Implementation of empirical modified generalized Harvey–Shack scatter model on smooth surface

Ma, Zhanpeng; Wang, Hu; Chen, Qinfang; Xue, Yaoke; Pan, Yue; Shen, Yang; Yan, Haoyu

doi:10.1364/josab.455182

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…This is due to the GHS theory of fitting on smooth surfaces and the use of microscope objectives in existing measuring machines. Ma et al 9 have added experimental corrections to the GHS theory, which we hope to use in future developments. The use of an objective lens results in a shallow depth of focus and is considered unsuitable for measuring curved surfaces.…”

Section: Discussionmentioning

confidence: 99%

Non-contact surface roughness measurement with a light-field camera

Igarashi

Mitsunari

Yamazaki

2022

Optical Metrology and Inspection for Industrial Applications IX

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A new non-contact surface roughness measurement technology has been developed. This is a technique that helps ensure the traceability of each component in the manufacturing process. Specifically, the surface roughness parameters Sq and Sdq were measured and evaluated using scattered light simulation and a light field camera. The roughness parameter Sq was determined using the generalized Harvey-Shack scattering theory and showed good agreement with existing machines. Furthermore, the roughness parameter Sdq was estimated from the argument “a” extracted from the K-correlation model.

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Section: Discussionmentioning

confidence: 99%

Non-contact surface roughness measurement with a light-field camera

Igarashi

Mitsunari

Yamazaki

2022

Optical Metrology and Inspection for Industrial Applications IX

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“…In the face of the BSDF, the measured BSDF data is usually used to accurately characterize the information of the surface. [28] , , ( )…”

Section: Ray Tracing Based On Monte Carlo Methodsmentioning

confidence: 99%

Research on stray light suppression of opto-mechanical system based on deep reinforcement learning models

yang,

xing,

Huang

et al. 2023

Preprint

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Stray light suppression constitutes a vital aspect in the development of opto-mechanical systems, but its complexity and the uncertainty surrounding scattered light necessitate intricate mathematical calculations and ample simulation iterations, along with extensive expertise and time. Consequently, researching stray light suppression in opto-mechanical systems becomes a time-consuming and challenging endeavor. To validate the feasibility of using reinforcement learning for stray light suppression, this study adopts a model-based deep reinforcement learning approach within a Monte Carlo ray-tracing environment to devise suppression strategies. The experimental results demonstrate that the model-based deep reinforcement learning method can propose effective stray light suppression measures tailored to various optical system configurations, resulting in significant improvements in suppression efficiency.

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“…After the approximation to Eqs. ( 22)-( 24), the BRDF of GHS model for the smooth surface is expressed as [26,29]…”

Section: B Generalized Harvey-shack Scatter Theorymentioning

confidence: 99%

“…(37). See [29] for more information on BRDF measurement of this material and the detailed calculation of PSD fitting.…”

Section: A Psd Calculation and Fittingmentioning

confidence: 99%

Harvey–Shack theory for a converging–diverging Gaussian beam

Hansen²,

wang

et al. 2023

J. Opt. Soc. Am. B

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The scattering characteristics of random rough surfaces illuminated with a 3D converging–diverging Gaussian beam are investigated by applying the conventional Harvey–Shack theory in conjunction with 2D plane-wave decomposition. The Gaussian beam is assumed to have an arbitrary angle of incidence and to be linearly s-polarized. Using data obtained from laser BRDF measurements on isotropic random rough surfaces with low surface roughness, we demonstrate that the Gaussian beam Harvey–Shack theory is in better accordance with the experimental data than the conventional Harvey–Shack theory. The two models become identical for a large beam waist radii but are significantly different for smaller ones.

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Implementation of empirical modified generalized Harvey–Shack scatter model on smooth surface

Cited by 7 publications

References 27 publications

Non-contact surface roughness measurement with a light-field camera

Non-contact surface roughness measurement with a light-field camera

Research on stray light suppression of opto-mechanical system based on deep reinforcement learning models

Harvey–Shack theory for a converging–diverging Gaussian beam

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