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A Novel Foam Coating Approach to Produce Abrasive Structures on Textiles

Kenttä Eija, Kumar Vinay, Andersson Petter, Forsström Ulla

Autex Research Journal

2022

Vol. 22, no. 3

335--342

Abrasive materials are classified as paper, nonwoven, or plastic-based multilayer structures, which are used for different kinds of surface finishing. Currently, the production of abrasive structures on textiles is carried out by spraying a slurry of binder and abrasive particles, e.g., Al2O3 or SiC, with subsequent drying and curing of the binder. The drawback of this production method is the poor runnability of the spraying process. Even small variations in the process parameters may lead to an uneven coating. Therefore, a novel coating approach was developed to produce abrasive structures with foam coating on textile substrates. The foam coating method, which is commonly used in the textile industry, has the potential to produce an even coating layer. The runnability and reliability of the foam coating process are good even with high solids. From a workplace safety perspective, another advantage of foam coating is that there are no airborne particles during the coating process. A polyamide woven cloth was foam coated with an aqueous slurry containing abrasive grains (SiC), a water-based UV-curable acrylate binder, and cellulose nanocrystals (CNCs) to adjust the slurry rheology. Stable abrasive-binder foams were generated from the slurries even at high solids of 50% using an anionic foaming agent. The cloth was foam coated and dried, and the resin was cured with a LED-UV lamp on a pilot scale. It was observed that without the addition of CNC the foam did not stay on the surface of the cloth after coating. CNC acts as a rheology modifier and co-binder, which prevent the foam from penetrating deeper into the pores of the cloth. CNC also acted as a dispersing agent: the slurry was effectively stabilized by the CNC to prevent sedimentation of the abrasive grains. An organic solvent-free composition was introduced by combining CNC with a water-based UV-resin.

A Novel Two-stage Residual Learning Based Convolutional Neural Network for Image Super Resolution

Sharma Shailza, Bawa Vivek Singh, Kumar Vinay

Fundamenta Informaticae

2019

Vol. 168, nr 2-4

335--351

Image super resolution has gained a lot of attention due to its applications in different fields of image processing. It is used to produce high-resolution images from low-resolution input. Because of the excellent learning capability of convolution neural networks, these networks are able to learn complex spatial structures for image super-resolution. In this paper, two different architectures have been proposed for image super resolution. The first architecture is Dual Subpixel Layer Convolution Neural Network (DSL-CNN), which stacks two subpixel CNN architectures to enhance model depth for better representational capability. Two stages provide an effective upscaling factor of 4. In the second architecture, named as Residue based Dual Subpixel Layer Convolution Neural Network (RDSL-CNN), two-stage residual learning has been introduced which effectively sustains the high frequency details and provides superior results than the previous state-of-the-art methods. The performance of the two architectures has been evaluated on various image datasets, and compared with other state-of-the-art methods.