# An integrated computational materials engineering framework for process-structure-property mapping in laser powder bed fusion

https://mdr.nims.go.jp/datasets/39d279fc-8555-44f8-8af7-58e7cfb17722

## Files

- [1-s2.0-S0264127525015187-main.pdf](https://mdr.nims.go.jp/filesets/d6c43f18-1726-45b5-afff-f1ba804cfc78/download) ([Detail](https://mdr.nims.go.jp/filesets/d6c43f18-1726-45b5-afff-f1ba804cfc78.md))

## Id

39d279fc-8555-44f8-8af7-58e7cfb17722

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-11-17T04:57:48.342306Z

## Updated at

2025-11-17T07:30:03.964187Z

## Published at

2025-11-17T07:24:58.802013Z

## Doi



## First published url

https://doi.org/10.1016/j.matdes.2025.115097

## Date published

2025-11-14

## Recorded date published

2025-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: An integrated computational materials engineering framework for process-structure-property
    mapping in laser powder bed fusion
  title_type: original
  lang: en

## Description

- description: 'This work presents a comprehensive, experimentally validated integrated
    computational materials engineering framework for mapping the process-structure-property
    relationships in laser powder bed fusion (L-PBF) of Hastelloy-X alloys. The framework
    couples heat transfer, cellular automata (CA) solidification, and crystal plasticity
    finite elements (CPFE) simulations within one workflow. The heat transfer model
    was calibrated using single-track experiments and Bayesian inference to accurately
    capture melt pool geometry and the transition from conduction to keyhole melting.
    The CA model, driven by thermal simulation data, successfully reproduced key microstructural
    features, including the equiaxed-to-columnar grain transition and the formation
    of a strong crystallographic texture. The mechanical behavior was then predicted
    by CPFE simulations on representative volume elements extracted from the CA microstructures,
    revealing a direct correlation between crystallographic texture and macroscopic
    mechanical properties. The framework was applied to the mapping of the (P,v) process
    space, identifying distinct regions based on defect formation, microstructure
    and mechanical response. This validated approach offers a robust and efficient
    alternative to experimental trial-and-error identification of optimal process
    window, paving the way for data-driven optimization of L-PBF processes. '
  description_type: abstract
  lang: und

## Creator

- name: Fabien Briffod
  role: author
  orcid: https://orcid.org/0000-0002-3635-4885
- name: Phuangphaga Daram
  role: author
  orcid: https://orcid.org/0000-0001-8937-6319
- name: Masahiro Kusano
  role: author
  orcid: https://orcid.org/0000-0002-5061-0195
- name: Makoto Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-5064-9583

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Additive manufacturing
  schema: not_defined
- subject: Crystal plasticity
  schema: not_defined
- subject: Microstructure
  schema: not_defined
- subject: Cellular automata
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Materials & Design
  issn: '02641275'
  volume: '260'
  article_number: '115097'

## Conference



## Related item



## Funding

- funder_name: Japan Society for the Promotion of Science London

## 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: d6c43f18-1726-45b5-afff-f1ba804cfc78
  filename: 1-s2.0-S0264127525015187-main.pdf
  content_type: application/pdf
  size: 9605715
  md5: 3c3cf7535b10ae53ad47c75237465735

## Thumbnail

fileset_id: d6c43f18-1726-45b5-afff-f1ba804cfc78
filename: 1-s2.0-S0264127525015187-main.pdf