Method and device for determining the layer thickness distribution in a paint layer

Abstract

A method for determining a layer thickness distribution in a paint layer produced during paint spraying after inputting specific spraying parameters into an electrostatically based paint spraying device. A data processing device sets up and uses a phenomenological mathematical model of a quasi-stationary three-dimensional spray pattern. Specific parameters, such as an angle of rotation of electrodes and a rate of movement of the spraying device are input into the phenomenological model as fixed input parameters. In addition, real physical input parameters such as paint volume, directing air data and a voltage value, whose influence on the spraying result is not accurately known, are fed to an artificial neural network. The neural network having been previously trained using real input data such as a configuration of the spraying device, a paint type, operating parameters, and measured values of the layer thickness distribution. The neural network carries out a conversion of the input parameters into model input parameters which are fed to the phenomenological model. Spray patterns formed by the phenomenological model are integrated in a further functional unit as a function of movement data of the spraying device which are contained in the input parameters to form the overall paint layer which is output.