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Development of a Monitoring Strategy for Laser-Textured Metallic Surfaces Using a Diffractive Approach.

ABSTRACT: The current status of research around the world concurs on the powerful influence of micro- and nano-textured surfaces in terms of surface functionalization. In order to characterize the manufactured topographical morphology with regard to the surface quality or homogeneity, major efforts are still required. In this work, an optical approach for the indirect evaluation of the quality and morphology of surface structures manufactured with Direct Laser Interference Patterning (DLIP) is presented. For testing the designed optical configuration, line-like surface patterns are fabricated at a 1064 nm wavelength on stainless steel with a repetitive distance of 4.9 µm, utilizing a two-beam DLIP configuration. Depending on the pulse to pulse overlap and hatch distance, different single and complex pattern geometries are produced, presenting non-homogenous and homogenous surface patterns. The developed optical system permitted the successfully classification of different pattern geometries, in particular, those showing single-scale morphology (high homogeneity). Additionally, the fabricated structures were measured using confocal microscopy method, and the obtained topographies were correlated with the recorded optical images.

SUBMITTER: Teutoburg-Weiss S

PROVIDER: S-EPMC6981385 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Development of a Monitoring Strategy for Laser-Textured Metallic Surfaces Using a Diffractive Approach.

Teutoburg-Weiss Sascha S Voisiat Bogdan B Soldera Marcos M Lasagni Andrés Fabián AF

Materials (Basel, Switzerland) 20191220 1

The current status of research around the world concurs on the powerful influence of micro- and nano-textured surfaces in terms of surface functionalization. In order to characterize the manufactured topographical morphology with regard to the surface quality or homogeneity, major efforts are still required. In this work, an optical approach for the indirect evaluation of the quality and morphology of surface structures manufactured with Direct Laser Interference Patterning (DLIP) is presented. ...[more]

PMID: 31861907

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Project description:Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components. Here, we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (Ni) and output (No), where Ni and No represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary, and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is ≥Ni × No, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N < Ni × No. These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive surfaces.

Dataset Information

Development of a Monitoring Strategy for Laser-Textured Metallic Surfaces Using a Diffractive Approach.

Publications

Development of a Monitoring Strategy for Laser-Textured Metallic Surfaces Using a Diffractive Approach.

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