Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures
Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), provides unmatched design flexibility for creating intricate steel structures with minimal post-processing. However, adopting L-PBF for high-performance applications is difficult due to the challenge of predicting microstruct...
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MDPI AG
2025-07-01
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| author | Yu Qiao Marius Grad Aida Nonn |
| author_facet | Yu Qiao Marius Grad Aida Nonn |
| author_sort | Yu Qiao |
| collection | DOAJ |
| description | Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), provides unmatched design flexibility for creating intricate steel structures with minimal post-processing. However, adopting L-PBF for high-performance applications is difficult due to the challenge of predicting microstructure evolution. This is because the process is sensitive to many parameters and has a complex thermal history. Thin-walled geometries present an added challenge because their dimensions often approach the scale of individual grains. Thus, microstructure becomes a critical factor in the overall integrity of the component. This study focuses on applying cellular automata (CA) modeling to establish robust and efficient process–structure relationships in L-PBF of 316L stainless steel. The CA framework simulates solidification-driven grain evolution and texture development across various processing conditions. Model predictions are evaluated against experimental electron backscatter diffraction (EBSD) data, with additional quantitative comparisons based on texture and morphology metrics. The results demonstrate that CA simulations calibrated with relevant process parameters can effectively reproduce key microstructural features, including grain size distributions, aspect ratios, and texture components, observed in thin-walled L-PBF structures. This work highlights the strengths and limitations of CA-based modeling and supports its role in reliably designing and optimizing complex L-PBF components. |
| format | Article |
| id | doaj-art-3094cf0b05c841bfaff7b73d1db03d09 |
| institution | DOAJ |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Metals |
| spelling | doaj-art-3094cf0b05c841bfaff7b73d1db03d092025-08-20T03:08:02ZengMDPI AGMetals2075-47012025-07-0115781210.3390/met15070812Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel StructuresYu Qiao0Marius Grad1Aida Nonn2Computational Mechanics and Materials Lab, Faculty of Mechanical Engineering, OTH Regensburg, Galgenbergstraße 30, 93053 Regensburg, GermanyComputational Mechanics and Materials Lab, Faculty of Mechanical Engineering, OTH Regensburg, Galgenbergstraße 30, 93053 Regensburg, GermanyComputational Mechanics and Materials Lab, Faculty of Mechanical Engineering, OTH Regensburg, Galgenbergstraße 30, 93053 Regensburg, GermanyAdditive manufacturing (AM), particularly laser powder bed fusion (L-PBF), provides unmatched design flexibility for creating intricate steel structures with minimal post-processing. However, adopting L-PBF for high-performance applications is difficult due to the challenge of predicting microstructure evolution. This is because the process is sensitive to many parameters and has a complex thermal history. Thin-walled geometries present an added challenge because their dimensions often approach the scale of individual grains. Thus, microstructure becomes a critical factor in the overall integrity of the component. This study focuses on applying cellular automata (CA) modeling to establish robust and efficient process–structure relationships in L-PBF of 316L stainless steel. The CA framework simulates solidification-driven grain evolution and texture development across various processing conditions. Model predictions are evaluated against experimental electron backscatter diffraction (EBSD) data, with additional quantitative comparisons based on texture and morphology metrics. The results demonstrate that CA simulations calibrated with relevant process parameters can effectively reproduce key microstructural features, including grain size distributions, aspect ratios, and texture components, observed in thin-walled L-PBF structures. This work highlights the strengths and limitations of CA-based modeling and supports its role in reliably designing and optimizing complex L-PBF components.https://www.mdpi.com/2075-4701/15/7/812L-PBFtexturemicrostructure simulationcellular automataEBSD validation |
| spellingShingle | Yu Qiao Marius Grad Aida Nonn Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures Metals L-PBF texture microstructure simulation cellular automata EBSD validation |
| title | Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures |
| title_full | Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures |
| title_fullStr | Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures |
| title_full_unstemmed | Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures |
| title_short | Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures |
| title_sort | toward an efficient and robust process structure prediction framework for filigree l pbf 316l stainless steel structures |
| topic | L-PBF texture microstructure simulation cellular automata EBSD validation |
| url | https://www.mdpi.com/2075-4701/15/7/812 |
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