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Proceedings Paper

Monte Carlo simulation for scanning white light interference measuring data of fracture surface
Author(s): Binwei Yang; Yufang Liu; Wendong Zou
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Paper Abstract

Scanning white light interferometry (SWLI) has become an established method to measure the geometrical form of objects. However, due to its complicated condition on the fracture surface, the noises severely degrade its interference signal quantity, consequently cause large reconstruction error. To improve the reconstruction accuracy, this paper proposes a method which adopts the Monte Carlo algorithm to simulate the interference data of a fracture surface. In our method, a random Gaussian surface is first generated as the fracture surface in the following experiments. Then, a numerical model of white light interference is constructed and thereby interference images based on Gaussian surfaces are simulated by the use of Monte Carlo method. In the experiments, the influence of surface parameters and repeated times of calculation on the results of simulation is observed and the process of longitudinal equi-distance step scanning in interference detection is simulated. The experimental results show that when the repeated times is large enough, the characteristics on the interference image becomes obvious. By observing the longitudinal interference signal, the interference curve shows similar properties to a interferogram signal. Finally, we analyze the theoretical feasibility of a parallel integral filter for the experimentally measured interference data. The results prove that the simulated interference data possesses primary capability in restoring surface information.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113380X (18 December 2019); doi: 10.1117/12.2542825
Show Author Affiliations
Binwei Yang, Nanchang Hangkong Univ. (China)
Yufang Liu, Nanchang Hangkong Univ. (China)
Wendong Zou, Nanchang Hangkong Univ. (China)

Published in SPIE Proceedings Vol. 11338:
AOPC 2019: Optical Sensing and Imaging Technology
John E. Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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