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dc.contributor.authorBorumand, Mohammad
dc.contributor.authorBorujeni, Sima E.
dc.contributor.authorNannapaneni, Saideep
dc.contributor.authorAusherman, Moriah
dc.contributor.authorMadiraddy, Guru
dc.contributor.authorSealy, Michael
dc.contributor.authorHwang, Gisuk
dc.identifier.citationBorumand, M, Esfandiarpour Borujeni, S, Nannapaneni, S, Ausherman, M, Madiraddy, G, Sealy, M, & Hwang, G. "Process Mapping of Additively-Manufactured Metallic Wicks Through Surrogate Modeling." Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition. Volume 2A: Advanced Manufacturing. Virtual, Online. November 1–5, 2021. V02AT02A013. ASME.
dc.descriptionClick on the DOI link to view this conference paper (may not be free).en_US
dc.description.abstractTailored wick structures are essential to develop efficient two-phase thermal management systems in various engineering applications, however, manufacturing a geometrically-complex wick is challenging using conventional manufacturing processes due to limited manufacturability and poor cost effectiveness. Additive manufacturing is an ideal alternative, however, the state-of-the-art metal three-dimensional printers have poor manufacturability when depositing pre-designed porous wicks with pore sizes below 100 μm. In this paper, a powder bed fusion 3D printer (Matsuura Lumex Avance-25) was employed to fabricate metallic wicks through partial sintering for pore sizes below 100 μm with data-driven control of process parameters. Hatch spacing and scan speed were selected as the two main AM process parameters to adjust. Due to the unavailability of process maps between the process parameters and properties of printed metallic wick structures, different surrogate-based models were employed to identify the combinations of the two process parameters that result in improved manufacturability of wick structures. Since the generation of training points for surrogate model training through experimentation is expensive and time-consuming, Bayesian optimization was used for sequential and intelligent selection of training points that provide maximum information gain regarding the relationships between the process parameters and the manufacturability of a 3D printed wick structure. The relationship between the required number of training points and model prediction accuracy was investigated. The AM parameters’ ranges were discretized using six values of hatch spacing and seven values of scan speed, which resulted in a total of 42 combinations across the two parameters. Preliminary results conclude that 80% prediction accuracy is achievable with approximately forty training points (only 10% of total combinations). This study provides insights into the selection of optimal process parameters for the desired additively-manufactured wick structure performance.en_US
dc.description.sponsorshipThis work is financially supported by the National Science Foundation (NSF), Award No. OIA-1929187 and the Wichita State University Convergence Sciences Initiative Program. This work is also financially supported by the College of Engineering, Wichita State University.en_US
dc.relation.ispartofseriesASME 2021 International Mechanical Engineering Congress and Exposition;2021
dc.subjectAdditive manufacturingen_US
dc.subjectBayesian optimizationen_US
dc.subjectLaser powder bed fusionen_US
dc.subjectData classificationen_US
dc.subject3D printed wicken_US
dc.subjectPorous materialsen_US
dc.subjectSupport vector machineen_US
dc.subjectRandom foresten_US
dc.titleProcess mapping of additively-manufactured metallic wicks through surrogate modelingen_US
dc.typeConference paperen_US
dc.rights.holder© 2021 by ASMEen_US

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