Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Spatio-temporal adaptive sampling for effective coverage measurement planning during quality inspection of free form surfaces using robotic 3D optical scanner

Tools
- Tools
+ Tools

Babu, Manoj, Franciosa, Pasquale and Ceglarek, Dariusz (2019) Spatio-temporal adaptive sampling for effective coverage measurement planning during quality inspection of free form surfaces using robotic 3D optical scanner. Journal of Manufacturing Systems, 53 . pp. 93-108. doi:10.1016/j.jmsy.2019.08.003 ISSN 0278-6125.

[img]
Preview
PDF
WRAP-spatio-temporal-adaptive-sampling-coverage-planning-free-robotic-Ceglarek-2019.pdf - Published Version - Requires a PDF viewer.
Available under License Creative Commons Attribution 4.0.

Download (8Mb) | Preview
Official URL: http://dx.doi.org/10.1016/j.jmsy.2019.08.003

Request Changes to record.

Abstract

In-line dimensional inspection of free form surfaces using robotic 3D-optical scanners provide an opportunity to reduce the mean-time-to-detection of product quality defects and has thus emerged as a critical enabler in Industry 4.0 to achieve near-zero defects. However, the time needed to inspect large industrial size sheet metal parts by 3D-optical scanners frequently exceeds the production cycle time (CT), consequently, limiting the application of in-line measurement systems for high production volume manufacturing processes such as those used in the automotive industry. This paper addresses the aforementioned challenge by developing the Spatio-Temporal Adaptive Sampling (STAS) methodology which has the capability for (i) estimation of whole part deviations based on partial measurement of a free form surface; and, (ii) adaptive selection of the next region to be measured in order to satisfy pre-defined measurement criterion. This is achieved by first, modelling spatio-temporal correlations in the high dimensional Cloud-of-Points measurement data by using a dimension reduced space-time Kalman filter; then, dynamically updating the model parameters during the inspection process by incorporating partial measurement data to predict entire part deviations and adaptively choose the next critical region of the part to be measured.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TS Manufactures
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Surfaces -- Areas and volumes, Three-dimensional printing , Spatial analysis (Statistics)
Journal or Publication Title: Journal of Manufacturing Systems
Publisher: Elsevier
ISSN: 0278-6125
Official Date: October 2019
Dates:
DateEvent
October 2019Published
29 September 2019Available
26 August 2019Accepted
Volume: 53
Page Range: pp. 93-108
DOI: 10.1016/j.jmsy.2019.08.003
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 25 September 2019
Date of first compliant Open Access: 25 September 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
t EP/ K019368/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
APC6 project 113151Innovate UKhttp://dx.doi.org/10.13039/501100006041
APC6 project 113151Department for Business, Innovation and Skillshttp://dx.doi.org/10.13039/501100004800
HVM Catapult projectsWarwick Manufacturing Grouphttp://viaf.org/viaf/123628346
JLR-Catapult project 8237Jaguar Land Rover (Firm)http://viaf.org/viaf/305209406

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us