Abstract
In order to understand the relationship between discharge plasma parameters and material surface properties, neural networks model were constructed. The sample data are yielded from many experiments for polypropylene surface modification. The experiments were arranged according to uniform design method and conducted in home-made dielectric barrier discharge (DBD) system. Here, voltage, air gap and discharge time were input parameters of the model. The output parameter was polypropylene surface water contact angle. Backpropagation algorithm was used to train neural networks model. Model evaluation was carried out by simulation and error analysis. The optimized model was applied to predict, and the results are in agreement with practical situation. The obtained neural networks model has excellent predictive capability.
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