Qualification pathways for additively manufactured components for nuclear applications

Hensley, Chris; Sisco, Kevin; Beauchamp, Serena; Godfrey, Amy; Rezayat, H.; McFalls, T.; Galicki, D.; List, F.A.; Carver, Keith; Stover, Carl P.; Gandy, David; Babu, S. S.

doi:10.1016/j.jnucmat.2021.152846

Cited by 26 publications

(12 citation statements)

References 85 publications

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“…In addition, modern techniques of component fabrication (AM, HIP) inherently require the qualification of the whole component, because the properties of the material become linked to the method and the process used for its fabrication. This requirement involves the development of suitable standards, that currently only partly exist, especially in the case of next generation reactors and relevant materials [155].…”

Section: Materials and Components' Qualificationmentioning

confidence: 99%

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Malerba

Mazouzi²,

Bertolus

et al. 2022

Energies

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Nuclear energy is presently the single major low-carbon electricity source in Europe and is overall expected to maintain (perhaps eventually even increase) its current installed power from now to 2045. Long-term operation (LTO) is a reality in essentially all nuclear European countries, even when planning to phase out. New builds are planned. Moreover, several European countries, including non-nuclear or phasing out ones, have interests in next generation nuclear systems. In this framework, materials and material science play a crucial role towards safer, more efficient, more economical and overall more sustainable nuclear energy. This paper proposes a research agenda that combines modern digital technologies with materials science practices to pursue a change of paradigm that promotes innovation, equally serving the different nuclear energy interests and positions throughout Europe. This paper chooses to overview structural and fuel materials used in current generation reactors, as well as their wider spectrum for next generation reactors, summarising the relevant issues. Next, it describes the materials science approaches that are common to any nuclear materials (including classes that are not addressed here, such as concrete, polymers and functional materials), identifying for each of them a research agenda goal. It is concluded that among these goals are the development of structured materials qualification test-beds and materials acceleration platforms (MAPs) for materials that operate under harsh conditions. Another goal is the development of multi-parameter-based approaches for materials health monitoring based on different non-destructive examination and testing (NDE&T) techniques. Hybrid models that suitably combine physics-based and data-driven approaches for materials behaviour prediction can valuably support these developments, together with the creation and population of a centralised, “smart” database for nuclear materials.

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Section: Materials and Components' Qualificationmentioning

confidence: 99%

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Malerba

Mazouzi²,

Bertolus

et al. 2022

Energies

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“…The point distance, hatch spacing, powder slice thickness, and scan rotation are geometric constraints that will not significantly affect the actual melt pool formed by the laser. Therefore, the melt pool formed will be most influenced by the laser power and dwell time of the laser, which guides the laser speed and the local energy density [13,22]. The laser power could not be increased past 195 W on the Renishaw, so dwell time was chosen as the best parameter to vary to explore higher and lower energy inputs.…”

Section: Effect Of Lpbf Processing On Sample Fabricationmentioning

confidence: 99%

Embedding Sensors in 3D Printed Metal Structures

Hyer¹,

Carver²,

List³

et al. 2021

Self Cite

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“…AM of high-strength metals, which have melting temperatures above 1300 o C, is currently based on laser powder-bed fusion (LPBF) method [2]. Because of the intrinsic features of LPBF, keyhole and lack of fusion microscopic pores can appear in the AM metal [3]. Before deployment in a nuclear reactor, nondestructive evaluation (NDE) of an AM structure needs to be performed to identify possible flaws.…”

Section: Introductionmentioning

confidence: 99%

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

et al. 2022

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Additive manufacturing (AM) of high-strength metals, which is typically based on laser powder bed fusion (LPBF), can introduce microscopic pores in the AM metal. Pulsed Infrared Thermography (PIT) offers several advantages for nondestructive imaging of subsurface defects in AM structures because the method is one-sided, non-contact and scalable to structures of arbitrary size. However, high-resolution PIT imaging results in the generation of a large volume of thermography data (~TB), which creates challenges for the storage and transmission of data. Compression of thermography data requires an approach that achieves high data compression ratio while preserving weak thermal features corresponding to microscopic material defects. We investigate thermography data compression using several unsupervised learning (UL) algorithms, which include Principal Component Analysis, Independent Component Analysis, Exploratory Factor Analysis, Sparse Dictionary Learning, and a novel lightweight Thermography Compressive Sparse Autoencoder (TCSA). Algorithms are benchmarked using PIT experimental data obtained from imaging of a stainless steel plate with calibrated porosity defects imprinted with AM process. For all algorithms, we obtain compression ratio > 30 (highest compression of 46 is achieved with TCSA), and peak signal-to-noise ratio for reconstruction accuracy > 73dB. Compared to existing methods, advantages of UL algorithms include achieving high compression ratio while preserving weak features to allow extraction of microscopic material defects from images. UL-based methods have general applicability because they are adaptable to compression of different data types, and allow for memory-efficient training and rapid on-line augmentation of the model.

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Qualification pathways for additively manufactured components for nuclear applications

Cited by 26 publications

References 85 publications

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Embedding Sensors in 3D Printed Metal Structures

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

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