DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing.
Saved in:
| Title: | DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing. |
|---|---|
| Authors: | Stump, Benjamin C.1 (AUTHOR) stumpbc@ornl.gov, Plotkowski, Alex2 (AUTHOR), Nutaro, James1 (AUTHOR) |
| Source: | Computational Materials Science. Mar2024, Vol. 237, pN.PAG-N.PAG. 1p. |
| Subjects: | Cellular automata, Materials science, Thermocycling, Grain, Computational neuroscience |
| Abstract: | Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA , to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling , in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio , is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed. [Display omitted] [ABSTRACT FROM AUTHOR] |
| Copyright of Computational Materials Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 175963537 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Stump%2C+Benjamin+C%2E%22">Stump, Benjamin C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> stumpbc@ornl.gov</i><br /><searchLink fieldCode="AR" term="%22Plotkowski%2C+Alex%22">Plotkowski, Alex</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nutaro%2C+James%22">Nutaro, James</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Materials+Science%22">Computational Materials Science</searchLink>. Mar2024, Vol. 237, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cellular+automata%22">Cellular automata</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+science%22">Materials science</searchLink><br /><searchLink fieldCode="DE" term="%22Thermocycling%22">Thermocycling</searchLink><br /><searchLink fieldCode="DE" term="%22Grain%22">Grain</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA , to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling , in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio , is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed. [Display omitted] [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Materials Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=175963537 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.commatsci.2024.112901 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Cellular automata Type: general – SubjectFull: Materials science Type: general – SubjectFull: Thermocycling Type: general – SubjectFull: Grain Type: general – SubjectFull: Computational neuroscience Type: general Titles: – TitleFull: DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Stump, Benjamin C. – PersonEntity: Name: NameFull: Plotkowski, Alex – PersonEntity: Name: NameFull: Nutaro, James IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09270256 Numbering: – Type: volume Value: 237 Titles: – TitleFull: Computational Materials Science Type: main |
| ResultId | 1 |
