Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Inertial Measurement Unit for On-Machine Diagnostics of Machine Tool Linear Axes
(National Institute of Standards and Technologies NIST)
(National Institute of Standards and Technologies NIST)
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Precision Engineering, Manufacturing and Metrology)ORCID iD: 0000-0001-9185-4607
(National Institute of Standards and Technologies NIST)
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of machines' components such as linear axes is typically a manual and time-consuming process. Thus, manufacturers need automated, efficient, and robust methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Towards this end, a method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. The IMU-based method uses data from accelerometers and rate gyroscopes to identify changes in linear and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios. Specifically, comparison of the IMU-based and laser-based results demonstrate that the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of linear axes. Consequently, an IMU was created for application of the IMU-based method on a machine tool as a proof of concept for detection of linear axis error motions. If the data collection and analysis are integrated within a machine controller, the process may be streamlined for the optimization of maintenance activities and scheduling, supporting more intelligent decision-making by manufacturing personnel and the development of self-diagnosing smart machine tools.

Place, publisher, year, edition, pages
2016.
Keywords [en]
Machine tool, linear axis, sensors, diagnostics
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Economics and Management; SRA - Production
Identifiers
URN: urn:nbn:se:kth:diva-200735Scopus ID: 2-s2.0-85030248252OAI: oai:DiVA.org:kth-200735DiVA, id: diva2:1070658
Conference
2016 Annual Conference of the Prognostics and Health Management Society,October 2-8, 2016, Denver, USA
Funder
XPRES - Initiative for excellence in production research
Note

QC 20170202

Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2018-05-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Search in DiVA

By author/editor
Archenti, Andreas
By organisation
Production Engineering
Production Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 106 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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