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- Title
Understanding the role of segmentation on process-structure–property predictions made via machine learning.
- Authors
Massey, Caroline E.; Saldana, Christopher J.; Moore, David G.
- Abstract
The present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density.
- Subjects
ENERGY level densities; MACHINE learning; SUPPORT vector machines; THRESHOLDING algorithms; DECISION trees; MACHINE performance
- Publication
International Journal of Advanced Manufacturing Technology, 2022, Vol 120, Issue 5/6, p4011
- ISSN
0268-3768
- Publication type
Article
- DOI
10.1007/s00170-022-09003-8