Categorization of Sprays by Image Analysis with Convolutional Neuronal Networks. Issue 2 (18th November 2022) - Pieloth, Damian; Rodeck, Matthias; Schaldach, Gerhard; Thommes, Markus
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. Abstract : An optical quality control system, capable of characterizing sprays with a predefined specification, is developed. Categorizing sprays by convolutional neuronal networks is described. The median drop size is determined with a high accuracy. The droplet size distribution estimations made by this method deviated from the measurements from the reference method (laser diffraction) by less than 1.5 %.