On the calibration of thermo-microstructural simulation models for Laser Powder Bed Fusion process: Integrating physics-informed neural networks with cellular automata Article Swipe
Jian Tang
,
Pooriya Scheel
,
Mohammad Sadegh Mohebbi
,
Christian Leinenbach
,
Laura De Lorenzis
,
E. Hosseini
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.addma.2024.104574
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.addma.2024.104574
Computational thermo-microstructural modelling has become a powerful tool for understanding the process- microstructure linkage in the Laser Powder Bed Fusion (PBF-LB) technique. Developing models that accurately represent experimental results requires properly calibrating non-measurable model parameters through computationally intensive inverse analysis. This study details the calibration of a thermo-microstructural model based on observations from single-track PBF-LB experiments for Hastelloy X (HX) alloy. The calibration framework integrates physics-informed neural networks (PINNs) for thermal analysis and cellular automata (CA) for microstructure simulation. Initially, a PINNs model is trained in an unsupervised fashion and validated against finite element simulation results to serve as a parametric solution for predicting singletrack temperature profiles and melt pool dimensions under various PBF-LB process settings and heat source parameters. Due to the high computational efficiency of the PINNs model and its ability to provide high-order derivatives through automatic differentiation, the model can be effectively used in the inverse calibration of the heat source parameters in the thermal model based on experimentally measured melt pool dimensions. The calibrated thermal model then supplies temperature data for subsequent CA microstructure modelling, where the nucleation parameters and the temperature dependence of the grain growth rate need to be determined. In addition, this study thoroughly discusses the challenges in calibrating the microstructural model, particularly based on experimental observations from single PBF-LB tracks. Ultimately, it identifies the optimal CA parameter set capable of representing the experimentally observed microstructures of PBF-LB HX under five different process conditions.
Related Topics
Concepts
Materials science
Calibration
Cellular automaton
Artificial neural network
Process (computing)
Fusion
Inertial confinement fusion
Laser
Biological system
Composite material
Process engineering
Optics
Artificial intelligence
Computer science
Mathematics
Philosophy
Engineering
Operating system
Statistics
Linguistics
Physics
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.addma.2024.104574
- OA Status
- hybrid
- Cited By
- 2
- References
- 118
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404877160
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404877160Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.addma.2024.104574Digital Object Identifier
- Title
-
On the calibration of thermo-microstructural simulation models for Laser Powder Bed Fusion process: Integrating physics-informed neural networks with cellular automataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-01Full publication date if available
- Authors
-
Jian Tang, Pooriya Scheel, Mohammad Sadegh Mohebbi, Christian Leinenbach, Laura De Lorenzis, E. HosseiniList of authors in order
- Landing page
-
https://doi.org/10.1016/j.addma.2024.104574Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.addma.2024.104574Direct OA link when available
- Concepts
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Materials science, Calibration, Cellular automaton, Artificial neural network, Process (computing), Fusion, Inertial confinement fusion, Laser, Biological system, Composite material, Process engineering, Optics, Artificial intelligence, Computer science, Mathematics, Philosophy, Engineering, Operating system, Statistics, Linguistics, Physics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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118Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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