The Flatness Error Evaluation of Metal Workpieces Based on Line Laser Scanning Digital Imaging Technology

Zirui Mao, Chaolong Zhang, Benjun Guo, Yuanping Xu, Chao Kong, Yue Zhu, Zhijie Xu, Jin Jin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Article number1333
Number of pages23
JournalPhotonics
Volume10
Issue number12
Early online date30 Nov 2023
DOIs
Publication statusPublished - 1 Dec 2023

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