liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
A differential entropy-based method for reverse engineering quality assessment
Università degli Studi di Messina, Italy.
Università degli Studi di Messina, Italy.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5698-5983
Università degli Studi di Messina, Italy.
Show others and affiliations
2023 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The present work proposes the use of point clouds differential entropy as a method for reverse engineering quality assessment. This quality assessment can be used to measure the deviation of objects made with additive manufacturing or CNC techniques. The quality of the execution is intended as a measure of the deviation of the geometry of the obtained object compared to the original CAD. This paper proposes the use of the quality index of the CorAl method to assess the quality of an objects compared to its original CAD. This index, based on the differential entropy, takes on a value the closer to 0 the more they obtained object is close to the original geometry. The advantage of this method is to have a global synthetic index. It is however possible to have entropy maps of the individual points to verify which are the areas with the greatest deviation. The method is robust for comparing point clouds at different densities. Objects obtained by additive manufacturing with different print qualities were used. The quality index evaluated for each object, as defined in the CorAl method, turns out to be gradually closer to 0 as the quality of the piece's construction increases.

Place, publisher, year, edition, pages
2023.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-198606OAI: oai:DiVA.org:liu-198606DiVA, id: diva2:1806262
Conference
ADM 2023 International Conference, Florence, Italy 6-8 September 2023
Available from: 2023-10-20 Created: 2023-10-20 Last updated: 2023-10-20

Open Access in DiVA

No full text in DiVA

Authority records

Forssén, Per-Erik

Search in DiVA

By author/editor
Forssén, Per-Erik
By organisation
Computer VisionFaculty of Science & Engineering
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 121 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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