Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters
Abstract We generated synthetic equiaxed grain structures using computer graphics software to explore the relationship between various grain size determination methods and true three-dimensional (3D) grain diameters. Mirroring grain measurement techniques, the synthetic 3D grain structures are image...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-73090-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234637526663168 |
|---|---|
| author | Kevin Gillespie Algirdas Baskys Ian Pong Jean-Francois Croteau |
| author_facet | Kevin Gillespie Algirdas Baskys Ian Pong Jean-Francois Croteau |
| author_sort | Kevin Gillespie |
| collection | DOAJ |
| description | Abstract We generated synthetic equiaxed grain structures using computer graphics software to explore the relationship between various grain size determination methods and true three-dimensional (3D) grain diameters. Mirroring grain measurement techniques, the synthetic 3D grain structures are imaged as 2D micrographs which are measured to yield 1D grain size parameters. Synthetic grain structures provide data at a mass scale and permit exploration of both polished and fractured surface micrographs, revealing one-to-one correspondence between exposed 2D grain cross-sections and individual 3D grains. Analysis of this correspondence yielded a procedure to approximate 3D equiaxed grain size and volume distributions based on the mode of the 2D fractograph grain size distribution. The 3D approximation procedure is shown to be less susceptible to different imaging conditions that affect small, undiscernible grains compared to the standard planimetric and linear intercept methods, which by design also tend to underestimate the 3D grain diameter. The procedure requires larger sample sizes to lower variance and a deeper analysis which could become more practical with machine learning (ML) models for grain boundary segmentation, which synthetic grain structures can help train. This work lays the foundation for analyzing other grain distributions such as columnar and composite grains in similar depth. |
| format | Article |
| id | doaj-art-97c88360377a4cd68e36b3ed7120bc4c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-97c88360377a4cd68e36b3ed7120bc4c2025-08-20T04:03:06ZengNature PortfolioScientific Reports2045-23222024-10-0114111410.1038/s41598-024-73090-8Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parametersKevin Gillespie0Algirdas Baskys1Ian Pong2Jean-Francois Croteau3Superconducting Magnets Group, Lawrence Berkeley National LaboratorySuperconducting Magnets Group, Lawrence Berkeley National LaboratorySuperconducting Magnets Group, Lawrence Berkeley National LaboratorySuperconducting Magnets Group, Lawrence Berkeley National LaboratoryAbstract We generated synthetic equiaxed grain structures using computer graphics software to explore the relationship between various grain size determination methods and true three-dimensional (3D) grain diameters. Mirroring grain measurement techniques, the synthetic 3D grain structures are imaged as 2D micrographs which are measured to yield 1D grain size parameters. Synthetic grain structures provide data at a mass scale and permit exploration of both polished and fractured surface micrographs, revealing one-to-one correspondence between exposed 2D grain cross-sections and individual 3D grains. Analysis of this correspondence yielded a procedure to approximate 3D equiaxed grain size and volume distributions based on the mode of the 2D fractograph grain size distribution. The 3D approximation procedure is shown to be less susceptible to different imaging conditions that affect small, undiscernible grains compared to the standard planimetric and linear intercept methods, which by design also tend to underestimate the 3D grain diameter. The procedure requires larger sample sizes to lower variance and a deeper analysis which could become more practical with machine learning (ML) models for grain boundary segmentation, which synthetic grain structures can help train. This work lays the foundation for analyzing other grain distributions such as columnar and composite grains in similar depth.https://doi.org/10.1038/s41598-024-73090-8 |
| spellingShingle | Kevin Gillespie Algirdas Baskys Ian Pong Jean-Francois Croteau Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters Scientific Reports |
| title | Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters |
| title_full | Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters |
| title_fullStr | Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters |
| title_full_unstemmed | Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters |
| title_short | Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters |
| title_sort | updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3d grain size distributions from 2d visualizations with 1d parameters |
| url | https://doi.org/10.1038/s41598-024-73090-8 |
| work_keys_str_mv | AT kevingillespie updimensioningstrategyderivedfromsyntheticequiaxedgrainstructuresforapproximating3dgrainsizedistributionsfrom2dvisualizationswith1dparameters AT algirdasbaskys updimensioningstrategyderivedfromsyntheticequiaxedgrainstructuresforapproximating3dgrainsizedistributionsfrom2dvisualizationswith1dparameters AT ianpong updimensioningstrategyderivedfromsyntheticequiaxedgrainstructuresforapproximating3dgrainsizedistributionsfrom2dvisualizationswith1dparameters AT jeanfrancoiscroteau updimensioningstrategyderivedfromsyntheticequiaxedgrainstructuresforapproximating3dgrainsizedistributionsfrom2dvisualizationswith1dparameters |