Smart process mapping of powder bed fusion additively manufactured metallic wicks using surrogate modeling

Mohammad Borumand, Saideep Nannapaneni, Gurucharan Madiraddy, Michael P. Sealy, Sima Esfandiarpour Borujeni and Gisuk Hwang ()
Additional contact information
Mohammad Borumand: Wichita State University
Saideep Nannapaneni: Wichita State University
Gurucharan Madiraddy: University of Nebraska-Lincoln
Michael P. Sealy: Purdue University
Sima Esfandiarpour Borujeni: Wichita State University
Gisuk Hwang: Wichita State University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 3, No 16, 1819-1833

Abstract: Abstract Powder bed fusion is an innovative additive manufacturing (AM) technique to achieve metallic wick structures for efficient two-phase thermal management systems. However, a technical challenge lies in the lack of standard process maps as it currently relies on an expensive trial and error approach. In this study, five types of surrogate models for classification analysis (i.e., naïve Bayes, logistic regression, random forest, support vector machine, and Gaussian process classification) were constructed and compared to efficiently unlock the relations between five process parameters (i.e., laser power, scan speed, hatch spacing, spot diameter, and effective laser energy) and wick manufacturability. The models were trained using data from a total of 187 AM wick manufacturability experiments. Using four process parameter (PP) model (five PP model without effective laser energy), the Gaussian process classification (GPC) showed the maximum median prediction accuracy (PA) of 93%, while it further improved to 99.7% using support vector machine (SVM) and five process parameter model. Also, the median PAs of the SVM and GPC remains above 98.5% with only 60% of the total experimental data using five PP model. The sensitivity analysis showed that the hatch spacing was the most sensitive parameter for the wick manufacturability using four PP model, while the effective laser energy is the most sensitive one using five PP model. This study provides insights into the smart selection of optimal process parameters for the desired metallic AM wicks.

Keywords: Process parameters; 3D printed wick; Porous materials; Surrogate model; Sensitivity analysis; Classification (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02330-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02330-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02330-5

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().