Roughness measurement of metals using a modified binary speckle image and adaptive optics

Fuh, Yiin Kuen; Hsu, Kuo Chan; Fan, Jia Ren

doi:10.1016/j.optlaseng.2011.11.003

Cited by 37 publications

(16 citation statements)

References 16 publications

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“…Figure 6 shows the relation between cell spreading and different positioning densities using a binary image method as reported previously [36,37]. Cell viabilities and spreading after culture for 1 and 3 days with various positioning densities of CNF are illustrated.…”

Section: Resultsmentioning

confidence: 89%

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Direct-write, highly aligned chitosan-poly(ethylene oxide) nanofiber patterns for cell morphology and spreading control

Fuh

Chen

2013

Nanoscale Res Lett

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Near-field electrospinning has been demonstrated to be able to achieve direct-write and highly aligned chitosan nanofibers (CNF) with prescribed positioning density. Cell spreading in preferential direction could be observed on parallel-aligned nanofibers, and the CNF patterns were capable of guiding cell extension when the distances between them are 20 and 100 μm, respectively. Alignment of the cells was characterized according to their elongation and orientation using the fast Fourier transform data and binary image analysis. Parallel CNF indicates that the alignment values sequentially increased as a function of positioning density such that incrementally more aligned cells were closely related to the increasing CNF positioning density. These maskless, low-cost, and direct-write patterns can be facily fabricated and will be a promising tool to study cell-based research such as cell adhesion, spreading, and tissue architecture.

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Section: Resultsmentioning

confidence: 89%

“…Here the binary image analysis [36,37] of pixel counts for dark (D) and bright (B) regions are taken from the optical images of cells cultured for 1 and 3 days to account for cell spreading. In the binary processing, it should be noted that B region counts decrease and D region counts increase with the increase in cell spreading.…”

Section: Resultsmentioning

confidence: 99%

Direct-write, highly aligned chitosan-poly(ethylene oxide) nanofiber patterns for cell morphology and spreading control

Fuh

Chen

2013

Nanoscale Res Lett

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“…It has been confirmed that the BRDF method is accurate and practical in scattered light measurement. Furthermore, speckle image systems are similar to two-axis scatterometry systems, but the amount of information obtained is less than that in three-axis scatterometry [28], [29]. Speckle pattern can evaluate the value of surface roughness but cannot display a clear surface image [30]- [36].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Surface Micro-Roughness by Off-Specular Measurements of Polarized Optical Scattering

Liu¹,

Chang²

2019

Measurement Science Review

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The characterization of surface micro-roughness is investigated by using off-specular measurements of polarized optical scattering. In the measurement system, the detection angles of optical scattering are defined by the vertical and level scattering angles. The rotating mechanism of angles is controlled by stepper motors. Waveplate and polarizer are used to adjust light polarization and detection. We conduct the optical scattering measurements by using four standard metal sheets of surface roughness. The nominal values (Ra) of standard micro-roughness are 1.6 μm, 0.8 μm, 0.4 μm, and 0.1 μm, respectively. Samples with different surface roughness are evaluated with the utilization of laser sources at three incident wavelengths. These polarized images are analyzed using a computer program to obtain the distribution of light intensity. The results show great correlation between the metal surface roughness and polarization states. This measurement system can be used to quickly and accurately distinguish between different surfaces and properties.

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“…They showed that as the normalized brightness to dark ratio decreased, the roughness increased as a first order exponential decay function. Several other studies have implemented a similar method in determining surface roughness using light scattering [19][20][21]. Even though most of the research on binary speckle pattern imaging uses coherent light which was not available, the same principles and techniques were tried and experimented with in this project.…”

Section: Tests For Evaluating Powder Propertiesmentioning

confidence: 99%

“…To determine the optimal threshold to divide the classes of pixels, the function graythresh was used, which uses Otsu's Method to minimize the "within-class variance" of foreground and background pixels, which is the sum of the two variances multiplied by their weight [35]. Based on the results of Gontard et al [36], with gradually increasing subdivisions, more particles should be detected correctly, but Fig. 4-11 did not reflect that.…”

Section: Thresholdingmentioning

confidence: 99%

Powder Bed Surface Quality and Particle Size Distribution for Metal Additive Manufacturing and Comparison with Discrete Element Model

Yee¹

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Metal additive manufacturing (AM) can produce complex parts that were once considered impossible or too costly to fabricate using conventional machining techniques, making AM machines an exceptional tool for rapid prototyping, one-off parts, and labor-intensive geometries. Due to the growing popularity of this technology, especially in the defense and medical industries, more researchers are looking into the physics and mechanics behind the AM process. Many factors and parameters contribute to the overall quality of a part, one of them being the powder bed itself. So far, little investigation has been dedicated to the behavior of the powder in the powder bed during the lasering process. A powder spreading machine that simulates the powder bed fusion process without the laser was designed by Lawrence Livermore National Laboratory and was built as a platform to observe powder characteristics. The focus for this project was surface roughness and particle size distribution (PSD), and how dose rate and coating speed affect the results. Images of the 316L stainless steel powder on the spreading device at multiple layers were taken and processed and analyzed in MATLAB to access surface quality of each region. Powder from nine regions of the build plate were also sampled and counted to determine regional particle size distribution. As a comparison, a simulation was developed to mimic the adhesive behavior of the powder, and to observe how powder distributes powder when spread. v ACKNOWLEDGMENTS I would first like to thank Dr. Davol for being the calm and collected advisor that I needed for this thesis. Your guidance always put me right back on track while your positivity calmed my nerves. I would like to thank Dr. Mayer for always asking the questions that push me to keep thinking and improving. From you, I have learned to take pride in my work. I would also like to thank Dr. Wang for all of his support throughout this project. I am grateful for the opportunities and encouragement you provided and your willingness to help at a moment's notice.

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Roughness measurement of metals using a modified binary speckle image and adaptive optics

Cited by 37 publications

References 16 publications

Direct-write, highly aligned chitosan-poly(ethylene oxide) nanofiber patterns for cell morphology and spreading control

Direct-write, highly aligned chitosan-poly(ethylene oxide) nanofiber patterns for cell morphology and spreading control

Characterization of Surface Micro-Roughness by Off-Specular Measurements of Polarized Optical Scattering

Powder Bed Surface Quality and Particle Size Distribution for Metal Additive Manufacturing and Comparison with Discrete Element Model

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