Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing Article Swipe
Jeong‐Ah Lee
,
Man Jae SaGong
,
Jaimyun Jung
,
Eun Seong Kim
,
Hyoung Seop Kim
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.jmrt.2022.11.137
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.jmrt.2022.11.137
Recently, there has been development toward metal additive manufacturing (MAM) because of its benefits like fabrication of complex geometries, waste minimization, freedom of design, and low-cost customization. Despite these advantages, the influence of the processing parameters on the properties of MAM products is neither well understood nor easily predictable. In this study, explainable machine learning (xML) models were applied to predict and understand the geometry and types of defects in MAM-processed Fe-Ni alloys. Gaussian process regression (GPR) was used to predict the as-printed height and porosity using data from Fe-Ni alloys produced via laser metal deposition (LMD) processing. Defect types (gas porosity, keyhole, and lack of fusion) were classified using a support vector machine (SVM) by comparing the measured and predicted porosities based on GPR. The Shapley additive explanation (SHAP) approach for xML was utilized to analyze feature importance based on both GPR and SVM data. This study provides insight into the use of the xML model in MAM to link processing with results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jmrt.2022.11.137
- OA Status
- gold
- Cited By
- 51
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309891831
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309891831Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jmrt.2022.11.137Digital Object Identifier
- Title
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Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
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2022-11-24Full publication date if available
- Authors
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Jeong‐Ah Lee, Man Jae SaGong, Jaimyun Jung, Eun Seong Kim, Hyoung Seop KimList of authors in order
- Landing page
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https://doi.org/10.1016/j.jmrt.2022.11.137Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jmrt.2022.11.137Direct OA link when available
- Concepts
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Materials science, Porosity, Deposition (geology), Artificial intelligence, Support vector machine, Kriging, Feature (linguistics), Machine learning, Computer science, Process engineering, Composite material, Geology, Philosophy, Engineering, Sediment, Paleontology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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51Total citation count in OpenAlex
- Citations by year (recent)
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2025: 24, 2024: 14, 2023: 13Per-year citation counts (last 5 years)
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60Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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