# Cellular automata simulations of texture evolution in flat-top laser additively manufactured Hastelloy-X

https://mdr.nims.go.jp/datasets/6289e8c6-3e2b-44b5-91d3-1aa26da89871

## File

- [1-s2.0-S2238785426016790-main.pdf](https://mdr.nims.go.jp/filesets/29a14d37-e3ef-44fe-958a-d4575af8919b/download) ([Detail](https://mdr.nims.go.jp/filesets/29a14d37-e3ef-44fe-958a-d4575af8919b.md))

## Id

6289e8c6-3e2b-44b5-91d3-1aa26da89871

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-07-03T03:45:37.883289Z

## Updated at

2026-07-03T03:48:41.034123Z

## Published at

2026-07-03T07:29:22.229446Z

## Doi



## First published url

https://doi.org/10.1016/j.jmrt.2026.06.251

## Date published

2026-06-30

## Recorded date published

2026-7

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Cellular automata simulations of texture evolution in flat-top laser additively
    manufactured Hastelloy-X
  title_type: original
  lang: en

## Description

- description: 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 $\{001\}_{z}\langle 110 \rangle_{x}$ texture
    of the substrate to a $\{001\}_{z}\langle 100 \rangle_{x}$ 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.
  description_type: abstract
  lang: und

## Creator

- name: Fabien Briffod
  role: author
  orcid: https://orcid.org/0000-0002-3635-4885
- name: Phuangphaga Daram
  role: author
- name: Tomonori Kitashima
  role: author
- name: Makoto Watanabe
  role: author

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Cellular automata
  schema: not_defined
- subject: Additive manufacturing
  schema: not_defined
- subject: Texture
  schema: not_defined
- subject: Microstructure
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: Journal of Materials Research and Technology
  issn: '22387854'
  volume: '43'
  start_page: 3228
  end_page: 3244

## Conference



## Related item



## Funding

- identifier: AF-2022201-A3
  funder_name: The Amada Foundation
- identifier: JPMJKP25R1
  funder_name: Japan Science and Technology Agency
- identifier: JPJ004596
  funder_name: Acquisition, Technology, and Logistics Agency (ATLA), Japan

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 29a14d37-e3ef-44fe-958a-d4575af8919b
  filename: 1-s2.0-S2238785426016790-main.pdf
  content_type: application/pdf
  size: 9392203
  md5: 0ad19fbbfaca417738410b0a27aba942

## Thumbnail

fileset_id: 29a14d37-e3ef-44fe-958a-d4575af8919b
filename: 1-s2.0-S2238785426016790-main.pdf