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Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

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Abstract

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

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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.

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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

  3. Fuel Fabrication Facility, Westinghouse Electric Company, Columbia, SC, USA

    William Cleary

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  1. Xin Zhang
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  2. Jafar Saniie
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  3. William Cleary
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  4. Alexander Heifetz
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Correspondence to Alexander Heifetz.

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Zhang, X., Saniie, J., Cleary, W. et al. Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images. JOM 72, 4682–4694 (2020). https://doi.org/10.1007/s11837-020-04408-w

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  • DOI: https://doi.org/10.1007/s11837-020-04408-w

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