Open Access

Benjamin Peric, Michael Engler, Katja Gutsche, Peter Woias

• This article presents a method for predicting the behavior of external gear pumps using a neural network, to optimize a higher-level dosing process. Sparse neural networks learn to map the characteristic curves of fluid machinery. The presented method achieves an error flow rate of approximately 3.28 milliliters per minute. The neural networks make it possible to directly substitute the volume flow sensor in order to design dosing applications without cost-intensive volume flow sensors. However, due to the existing manufacturing tolerances of the used pumps, the overall pump efficiency differs significantly. For this reason, it is not directly possible to apply the networks universally to an entire pump series, which strongly limits the industrially scalable solution. For this reason, the algorithms are extended by a linear regression model, which enables users to calibrate the neural networks to the individual pumps based on very few reference measurements.