The present invention overcomes the problems in the prior art by providing in one embodiment a method of optimizing a painting process for applying a paint layer on an article. The method comprises defining a functional relationship between paint processing cheap oil paintings parameters and a paint layer property (i.e., the average paint layer thickness) using a neural network.
This functional relationship is then used in a paint optimization function abstract oil paintings that measures a combination of quality control parameters and paint transfer efficiency. Finally, the paint optimization function is optimized by adjusting the paint processing parameters utilizing the functional relationship formed by the neural network (“NN”).
The method of the invention is advantageously oil paintings used to establish a global model of the paint process that can be used to predict for a given combination of environmental factors such as down draft (at the bell zone and reciprocator zone), air temperature, and air humidity, and average fluid flow rate the average film thickness on a particular surface of the vehicle body. This prediction is further used to calculate the optimal average fluid flow rates for the left, vertical and horizontal bells, and the optimal down drafts in the bell and reciprocator zones. Accordingly, the methods of the invention can be used to predict the impact of different combinations of process parameters on the final film thickness.
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