Authors: | Pieloth, Damian Rodeck, Matthias Schaldach, Gerhard Thommes, Markus |
Title: | Categorization of sprays by image analysis with convolutional neuronal networks |
Language (ISO): | en |
Abstract: | Spray characterization has been an issue for process and product characterization for decades. Because of this, a convolutional neuronal network was developed to determine the droplet size from spray images. The images were taken using a digital camera, a light source, and a dark room. These were subsequently employed to design and train a convolutional neuronal network using open-source software packages and a desktop computer. The accuracy of the network droplet size determinations was checked with additional, independent images. The median drop size was assessed with a high accuracy of more than 99.8 % as the mean spray performance indicator. Additionally, the droplet size distribution measurements from the neural network method deviated from those from the reference method (laser diffraction) by less than 1.5 %. Convolutional neuronal networks can be applied to determine the spray performance using spray cone images. This approach could be useful for multiple applications. |
Subject Headings: | Convolutional neural networks Droplet size Image analysis Machine learning Spray categorization |
Subject Headings (RSWK): | Zellulares neuronales Netz Tropfengröße Bildanalyse Maschinelles Lernen Zerstäubung |
URI: | http://hdl.handle.net/2003/42164 http://dx.doi.org/10.17877/DE290R-23997 |
Issue Date: | 2022-11-04 |
Rights link: | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Appears in Collections: | Lehrstuhl Feststoffverfahrenstechnik |
Files in This Item:
File | Description | Size | Format | |
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Chem Eng Technol - 2022 - Pieloth - Categorization of Sprays by Image Analysis with Convolutional Neuronal Networks.pdf | DNB | 1.61 MB | Adobe PDF | View/Open |
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