Cellular automata simulations of texture evolution in flat-top laser additively manufactured Hastelloy-X
Source abstract
Abstract
The present study investigates the influence of process parameters, nucleation density, and stochastic effects on the microstructure evolution of Hastelloy X samples fabricated by selective laser melting on near single crystal Ni substrates using a flat top laser. Experiments and cellular automata simulations were performed to analyze crystallographic texture and M-index evolution along the build height as function of laser power, scan speed and heterogeneous nucleation density. The results show that higher linear energy densities promote epitaxial growth, stabilizing the texture, while lower energy densities lead to finer grains and increased high-angle grain boundaries due to enhanced nucleation. A nucleation density of 50$\times$10$12$ m$-3$ provided the most reasonable agreement in terms of texture strength evolution with most experimental observations while higher nucleation densities lead to a weakening of the texture combined with a faster transition from the initial $\{001\}z\langle 110 \ranglex$ texture of the substrate to a $\{001\}z\langle 100 \ranglex$ texture. Additionally, microstructure variability due to stochastic nucleation was found to be process-dependent, with some conditions exhibiting stable, repeatable texture evolution, while other conditions showed high sensitivity to random seed selection. These findings highlight the need for probabilistic modeling approaches to improve the predictive accuracy of AM microstructure simulations.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Cellular Automata and Applications · Solidification and crystal growth phenomena
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (50)
Show source-provided references
1. Metal additive manufacturing: A review10.1007/s11665-014-0958-z ↗
2. Additive manufacturing of metallic components – Process, structure and properties10.1016/j.pmatsci.2017.10.001 ↗
3. Liu · 2021
4. Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges10.3390/app11031213 ↗
5. Grain structure evolution in Inconel 718 during selective electron beam melting10.1016/j.msea.2016.05.046 ↗
6. Strategy for texture management in metals additive manufacturing10.1007/s11837-017-2264-3 ↗
7. Texture evolution in metallic materials during additive manufacturing: A review10.1007/s41403-021-00271-6 ↗
8. Crystallographic texture control of beta-type Ti–15Mo–5Zr–3Al alloy by selective laser melting for the development of novel implants with a biocompatible low Young’s modulus10.1016/j.scriptamat.2016.12.038 ↗
9. Crystallographic orientation control of 316l austenitic stainless steel via selective laser melting10.2355/isijinternational.ISIJINT-2019-744 ↗
10. Hagihara · 2021
11. Superimpositional design of crystallographic textures and macroscopic shapes via metal additive manufacturing—Game-change in component design10.1016/j.actamat.2025.120709 ↗
12. Control of texture and microstructure in additive manufacturing of stainless steel 316 L10.1016/j.jallcom.2023.173040 ↗
13. Influence of keyhole and conduction mode melting for top-hat shaped beam profiles in laser powder bed fusion10.1016/j.jmatprotec.2019.116514 ↗
14. Controlling the Young’s modulus of a β -type Ti–Nb alloy via strong texturing by LPBF10.1016/j.matdes.2022.110516 ↗
15. Manufacturing single crystals of pure nickel via selective laser melting with a flat-top laser beam
16. Effect of scan strategy on the formation of a pure nickel single-crystal structure using a flat-top laser beam via laser powder bed fusion10.1080/14686996.2023.2201380 ↗
17. A three-dimensional cellular automaton-finite element model for the prediction of solidification grain structures10.1007/s11661-999-0226-2 ↗
18. A solution to the problem of the mesh anisotropy in cellular automata simulations of grain growth10.1016/j.commatsci.2015.06.026 ↗
19. A coupled finite element cellular automaton model to predict thermal history and grain morphology of Ti–6Al–4V during direct metal deposition (DMD)
20. Three-dimensional cellular automaton-finite element modeling of solidification grain structures for arc-welding processes10.1016/j.actamat.2016.05.011 ↗
21. Evolution of grain structure during laser additive manufacturing. Simulation by a cellular automata method10.1016/j.matdes.2016.05.125 ↗
22. Three-dimensional modeling of the microstructure evolution during metal additive manufacturing10.1016/j.commatsci.2017.09.018 ↗
23. Prediction of microstructure in selective laser melted Ti–6Al–4V alloy by cellular automaton10.1016/j.jallcom.2018.03.116 ↗
24. A parallelized three-dimensional cellular automaton model for grain growth during additive manufacturing10.1007/s00466-017-1535-8 ↗
25. Integrated computational materials engineering to predict melt-pool dimensions and 3D grain structures for selective laser melting of Inconel 62510.1007/s40192-019-00145-4 ↗
26. A cellular automaton finite volume method for microstructure evolution during additive manufacturing10.1016/j.matdes.2019.107672 ↗
27. Grain growth prediction in selective electron beam melting of Ti–6Al–4V with a cellular automaton method10.1016/j.matdes.2020.109410 ↗
28. ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling10.1016/j.commatsci.2022.111692 ↗
29. An OpenMP GPU-offload implementation of a non-equilibrium solidification cellular automata model for additive manufacturing10.1016/j.cpc.2022.108605 ↗
30. A multi-level capture algorithm for accelerating cellular automata predictions of grain structure and texture in additive manufacturing
31. Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing10.1088/1361-651X/ab9734 ↗
32. Understanding uncertainty in microstructure evolution and constitutive properties in additive process modeling10.3390/met12020324 ↗
33. Calibrating uncertain parameters in melt pool simulations of additive manufacturing10.1016/j.commatsci.2022.111904 ↗
34. Effects of substrate preheating and processing parameters on the crystallographic texture of Hastelloy X on SX-like Ni fabricated by L-PBF process with flat-top laser beam10.1016/j.jmrt.2026.01.142 ↗
35. An integrated computational materials engineering framework for process–structure–property mapping in laser powder bed fusion10.1016/j.matdes.2025.115097 ↗
36. A dynamic volumetric heat source model for laser additive manufacturing
37. Mills · 2002 · 244
38. Gandin · 1997 · 2187
39. Theory of I rostructural development during rapid solidification10.1016/0001-6160(86)90056-8 ↗
40. Texture analysis with MTEX- free and open source software toolbox
41. The misorientation index: Development of a new method for calculating the strength of lattice-preferred orientation10.1016/j.tecto.2005.08.023 ↗
42. Stability of crystallographic texture in laser powder bed fusion: Understanding the competition of crystal growth using a single crystalline seed
43. Neural cellular automata for solidification microstructure modelling10.1016/j.cma.2023.116197 ↗
44. Computational assessment of thermokinetics and associated microstructural evolution in laser powder bed fusion manufacturing of Ti6Al4V alloy10.1038/s41598-020-63281-4 ↗
45. Heat source model development for thermal analysis of laser powder bed fusion using Bayesian optimization and machine learning10.1007/s40192-023-00334-2 ↗
46. The columnar to equiaxed transition of CoCrNi medium-entropy alloy fabricated by laser directed energy deposition10.1016/j.matdes.2023.112538 ↗
47. Controlling heat accumulation through changing time per layer in laser powder bed fusion of nickel-based superalloy10.1016/j.jmapro.2024.09.001 ↗
48. Three-dimensional analysis of grain structure and texture of additively manufactured 316L austenitic stainless steel
49. Optimizing the cellular automata finite element model for additive manufacturing to simulate large microstructures10.1016/j.actamat.2021.116930 ↗
50. Porosity, residual stress, wear properties and impact toughness of additively manufactured low-alloy steel: A review