Affiliation:
1. Department of Industrial Engineering, University of Louisville, Louisville, KY 40292, USA
Abstract
Laser powder–bed fusion (L-PBF) metal additive manufacturing has been widely utilized in various industries. However, large variability and inconsistent quality of the built parts still hinder the full potential of this manufacturing technology. Regarding part quality, the poor surface finish of sloping features remains one of the major shortcomings of L-PBF. The process parameters and contouring strategies have been identified as the primary factors dictating the surface roughness of the inclined surfaces, both up-skin and down-skin. Experimental approaches to modify the surface roughness by tuning contouring parameters could be costly and time-consuming. In addition, such methods cannot provide adequate physical insights into the phenomenon. Therefore, this study presents a multi-physics modeling framework to simulate a multi-track multi-layer L-PBF process in fabricating an inclined sample. The established simulation provides a valuable physical understanding of the driving forces exacerbating the formation and roughness of the inclined surfaces. The simulation results imply that the voids, formed due to insufficient melting in the low-energy contouring scan, are the leading cause of higher surface roughness for up-skin regions. On the other hand, though the visualization of attached particles is challenging regarding the down-skin surface, the simulated results show a lower and abnormal thermal gradient at the melt boundary due to the poorly supported melt region. The presence of thermal gradient irregularities suggests an overabundance of powder particles adhering to the melt boundary, resulting in increased surface roughness on the down-skin.
Funder
Technical Data Analysis, Inc.
Subject
General Materials Science,Metals and Alloys
Reference43 articles.
1. Gibson, I., Rosen, D., and Stucker, B. (2021). Additive Manufacturing Technologies, Springer.
2. A Review of Metal Additive Manufacturing Technologies;Yakout;Solid State Phenom.,2018
3. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods;Herriott;Comput. Mater. Sci.,2020
4. Three-Dimensional Additively Manufactured Microstructures and Their Mechanical Properties;Rodgers;JOM,2019
5. Uncertainty quantification and reduction in metal additive manufacturing;Wang;NPJ Comput. Mater.,2020
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