Grant: Engage Grants Program
University of Ontario Institute of Technology
$24,987.00
Reducing the sensitivity to 3D scanning noises in measurement of planar surfaces
FY 2017-18
Summary
Coordinate metrology is the main inspection method for product or process control in various manufacturingsystems today. The accuracy and reliability of inspection directly affect the quality of products or processes, therate of failure, and the production waste. Recent developments in optical coordinate metrology allows fastcapturing of large amount of 3D data points from the surface of the measured objects which is highly desiredfor dynamic measurements in automated production lines. However, the accuracy of measurement data highlydepends on various sources of noises during the surface scanning process. The noisy points in the scannedpoint cloud increase the amount of uncertainty at different areas in the 3D point cloud, which affects therobustness of the measurement. Additional challenges are encountered when the texture of scanned surfaces arehighly reflective. As there is no prior knowledge of how the noise is distributed in the point cloud, the optimumdetermination of model parameters is often a difficult and challenging process. This project investigates anapproach to improve the reliability and accuracy of the measurement by determining the number and locationof surface measurement points. It aims to find a solution for the planar surfaces and flatness evaluation becausethe flat surfaces and patches are the most common geometric features in various manufacturing products.Bluewrist Inc. is a Canadian - based company focused on automated measurement and production qualitycontrol systems who primarily services the advanced manufacturing sectors. Bluewrist's main product includein-line dimensional measurement systems, robotic guidance system, and automation engine andcommunication software. This project allows Bluewirst to develop a more reliable platform for coordinatemetrology and 3D scanning that can be implemented in their automated coordinate metrology products andservices to its clients. This research will assist Bluewrist to develop customized methodologies to reduce thelevel of metrology uncertainty for various Canadian industrial sectors. It will also have a direct impact ontraining of highly qualified personnel who can become the leaders of Canada's future metrology industry.
Program
Engage Grants Program
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
520969-2017
Name nom
Barari, Ahmad
Department département
Engineering and Applied Science, Faculty of
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
24987
Program id
EGP
Program name en
Engage Grants Program
Program name fr
Programme de subventions d'engagement partenariat
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1553
Committee name en
Ontario Internal Decision Committee
Committee name fr
Comité de décision interne pour l'Ontario
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
1702
Research subject group en
Design and manufacturing
Research subject group fr
Conception et fabrication
Research subject en
Advanced manufacturing
Research subject fr
Méthodes de fabrication avancées
Partie
1
Nb partie
1
Application title
Reducing the sensitivity to 3D scanning noises in measurement of planar surfaces
Keyword
NSERC
Application summary
Coordinate metrology is the main inspection method for product or process control in various manufacturingsystems today. The accuracy and reliability of inspection directly affect the quality of products or processes, therate of failure, and the production waste. Recent developments in optical coordinate metrology allows fastcapturing of large amount of 3D data points from the surface of the measured objects which is highly desiredfor dynamic measurements in automated production lines. However, the accuracy of measurement data highlydepends on various sources of noises during the surface scanning process. The noisy points in the scannedpoint cloud increase the amount of uncertainty at different areas in the 3D point cloud, which affects therobustness of the measurement. Additional challenges are encountered when the texture of scanned surfaces arehighly reflective. As there is no prior knowledge of how the noise is distributed in the point cloud, the optimumdetermination of model parameters is often a difficult and challenging process. This project investigates anapproach to improve the reliability and accuracy of the measurement by determining the number and locationof surface measurement points. It aims to find a solution for the planar surfaces and flatness evaluation becausethe flat surfaces and patches are the most common geometric features in various manufacturing products.Bluewrist Inc. is a Canadian - based company focused on automated measurement and production qualitycontrol systems who primarily services the advanced manufacturing sectors. Bluewrist's main product includein-line dimensional measurement systems, robotic guidance system, and automation engine andcommunication software. This project allows Bluewirst to develop a more reliable platform for coordinatemetrology and 3D scanning that can be implemented in their automated coordinate metrology products andservices to its clients. This research will assist Bluewrist to develop customized methodologies to reduce thelevel of metrology uncertainty for various Canadian industrial sectors. It will also have a direct impact ontraining of highly qualified personnel who can become the leaders of Canada's future metrology industry.
