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IJAT Vol.10 No.1 pp. 69-77
doi: 10.20965/ijat.2016.p0069
(2016)

Paper:

On-Machine Surface Texture Measuring System Using Laser Speckle Pattern Analysis

Motochika Shimizu*,**, Hiroshi Sawano***, Hayato Yoshioka***, and Hidenori Shinno***

*Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
4259-G2-19 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan

**Research Fellow of Japan Society for the Promotion of Science

***Precision and Intelligence Laboratory, Tokyo Institute of Technology
4259-G2-19 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan

Received:
July 25, 2015
Accepted:
October 5, 2015
Online released:
January 4, 2016
Published:
January 5, 2016
Keywords:
optical measurement, on-machine measurement, surface texture, laser speckle
Abstract

Functional surfaces are expected technologies in various industries. However, the efficient generation of microscale surface structures is difficult because the process relies on precision surface texture assessments, which require long measuring times. An on-machine measuring system may solve this issue. In this study, a new on-machine surface texture measuring system based on laser speckle pattern analysis is proposed. The proposed system efficiently assesses various qualities of the surface texture, such as surface roughness, undulation of microscale surface structures, and anisotropy, from a laser speckle pattern obtained from the precision-machined surface. For precise measurements, disturbances caused by the machining system itself and the environment must be avoided. The laser speckle detection unit in the proposed system is supported by non-contact active vibration-isolation units, which reduce the transmission of ground vibrations. From the experimental results, the system indicated high repeatability in measurements and robustness against disturbances.

Cite this article as:
M. Shimizu, H. Sawano, H. Yoshioka, and H. Shinno, “On-Machine Surface Texture Measuring System Using Laser Speckle Pattern Analysis,” Int. J. Automation Technol., Vol.10, No.1, pp. 69-77, 2016.
Data files:
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Last updated on Dec. 05, 2019