of Figuresmentioning

confidence: 99%

In-Situ Monitoring Assisted Large-Scale Additive Manufacturing of Mild Steel and 316L Alloys for Nuclear Application

Nag,

Paramanathan,

List III

et al. 2023

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“…It also highlights the increased media exposure of these technologies since the COVID-19 pandemic, as the ability to create PPE (Personal Protective Equipment) rapidly made the technology very popular [8]. Moreover, this technology has already proven its ability to be fully implemented in the industry [9][10][11], being used to manufacture functional metallic parts and entering an increasing number of industrial fields, such as nuclear [12,13], construction [14,15], railway [16], and military applications [17]. Its capacity to create complex geometries and maintain good mechanical properties [18][19][20][21] while potentially saving materials and energy compared to conventional manufacturing strategies [22][23][24] has made it a widely researched technology.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Energy and Material Consumption for the Manufacturing of an Aeronautical Tooling: An Experimental Comparison between Pure Machining and Big Area Additive Manufacturing

Marqués,

Dieste,

Monzón

et al. 2024

Materials

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Additive manufacturing (AM) has been fully incorporated into both the academic and the industrial world. This technology has been shown to lower costs and environmental impacts. Moreover, AM-based technologies, such as wire arc additive manufacturing (WAAM), have been proven suitable for the manufacturing of large products with significant mechanical requirements. This study examines the manufacture of two aeronautical toolings: first, using conventional techniques, and second, using a big area additive manufacturing (BAAM) process, specifically WAAM technology, followed by second-stage hybrid machining. Both toolings can be considered interchangeable in terms of design and performance. Energy and material consumption were analysed and compared throughout both tooling procedures. The results show the important optimisation of both procedures in manufacturing WAAM tooling, encompassing the additive process and second-stage hybrid machining. Nevertheless, the time required for WAAM tooling manufacturing increased significantly compared to conventional manufacturing tooling. Moreover, based on metrology data from the AM process, a theoretical study was conducted to assess different design optimisations for WAAM tooling manufacturing and determine their influence on material and energy consumption. These theoretical results improve those already obtained regarding energy and raw material savings.

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“…Being considered as an advanced method of manufacturing products by adding materials layer-by-layer, additive manufacturing (AM) technologies can fabricate very complex components (M. K. Thompson et al 2016). Especially, metal AM (MAM) have been increasingly used in important industrial sectors such as aeronautics, automobile, shipbuilding, and tooling (Altıparmak and Xiao 2021;Betzler 2021;Madhavadas et al 2022). Together with powder-bed-fusion (PBF) AM technologies, directed-energy-deposition (DED) is an important technique in the MAM group (Le et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Efficient prediction of thermal history in wire and arc additive manufacturing combining machine learning and numerical simulation

Bui

Pham

et al. 2023

Int J Adv Manuf Technol

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Among metallic additive manufacturing technologies, wire and arc-directed energy deposition (WADED) is recently adopted to manufacture large industrial components. In this process, controlling the temperature evolution is very important since it directly influences the quality of the deposited parts. Typically, the temperature history in WADED can be obtained through experiments and/or numerical simulations, which are generally time-consuming and expensive. In this research, we developed a robust surrogate model (SM) for predicting the temperature history in WADED based on the combination of machining learning (ML) and finite element (FE) simulation. The SM model was built to predict the temperature history in the WADED of single weld tracks. For this purpose, FE model was first developed and validated against experiments. This validated FE model is then used to generate the data to train the ML modes based on the feed-forward neural network (FFNN). The trained SM model can fast and accurately predict the temperature history in the cases which were not previously used for training with a very high accuracy of more than 99% and in a very short time with only 38 s (after being trained) as compared with 5 h for a FE model. The trained SM can be used for studies that require a large number of simulations such as uncertainty quantification or process optimization.

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Additive Manufacturing in the Nuclear Reactor Industry

Cited by 8 publications

References 28 publications

In-Situ Monitoring Assisted Large-Scale Additive Manufacturing of Mild Steel and 316L Alloys for Nuclear Application

In-Situ Monitoring Assisted Large-Scale Additive Manufacturing of Mild Steel and 316L Alloys for Nuclear Application

Analysis of Energy and Material Consumption for the Manufacturing of an Aeronautical Tooling: An Experimental Comparison between Pure Machining and Big Area Additive Manufacturing

Efficient prediction of thermal history in wire and arc additive manufacturing combining machine learning and numerical simulation

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