Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Analysis of industrial X-ray computed tomography data with deep neural networks
University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. (PTW)
Chalmers University of Technology, Gothenburg (SWE).
2021 (English)In: Proceedings Volume 11840, Developments in X-Ray Tomography XIII, SPIE , 2021, Vol. 11840Conference paper, Published paper (Refereed)
Abstract [en]

X-ray computed tomography (XCT) is increasingly utilized industrially at material- and process development as well as in non-destructive quality control; XCT is important to many emerging manufacturing technologies, for example metal additive manufacturing. These trends lead to increased needs of safe automatic or semi-automatic data interpretation, considered an open research question for many critical high value industrial products such as within the aerospace industry. By safe, we mean that the interpretation is not allowed to unawarely or unexpectedly fail; specifically the algorithms must react sensibly to inputs dissimilar to the training data, so called out-of-distribution (OOD) inputs. In this work we explore data interpretation with deep neural networks to address: robust safe data interpretation which includes a confidence estimate with respect to OOD data, an OOD detector; generation of realistic synthetic material aw indications for the material science and nondestructive evaluation community. We have focused on industrial XCT related challenges, addressing difficulties with spatially correlated X-ray quantum noise. Results are reported on training auto-encoders (AE) and generative adversarial networks (GAN), on a publicly available XCT dataset of additively manufactured metal. We demonstrate that adding modeled X-ray noise during training reduces artefacts in the generated imperfection indications as well as improves the OOD detector performance. In addition, we show that the OOD detector can detect real and synthetic OOD data and still model the accepted in-distribution data down to the X-ray noise levels. 

Place, publisher, year, edition, pages
SPIE , 2021. Vol. 11840
Keywords [en]
3D printers; Additives; Aerospace industry; Computerized tomography; Generative adversarial networks; Industrial research; Nondestructive examination; Quality control; Quantum noise; X ray detectors, Computed tomography data; Data interpretation; Deep learning; Manufacturing technologies; Material development; Materials and process; Non destructive; Non destructive evaluation; Process development; X-ray computed tomography, Deep neural networks
National Category
Other Mechanical Engineering Radiology, Nuclear Medicine and Medical Imaging Bioinformatics (Computational Biology)
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-18179DOI: 10.1117/12.2594714Scopus ID: 2-s2.0-85123049410OAI: oai:DiVA.org:hv-18179DiVA, id: diva2:1647520
Conference
SPIE Optical Engineering + Applications, 2021, San Diego, California, United States
Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2022-03-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Lindgren, Erik

Search in DiVA

By author/editor
Lindgren, Erik
By organisation
Division of Subtractive and Additive Manufacturing
Other Mechanical EngineeringRadiology, Nuclear Medicine and Medical ImagingBioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 93 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf