Pieloth, DamianRodeck, MatthiasSchaldach, GerhardThommes, Markus2023-10-202023-10-202022-11-04http://hdl.handle.net/2003/4216410.17877/DE290R-23997Spray 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.enChemical engineering & technology;46(2)https://creativecommons.org/licenses/by-nc-nd/4.0/Convolutional neural networksDroplet sizeImage analysisMachine learningSpray categorization660ResearchArticleZellulares neuronales NetzTropfengrößeBildanalyseMaschinelles LernenZerstäubung