ISIS Neutron and Muon Source Data Journal
This is a page describing data taken during an experiment at the ISIS Neutron and Muon Source. Information about the ISIS Neutron and Muon Source can be found at https://www.isis.stfc.ac.uk.
Developing a machine-learning-assisted inherent strain method for fast and reliable residual stress and distortion prediction in LPBF components
Abstract: This project aims to develop a machine-learning-assisted (MLA) inherent strain (IS) method for fast and accurate residual stress and distortion prediction for real-size Hastelloy X components manufactured through laser powder bed fusion (LPBF). Currently employed IS methods for LPBF deviate from the original IS framework and come with limitations as they assume a constant thermomechanical history all over the part. Instead, we propose to employ artificial neural networks (ANNs) to learn the relationship between site-specific IS values and geometrical features of the part first based on simulations. The ANN aims to approximate the non-uniform IS field of the entire component as unique source of residual stresses. This proposed method is in line with the original framework behind the IS theory. Once established, the proposed framework brings a drastic reduction in computational cost. The approval of this beam time request allows to additionally train the ANN based on experimental data, and thus, it is expected to improve the reliability and accuracy of the proposed method.
Principal Investigator: Mr Patrik Markovic
Local Contact: Dr Joe Kelleher
Experimenter: Dr Catrin Davies
Experimenter: Dr Ehsan Hosseini
Experimenter: Mr Peter Hanna
Experimenter: Mr Martin Gillet
Experimenter: Miss Amy Milne
Experimenter: Mr David Macknelly
DOI: 10.5286/ISIS.E.RB2410495
ISIS Experiment Number: RB2410495
| Part DOI | Instrument | Public release date | Download Link |
|---|---|---|---|
| 10.5286/ISIS.E.RB2410495-1 | ENGINX | 06 August 2027 | Download |
Publisher: STFC ISIS Neutron and Muon Source
Data format: RAW/Nexus
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Data Citation
The recommended format for citing this dataset in a research
publication is as:
[author], [date], [title], [publisher],
[doi]
For Example:
Mr Patrik Markovic et al; (2024): Developing a machine-learning-assisted inherent strain method for fast and reliable residual stress and distortion prediction in LPBF components, STFC ISIS Neutron and Muon Source, https://doi.org/10.5286/ISIS.E.RB2410495
Data is released under the CC-BY-4.0 license.
