Smart process mapping of powder bed fusion additively manufactured metallic wicks using surrogate modeling
Borumand, Mohammad Nannapaneni, Saideep Madiraddy, Guru Sealy, Michael Esfandiarpour Borujeni, S. Hwang, Gisuk
Borumand, Mohammad
Nannapaneni, Saideep
Madiraddy, Guru
Sealy, Michael
Esfandiarpour Borujeni, S.
Hwang, Gisuk
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2024
Type
Article
Genre
Keywords
3D printed wick,Classification,Porous materials,Process parameters,Sensitivity analysis,Surrogate model
Subjects (LCSH)
Citation
Borumand, M., Nannapaneni, S., Madiraddy, G., Sealy, M.P., Esfandiarpour Borujeni, S., Hwang, G. Smart process mapping of powder bed fusion additively manufactured metallic wicks using surrogate modeling. (2024). Journal of Intelligent Manufacturing. DOI: 10.1007/s10845-024-02330-5
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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Table of Contents
Description
Publisher
Springer
Journal
Journal of Intelligent Manufacturing
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
0956-5515