Neutron interferometry detection of early crack formation caused by bending fatigue in additively manufactured SS316 dogbones

Brooks, Adam; Hussey, Daniel S.; Yao, Hong; Haghshenas, Ali; Yuan, Jumao; LaManna, Jacob M.; Jacobson, David L.; Lowery, Caroline G.; Kardjilov, Nikolay; Guo, Shengmin; Khonsari, M. M.; Butler, Leslie G.

doi:10.1016/j.matdes.2017.12.001

Cited by 29 publications

(12 citation statements)

References 40 publications

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“…The powder-bed 3D printers are optimized to work only with a handful of powders and the parts built using such printers have a rough exterior and porous interior. Collecting and analyzing the optical scan imaging data of the 3D printed metal parts provides mesoscale information that we either can't see or may not know what to look for (Brooks et al, 2018).…”

Section: Collecting Mesoscale Informationmentioning

confidence: 99%

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

Banadaki¹

2019

JCSIT

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Additive manufacturing (AM) is a crucial component of a smart factory that promises to change traditional supply chains. However, the parts built using state-of-the-art 3D printers have noticeable unpredictable mechanical properties. In this paper, a machine learning (ML) model is proposed as a promising approach to improve the underlying failure phenomena in the AM process. The paper also describe how a ML model can be distributed to form an interactive learning network of smart AM components to fulfil the Industry 4.0 requirements including self-organization, distributed control, communication, and real-time decision-making capability.Currently, almost all AM machines have only limited sensing capabilities that are mostly inaccessible to the users, or completely "open-loop" system operating without any feedback measurement systems for correction during the process. However, future AM machines must be a smart system that can perform self-monitoring, self-calibrating, and quality self-controlling in real-time. The gap between the smart factory and existing manufacturing systems can be bridged concerning the automation, flexibility, and reconfigurability of AM machines in an interactive distributed network as a natural way of scaling up learning algorithms.

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Section: Collecting Mesoscale Informationmentioning

confidence: 99%

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

Banadaki¹

2019

JCSIT

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“…Already that has enabled a number of distinguished studies visualizing e.g. structural flaws in engineering materials 5–9 and in particular magnetic domain structures in the volume of bulk materials 10–17 , where they are not assessable by any other means. However, based on the small-angle scattering theory a description of dark-field contrast became available 18 , which in analogy to spin-echo small-angle scattering, enables quantitative assessment of the scattering structures 19–24 .…”

Section: Introductionmentioning

confidence: 99%

“…Extending the range through variation of the wavelength is limited by the severe loss of visibility when moving away from the design wavelength of this monochromatic technique. Grating-based far-field interferometry 9,34 and spin-echo modulated dark-field imaging 35 enable to access different length scales in the nanometer regime, but these face different problems in extending to the micrometer scale in terms of sample-to-detector distances requiring extreme sample-to-detector distances impacting either spatial resolution or flux conditions severely.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Talbot-Lau neutron grating interferometry and incoherent scattering correction for quantitative dark-field imaging

Kim

Valsecchi

Kim

et al. 2019

Sci Rep

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We introduce the application of a symmetric Talbot-Lau neutron grating interferometer which provides a significantly extended autocorrelation length range essential for quantitative dark-field contrast imaging. The highly efficient set-up overcomes the limitation of the conventional Talbot-Lau technique to a severely limited micrometer range as well as the limitation of the other advanced dark-field imaging techniques in the nanometer regime. The novel set-up enables efficient and continuous dark-field contrast imaging providing quantitative small-angle neutron scattering information for structures in a regime from some tens of nanometers to several tens of micrometers. The quantitative analysis enabled in and by such an extended range is demonstrated through application to reference sample systems of the diluted polystyrene particle in aqueous solutions. Here we additionally demonstrate and successfully discuss the correction for incoherent scattering. This correction results to be necessary to achieve meaningful quantitative structural results. Furthermore, we present the measurements, data modelling and analysis of the two distinct kinds of cohesive powders enabled by the novel approach, revealing the significant structural differences of their fractal nature.

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“…Furthermore, the DFI contrast proved to be a powerful tool for the investigation of magnetic domains in particular in bulk ferromagnetic grain-oriented electrical steels and of magnetic phenomena in superconductors as well [30][31][32][33][34][35][36][37][38][39][40] . Another important application field of DFI is related to the porosity and early crack formation detection in engineering materials, becoming particularly important in additive manufacturing studies 23,41 .…”

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confidence: 99%

Characterization of oriented microstructures through anisotropic small-angle scattering by 2D neutron dark-field imaging

et al. 2020

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Within neutron imaging, different methods have been developed with the aim to go beyond the conventional contrast modalities, such as grating interferometry. Existing grating interferometers are sensitive to scattering in a single direction only, and thus investigations of anisotropic scattering structures imply the need for a circular scan of either the sample or the gratings. Here we propose an approach that allows assessment of anisotropic scattering in a single acquisition mode and to broaden the range of the investigation with respect to the probed correlation lengths. This is achieved by a far-field grating interferometer with a tailored 2D-design. The combination of a directional neutron dark-field imaging approach with a scan of the sample to detector distance yields to the characterization of the local 2D real-space correlation functions of a strongly oriented sample analogous to conventional small-angle scattering. Our results usher in quantitative and spatially resolved investigations of anisotropic and strongly oriented systems beyond current capabilities.

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Neutron interferometry detection of early crack formation caused by bending fatigue in additively manufactured SS316 dogbones

Cited by 29 publications

References 40 publications

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

Symmetric Talbot-Lau neutron grating interferometry and incoherent scattering correction for quantitative dark-field imaging

Characterization of oriented microstructures through anisotropic small-angle scattering by 2D neutron dark-field imaging

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