AccScience Publishing / MSAM / Volume 3 / Issue 3 / DOI: 10.36922/msam.3652
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ORIGINAL RESEARCH ARTICLE

An experimental process parameter study on the identification of defects in additively fabricated Al6061 with laser powder bed fusion

Faik Derya Ince1 ,  Sivaji Karna2 ,  Tianyu Zhang2 ,  Andrew J. Gross2 ,  Timothy Krentz3 ,  Dale Hitchcock3 ,  Lang Yuan2* ,  Tuğrul Özel1*
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1 Manufacturing and Automation Research Laboratory, Department of Industrial and Systems Engineering, School of Engineering, Rutgers University-New Brunswick, Piscataway, New Jersey, United States of America
2 Department of Mechanical Engineering, Molinaroli College of Engineering and Computing, University of South Carolina, Columbia, South Carolina, United States of America
3 Tritium Technology Division, Savannah River National Lab, Aiken, South Carolina, United States of America
MSAM 2024, 3(3), 3652 https://doi.org/10.36922/msam.3652
Submitted: 13 May 2024 | Accepted: 24 July 2024 | Published: 30 August 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Additively fabricated metal parts using laser powder bed fusion (L-PBF) possess sophisticated morphology due to the recurrent use of laser-induced metal powder melting and solidification. The surface and 3D morphology of these parts often include defects in the form of protrusions, depressions, pores, voids, keyholes, or cracks that are known to be influenced by laser scanning paths and layer-to-layer processing. Such inconsistent part quality hampers the extensive adoption of L-PBF. Pores and cracks are detrimental to the fatigue life of the parts and components. Quantifying and controlling part defects and optimizing processing and scanning strategy parameters adaptively in real-time through in situ monitoring systems are highly desired. This study investigates the optimization of experimental process parameters (power, scan velocity, and hatch spacing) and their effects on the cracking and porosity of Al6061 alloy using machine learning techniques. Multi-objective optimization is formulated and conducted to determine the L-PBF parameters that minimize both porosity and crack densities.

Keywords
Additive manufacturing
Laser powder bed fusion
Aluminum alloy 6061
Cracks
Porosity
Optimization
Funding
SRNL, Contract No. 89303321CEM000080, Receiver: Lang Yuan
Conflict of interest
Tuğrul Özel serves as the Editorial Board Member of the journal, but did not in any way involve in the editorial and peer-review process conducted for this paper, directly or indirectly. Other authors declare they have no competing interests.
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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Published by AccScience Publishing
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