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

  • Additive Manufacturing for Energy Applications
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Abstract

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. 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.

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Acknowledgements

This work was supported by the US Department of Energy, Office of Nuclear Energy, Nuclear Energy Enabling Technology (NEET) Advanced Methods of Manufacturing (AMM) program, under contract DE-AC02-06CH11357. We would like to acknowledge helpful discussions with Sasan Bakhtiari from Argonne National Laboratory, Boris Khaykovich from MIT Nuclear Laboratory and William Cleary from Westinghouse Electric Company.

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Authors and Affiliations

  1. Nuclear Science and Engineering Division, Argonne National Laboratory, Argonne, IL, USA

    Xin Zhang & Alexander Heifetz

  2. Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA

    Xin Zhang & Jafar Saniie

Authors
  1. Xin Zhang
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  2. Jafar Saniie
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  3. Alexander Heifetz
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Correspondence to Alexander Heifetz.

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Zhang, X., Saniie, J. & Heifetz, A. Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning Algorithms. JOM 72, 4244–4253 (2020). https://doi.org/10.1007/s11837-020-04428-6

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  • DOI: https://doi.org/10.1007/s11837-020-04428-6

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