Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143850
Title: Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography
Authors: Lu, Qingyang
Nguyen, Nhat V.
Hum, Allen Jun Wee
Tran, Tuan
Wong, Chee How
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Lu, Q., Nguyen, N. V., Hum, A. J. W., Tran, T., & Wong, C. H. (2020). Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography. Additive Manufacturing, 35, 101287-. doi:10.1016/j.addma.2020.101287
Journal: Additive Manufacturing 
Abstract: Additive manufacturing has opened doors for the efficient fabrication of individually tailored and complicated functional parts. However, the three-dimensional (3D) printing process is vulnerable to defects generation, necessitating the need for in-situ monitoring and control technologies for quality assessment of parts. An in-situ monitoring system (IMS) based on optical imaging was developed in-house for implementation on the selective laser melting process. A digital single lens reflex camera, mirror and several sets of light emitting diode strip lights formed the main constituents of the IMS. Cylindrical samples of 316L stainless steel were printed with variations in their energy density. Features taken in optical images were extracted and evaluated via image processing. Micro computed tomography (CT), which is capable of assessing the internal defects and recovering the 3D representation of a structure, was used as a validation method to correlate the features identified in the optical images. Results have shown that features captured in-situ were correlated to defects detected by micro CT, revealing the potential of using optical images captured during printing as an indicator to the extent of defects present in selective laser melted parts.
URI: https://hdl.handle.net/10356/143850
ISSN: 2214-8604
DOI: 10.1016/j.addma.2020.101287
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Singapore Centre for 3D Printing 
Rights: © 2020 Elsevier B.V. All rights reserved. This paper was published in Additive Manufacturing and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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