TY - JOUR
T1 - The Flatness Error Evaluation of Metal Workpieces Based on Line Laser Scanning Digital Imaging Technology
AU - Mao, Zirui
AU - Zhang, Chaolong
AU - Guo, Benjun
AU - Xu, Yuanping
AU - Kong, Chao
AU - Zhu, Yue
AU - Xu, Zhijie
AU - Jin, Jin
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/12
Y1 - 2023/12
N2 - With the development of intelligent manufacturing, the production and assembly accuracy of components in factories is increasing in line with growing demand. However, the traditional manual quality inspection is inefficient, inaccurate, and costly. To this end, digital and optical imaging techniques are used to achieve intelligent quality inspection. However, during the reconstruction process, the high reflectivity of object materials affects the speed and accuracy of reconstruction results. To overcome these problems, this study investigated the three-dimensional (3D) digital imaging techniques based on line laser scanning. It advances a novel methodology for image segmentation, underpinned by deep learning algorithms, to augment the precision of the reconstruction results while simultaneously enhancing processing velocity. After the reconstruction phase, the research assesses flatness tolerance using point cloud registration technology. Finally, we constructed a measurement platform with a cost of less than CNY 100,000 (about USD 14,000) and obtained a measurement accuracy of 30 microns.
AB - With the development of intelligent manufacturing, the production and assembly accuracy of components in factories is increasing in line with growing demand. However, the traditional manual quality inspection is inefficient, inaccurate, and costly. To this end, digital and optical imaging techniques are used to achieve intelligent quality inspection. However, during the reconstruction process, the high reflectivity of object materials affects the speed and accuracy of reconstruction results. To overcome these problems, this study investigated the three-dimensional (3D) digital imaging techniques based on line laser scanning. It advances a novel methodology for image segmentation, underpinned by deep learning algorithms, to augment the precision of the reconstruction results while simultaneously enhancing processing velocity. After the reconstruction phase, the research assesses flatness tolerance using point cloud registration technology. Finally, we constructed a measurement platform with a cost of less than CNY 100,000 (about USD 14,000) and obtained a measurement accuracy of 30 microns.
KW - 3D measurement
KW - deep learning
KW - digital imaging
KW - flatness error
KW - line laser scanning
UR - http://www.scopus.com/inward/record.url?scp=85180701218&partnerID=8YFLogxK
U2 - 10.3390/photonics10121333
DO - 10.3390/photonics10121333
M3 - Article
AN - SCOPUS:85180701218
SN - 2304-6732
VL - 10
JO - Photonics
JF - Photonics
IS - 12
M1 - 1333
ER -