Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning Algorithms

Zhang, Xin; Saniie, Jafar; Heifetz, Alexander

doi:10.1007/s11837-020-04428-6

Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning Algorithms

Journal Article · Mon Oct 19 00:00:00 EDT 2020 · JOM. Journal of the Minerals, Metals & Materials Society

DOI:https://doi.org/10.1007/s11837-020-04428-6· OSTI ID:1774602

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Argonne National Lab. (ANL), Argonne, IL (United States); Illinois Institute of Technology, Chicago, IL (United States)
Illinois Institute of Technology, Chicago, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on the laser powder bed fusion process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environments in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Here, we investigate TT of AM stainless steel 316L specimens with imprinted internal porosity defects using a relatively low-cost, small form factor infrared camera based on an uncooled micro-bolometer detector. Sparse coding-related K-means singular value decomposition machine learning, image processing algorithms are developed to improve the quality of TT images through removal of additive white Gaussian noise without blurring the images.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE), Nuclear Energy Enabling Technologies (NEET)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1774602

Journal Information:: JOM. Journal of the Minerals, Metals & Materials Society, Journal Name: JOM. Journal of the Minerals, Metals & Materials Society Journal Issue: 12 Vol. 72; ISSN 1047-4838

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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