Reference number : GC-2017-Q4-00115, amendment number : , agreement type : G, recipient legal name : Barari, Ahmad (University of Ontario Institute of Technology), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Coordinate metrology is the main inspection method for product or process control in various manufacturing_x000D_
systems today. The accuracy and reliability of inspection directly affect the quality of products or processes, the_x000D_
rate of failure, and the production waste. Recent developments in optical coordinate metrology allows fast_x000D_
capturing of large amount of 3D data points from the surface of the measured objects which is highly desired_x000D_
for dynamic measurements in automated production lines. However, the accuracy of measurement data highly_x000D_
depends on various sources of noises during the surface scanning process. The noisy points in the scanned_x000D_
point cloud increase the amount of uncertainty at different areas in the 3D point cloud, which affects the_x000D_
robustness of the measurement. Additional challenges are encountered when the texture of scanned surfaces are_x000D_
highly reflective. As there is no prior knowledge of how the noise is distributed in the point cloud, the optimum_x000D_
determination of model parameters is often a difficult and challenging process. This project investigates an_x000D_
approach to improve the reliability and accuracy of the measurement by determining the number and location_x000D_
of surface measurement points. It aims to find a solution for the planar surfaces and flatness evaluation because_x000D_
the flat surfaces and patches are the most common geometric features in various manufacturing products._x000D_
Bluewrist Inc. is a Canadian - based company focused on automated measurement and production quality_x000D_
control systems who primarily services the advanced manufacturing sectors. Bluewrist's main product include_x000D_
in-line dimensional measurement systems, robotic guidance system, and automation engine and_x000D_
communication software. This project allows Bluewirst to develop a more reliable platform for coordinate_x000D_
metrology and 3D scanning that can be implemented in their automated coordinate metrology products and_x000D_
services to its clients. This research will assist Bluewrist to develop customized methodologies to reduce the_x000D_
level of metrology uncertainty for various Canadian industrial sectors. It will also have a direct impact on_x000D_
training of highly qualified personnel who can become the leaders of Canada's future metrology industry., program purpose french : Coordinate metrology is the main inspection method for product or process control in various manufacturing_x000D_
systems today. The accuracy and reliability of inspection directly affect the quality of products or processes, the_x000D_
rate of failure, and the production waste. Recent developments in optical coordinate metrology allows fast_x000D_
capturing of large amount of 3D data points from the surface of the measured objects which is highly desired_x000D_
for dynamic measurements in automated production lines. However, the accuracy of measurement data highly_x000D_
depends on various sources of noises during the surface scanning process. The noisy points in the scanned_x000D_
point cloud increase the amount of uncertainty at different areas in the 3D point cloud, which affects the_x000D_
robustness of the measurement. Additional challenges are encountered when the texture of scanned surfaces are_x000D_
highly reflective. As there is no prior knowledge of how the noise is distributed in the point cloud, the optimum_x000D_
determination of model parameters is often a difficult and challenging process. This project investigates an_x000D_
approach to improve the reliability and accuracy of the measurement by determining the number and location_x000D_
of surface measurement points. It aims to find a solution for the planar surfaces and flatness evaluation because_x000D_
the flat surfaces and patches are the most common geometric features in various manufacturing products._x000D_
Bluewrist Inc. is a Canadian - based company focused on automated measurement and production quality_x000D_
control systems who primarily services the advanced manufacturing sectors. Bluewrist's main product include_x000D_
in-line dimensional measurement systems, robotic guidance system, and automation engine and_x000D_
communication software. This project allows Bluewirst to develop a more reliable platform for coordinate_x000D_
metrology and 3D scanning that can be implemented in their automated coordinate metrology products and_x000D_
services to its clients. This research will assist Bluewrist to develop customized methodologies to reduce the_x000D_
level of metrology uncertainty for various Canadian industrial sectors. It will also have a direct impact on_x000D_
training of highly qualified personnel who can become the leaders of Canada's future metrology industry., agreement title english : Reducing the sensitivity to 3D scanning noises in measurement of planar surfaces, agreement title french : Reducing the sensitivity to 3D scanning noises in measurement of planar surfaces, agreement number : EGP, agreement value : 24987.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada