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Sensors for Optical Metrology 2022
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Sensors for Optical Metrology 2022

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Nanosensors".

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 4869

Special Issue Editor

Prof. Dr. Han Haitjema

E-Mail Website
Guest Editor
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Interests: dimensional metrology; interferometry; surface roughness; surface filtering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together researchers active in the research, development, calibration, and characterization of sensors that are applied in optical metrology in order to achieve the highest accuracy and, thus, the lowest uncertainty.

In the continuous development towards higher speed, lower cost, more data, smaller acquisition times, better traceability, less user intervention, and more autonomous systems, the development, characterization, and calibration of sensors for optical metrology is even more immanent than before.

Works on classical technologies, such as Fizeau interferometers and displacement laser interferometers, are welcome, as well as those based on more recent innovative techniques, such as frequency combs and optical clocks, instantaneous surface profiling, areal chromatic confocal measurements, multi-wavelength digital holography, wavefront sensors, laser distance sensors, etc. Manuscripts on the overcoming of challenges, related, for example, to in-process use of sensors for accurate surface measurements, will also be appreciated.

I cordially invite you to share your work, expertise, and insights with the optical measurement and calibration community in the form of research articles and reviews.

Prof. Dr. Han Haitjema
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor
  • frequency comb
  • interferometry
  • digital holography
  • calibration
  • triangulation
  • free form optics
  • traceability
  • chromatic confocal sensor

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Published Papers (2 papers)

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Research

18 pages, 6461 KiB  
Article
Comparison of Correlation between 3D Surface Roughness and Laser Speckle Pattern for Experimental Setup Using He-Ne as Laser Source and Laser Pointer as Laser Source
by Suganandha Bharathi Jayabarathi and Mani Maran Ratnam
Sensors 2022, 22(16), 6003; https://doi.org/10.3390/s22166003 - 11 Aug 2022
Cited by 7 | Viewed by 2293
Abstract
Correlation between 3D surface roughness and characteristic features extracted from laser speckle pattern was done using an inexpensive laser pointer and a digital single lens reflex (DSLR) camera in previous research work. There had been no comparison work done between the experimental setup [...] Read more.
Correlation between 3D surface roughness and characteristic features extracted from laser speckle pattern was done using an inexpensive laser pointer and a digital single lens reflex (DSLR) camera in previous research work. There had been no comparison work done between the experimental setup which uses a laser pointer, which has a diode laser as the laser source, and the experimental setup, which uses a He-Ne laser as the laser source. As such, in the current work, a comparison study between two experimental setups was carried out. One experimental setup was using a He-Ne laser, spatial filter, and charged coupled device (CCD) camera, while another experimental setup was using a laser pointer and DSLR camera. The laser beam was illuminated at angles of 30°, 45°, and 60° from the horizontal. When a laser beam falls on the surface, the beam gets scattered, and the scattered beam undergoes interference and produces speckle patterns which are captured using a camera. Using a Matlab program, the gray level co-occurrence matrix (GLCM) characteristic features, such as contrast (GLCM), correlation (GLCM), energy (GLCM), entropy (GLCM), homogeneity (GLCM), and maximum probability, and non-GLCM characteristic features, such as mean, standard deviation (STD), uniformity, entropy, normalized R, and white-to-black ratio (W/B), were extracted and correlated with 3D surface roughness parameters. The coefficient of determination (R2) was determined for each case. Compared to the setup using a laser pointer, the setup using a He-Ne laser gave better results. In the setup using the He-Ne laser, there were correlations with a coefficient of determination R2 ≥ 0.7 at illumination angles of 30°, 45°, and 60°, whereas in the setup using a laser pointer, there were correlations with R2 ≥ 0.7 at illumination angles of 30° and 45°. Mean characteristic features had more correlations with R2 ≥ 0.7 in the case of the angle of illumination of 45° (7 out of 36 correlations) and 60° (11 out of 82 correlations), while R-normalized characteristic features had more correlations with R2 ≥ 0.7 in the case of the angle of illumination of 30° (9 out of 38 correlations) for the setup using the He-Ne laser. Correlation (GLCM) had more correlations with R2 ≥ 0.7 in the case of the setup using a laser pointer (2 out of 2 correlations for illumination angle of 30°, and 4 out of 19 correlations for an illumination angle of 45°). Roughness parameters Sa and Sq had more correlations with R2 ≥ 0.7 for an illumination angle of 30° (1 out of 2 correlations each), and Sp and Sz had more correlations with R2 ≥ 0.7 for an illumination angle of 45° (4 out of 19 correlations each) in the case of the setup using a laser pointer. The novelty of this work is (1) being a correlation study between 3D surface roughness and speckle pattern using a He-Ne laser and spatial filter, and (2) being a comparison study between two experimental setups on the correlation between 3D surface roughness and speckle pattern. Full article
(This article belongs to the Special Issue Sensors for Optical Metrology 2022)
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16 pages, 40434 KiB  
Article
Correlation Study of 3D Surface Roughness of Milled Surfaces with Laser Speckle Pattern
by Suganandha Bharathi Jayabarathi and Mani Maran Ratnam
Sensors 2022, 22(8), 2842; https://doi.org/10.3390/s22082842 - 7 Apr 2022
Cited by 6 | Viewed by 1987
Abstract
Current studies are focused on the correlation between characteristic features extracted from the laser speckle pattern of machined surfaces and 2D surface roughness parameters. Since milled surfaces are 3D in nature, 3D surface roughness parameters will provide a more accurate representation of the [...] Read more.
Current studies are focused on the correlation between characteristic features extracted from the laser speckle pattern of machined surfaces and 2D surface roughness parameters. Since milled surfaces are 3D in nature, 3D surface roughness parameters will provide a more accurate representation of the surface. Novelties of this work are: (1) an inexpensive laser pointer, which was used for presentation and was used without any spatial filtering setup for producing the laser speckle pattern; (2) a correlation study, which was conducted between characteristic features extracted from the speckle pattern and 3D surface roughness; and (3) the influence of angle of illumination, lens aperture size (f-number) and shutter speed on the correlation. A highest coefficient of determination of 0.8955 was obtained for the correlation between the gray level co-occurrence matrix descriptor, namely energy, and 3D surface roughness parameter, namely ten-point height S10z, at an illumination angle of 45°, f-number of 16 and shutter speed of 1/100 s. Full article
(This article belongs to the Special Issue Sensors for Optical Metrology 2022)
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