PAMF: An LLM-driven framework for automated mesh generation in mechanical simulation and CAE workflows.
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| Title: | PAMF: An LLM-driven framework for automated mesh generation in mechanical simulation and CAE workflows. |
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| Authors: | Pan, Feiyang1 (AUTHOR), Yin, Chunyan1 (AUTHOR), Dou, Guangbin1 (AUTHOR) gdou@seu.edu.cn |
| Source: | Journal of Mechanical Science & Technology. Jun2026, Vol. 40 Issue 6, p4497-4507. 11p. |
| Subjects: | Numerical grid generation (Numerical analysis), Computer-aided engineering, Engineering simulations, Finite element method, Detectors, Language models, Error analysis in mathematics |
| Abstract: | Finite element method (FEM) is pivotal in mechanical engineering, particularly for sensor design and reliability analysis, where high-quality meshing critically impacts computational accuracy and efficiency. However, generating adaptive meshes for complex geometries in traditional FEM relies heavily on computationally expensive posterior error estimation and iterative refinement. To address this, we propose the predictive adaptive meshing framework (PAMF) leveraging two fine-tuned LLM agents for a priori mesh generation and error prediction. By feeding predicted errors back to the mesh generator, PAMF actively refines meshes, significantly improving success rates. For validation, we developed an APDL code library with 100 mechanical models, featuring representative sensor geometries, under fixed constraints and force loads. Experiments show PAMF achieves 88.4 % mesh success rate, with 66.8 % of meshes yielding below 10 % error (Zienkiewicz-Zhu estimation), accelerating generation by 3.69 times versus traditional methods. This framework demonstrates LLMs' potential to revolutionize efficiency in mechanical simulation. To ensure full transparency and reproducibility, the complete dataset, all prompts, and related code for this study are publicly available at: https://github.com/2002-Pan/PAMF. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194452085 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: PAMF: An LLM-driven framework for automated mesh generation in mechanical simulation and CAE workflows. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pan%2C+Feiyang%22">Pan, Feiyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Chunyan%22">Yin, Chunyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dou%2C+Guangbin%22">Dou, Guangbin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gdou@seu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Jun2026, Vol. 40 Issue 6, p4497-4507. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Numerical+grid+generation+%28Numerical+analysis%29%22">Numerical grid generation (Numerical analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+engineering%22">Computer-aided engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+simulations%22">Engineering simulations</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Error+analysis+in+mathematics%22">Error analysis in mathematics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Finite element method (FEM) is pivotal in mechanical engineering, particularly for sensor design and reliability analysis, where high-quality meshing critically impacts computational accuracy and efficiency. However, generating adaptive meshes for complex geometries in traditional FEM relies heavily on computationally expensive posterior error estimation and iterative refinement. To address this, we propose the predictive adaptive meshing framework (PAMF) leveraging two fine-tuned LLM agents for a priori mesh generation and error prediction. By feeding predicted errors back to the mesh generator, PAMF actively refines meshes, significantly improving success rates. For validation, we developed an APDL code library with 100 mechanical models, featuring representative sensor geometries, under fixed constraints and force loads. Experiments show PAMF achieves 88.4 % mesh success rate, with 66.8 % of meshes yielding below 10 % error (Zienkiewicz-Zhu estimation), accelerating generation by 3.69 times versus traditional methods. This framework demonstrates LLMs' potential to revolutionize efficiency in mechanical simulation. To ensure full transparency and reproducibility, the complete dataset, all prompts, and related code for this study are publicly available at: https://github.com/2002-Pan/PAMF. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12206-026-0334-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 4497 Subjects: – SubjectFull: Numerical grid generation (Numerical analysis) Type: general – SubjectFull: Computer-aided engineering Type: general – SubjectFull: Engineering simulations Type: general – SubjectFull: Finite element method Type: general – SubjectFull: Detectors Type: general – SubjectFull: Language models Type: general – SubjectFull: Error analysis in mathematics Type: general Titles: – TitleFull: PAMF: An LLM-driven framework for automated mesh generation in mechanical simulation and CAE workflows. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pan, Feiyang – PersonEntity: Name: NameFull: Yin, Chunyan – PersonEntity: Name: NameFull: Dou, Guangbin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1738494X Numbering: – Type: volume Value: 40 – Type: issue Value: 6 Titles: – TitleFull: Journal of Mechanical Science & Technology Type: main |
| ResultId | 1 |
