Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88968
Title: A non-contact measuring system for in-situ surface characterization based on laser confocal microscopy
Authors: Fu, Shaowei
Cheng, Fang
Tjahjowidodo, Tegoeh
Zhou, Yu
Butler, David
Keywords: Non-contact
Surface Roughness
DRNTU::Engineering::Mechanical engineering
Issue Date: 2018
Source: Fu, S., Cheng, F., Tjahjowidodo, T., Zhou, Y., & Butler, D. (2018). A non-contact measuring system for in-situ surface characterization based on laser confocal microscopy. Sensors, 18(8), 2657-. doi:10.3390/s18082657
Series/Report no.: Sensors
Abstract: The characterization of surface topographic features on a component is typically quantified using two-dimensional roughness descriptors which are captured by off-line desktop instruments. Ideally any measurement system should be integrated into the manufacturing process to provide in-situ measurement and real-time feedback. A non-contact in-situ surface topography measuring system is proposed in this paper. The proposed system utilizes a laser confocal sensor in both lateral and vertical scanning modes to measure the height of the target features. The roughness parameters are calculated in the developed data processing software according to ISO 4287. To reduce the inherent disadvantage of confocal microscopy, e.g., scattering noise at steep angles and background noise from specular reflection from the optical elements, the developed system has been calibrated and a linear correction factor has been applied in this study. A particular challenge identified for this work is the in-situ measurement of features generated by a robotized surface finishing system. The proposed system was integrated onto a robotic arm with the measuring distance and angle adjusted during measurement based on a CAD model of the component in question. Experimental data confirms the capability of this system to measure the surface roughness within the Ra range of 0.2–7 μm (bandwidth λc/λs of 300), with a relative accuracy of 5%.
URI: https://hdl.handle.net/10356/88968
http://hdl.handle.net/10220/46015
ISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s18082657
Rights: © 2018 by The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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