Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning A
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https://doi.org/10.1007/s11837-020-04428-6 Ó 2020 The Minerals, Metals & Materials Society
ADDITIVE MANUFACTURING FOR ENERGY APPLICATIONS
Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning Algorithms XIN ZHANG
,1,2 JAFAR SANIIE
,2 and ALEXANDER HEIFETZ
1,3
1.—Nuclear Science and Engineering Division, Argonne National Laboratory, Argonne, IL, USA. 2.—Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA. 3.—e-mail: [email protected]
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 microbolometer 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.
INTRODUCTION Additive manufacturing (AM) is an emerging method for cost-efficient production of low-volume custom and unique parts with minimal supplychain dependence.1 In particular, AM potentially provides a cost-saving option for replacing aging nuclear reactor parts and reducing costs for new construction of advanced reactors.2 Metals of interest for passive structures in nuclear applications typically include high-strength corrosion-resistant stainless steel alloys, such as stainless steel 316/ 316L (SS316L). Because of high strength, with typical values of 205 MPa tensile yield strength and 515 MPa ultimate tensile strength, shape forming of SS316L into complex geometry structures is not trivial. AM of such metals, which have a melting range 1370°C to 1430°C, is currently based on laser powder-bed fusion (LPBF).3,4 Because of the intrinsic features of LPBF, e.g., Marangoni convection, (Received July 20, 2020; accepted September 29, 2020)
material defects such as porosity and anisotropy can appear in the metallic structure.5 Porosity can be introduced into AM parts due to incomplete melting of the powder particles or insufficient overlapping of the melt pools.6 Oscillations in the surface of the melt pool caused by rapid heating and cooling result in powder ejection and splattering of the melt, resulting in surface roughness and porosity.7 Furthermore, improper cooling rates can cause the formatio
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