Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach Article Swipe
Jiahui Ye
,
Mohamad Mahmoudi
,
Kübra Karayağız
,
Luke Johnson
,
Raiyan Seede
,
İbrahim Karaman
,
Raymundo Arróyave
,
Alaa Elwany
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1115/1.4052270
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1115/1.4052270
Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often the case that a suite of hierarchically linked (or coupled) simulation models is needed to achieve the above tasks, as the entirety of the complex physical phenomena relevant to the understanding of process-structure-property-performance relationships in the context of AM precludes the use of a single simulation framework. In this study using a Bayesian network approach, we address the important problem of conducting uncertainty quantification (UQ) analysis for multiple hierarchical models to establish process-microstructure relationships in laser powder bed fusion (LPBF) AM. More significantly, we present the framework to calibrate and analyze simulation models that have experimentally unmeasurable variables, which are quantities of interest predicted by an upstream model and deemed necessary for the downstream model in the chain. We validate the framework using a case study on predicting the microstructure of a binary nickel-niobium alloy processed using LPBF as a function of processing parameters. Our framework is shown to be able to predict segregation of niobium with up to 94.3% prediction accuracy on test data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1115/1.4052270
- https://asmedigitalcollection.asme.org/risk/article-pdf/8/1/011111/6772376/risk_008_01_011111.pdf
- OA Status
- bronze
- Cited By
- 13
- References
- 64
- Related Works
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- OpenAlex ID
- https://openalex.org/W3198842436
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Raw OpenAlex JSON
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https://openalex.org/W3198842436Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1115/1.4052270Digital Object Identifier
- Title
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Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-08-31Full publication date if available
- Authors
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Jiahui Ye, Mohamad Mahmoudi, Kübra Karayağız, Luke Johnson, Raiyan Seede, İbrahim Karaman, Raymundo Arróyave, Alaa ElwanyList of authors in order
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https://doi.org/10.1115/1.4052270Publisher landing page
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https://asmedigitalcollection.asme.org/risk/article-pdf/8/1/011111/6772376/risk_008_01_011111.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://asmedigitalcollection.asme.org/risk/article-pdf/8/1/011111/6772376/risk_008_01_011111.pdfDirect OA link when available
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Context (archaeology), Computer science, Process (computing), Calibration, Bayesian network, Artificial intelligence, Mathematics, Statistics, Operating system, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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2025: 3, 2024: 4, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
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64Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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