Abstract
In the design of multiple response parameters optimization, weighted principal component analysis (weighted PCA) is used to build the relationship between the response variables and controllable factor model by linear regression. But in the complicated nonlinear production process, the fit of the linear regression model is not high that cannot satisfy the requirement of the parameter design model. This study proposed an improved weighted PCA based on RBF neural network prediction model. In this paper, RBF neural network was used to construct nonlinear prediction model of production process and to adjust the weighted PCA algorithm by adding the predict ability index of neural network model. In the design of multiple response parameters, this approach improve the effect of process parameters optimization. And applied this method to multiple response parameters optimization design of metallization polypropylene film capacitor thermal polymerization process, the results show that capacitance value and the loss tangent are all improved, and the effect of optimization parameters is achieve to satisfactory results.
Highlights
With the complexity of the production process and product quality requirements, it is need to consider multiple quality characteristics in the process of product optimization
The quality loss function and satisfaction function method have an extensive application in the multiple response optimization design, but it ignores the correlation between the response variables, which will affect the effect of optimization design [1]
This paper proposed an improved principal component analysis (PCA) method based on radial basis function(RBF) neural network for multiple response parameters optimization
Summary
With the complexity of the production process and product quality requirements, it is need to consider multiple quality characteristics in the process of product optimization. Zhang Yingdong [6] proposed an improved weighted principal component analysis(PCA) which can construct a mathematical model between the impact factor and multiple response variable. This paper proposed an improved PCA method based on radial basis function(RBF) neural network for multiple response parameters optimization. It is priority to improve the response variable which have a strong prediction ability, and realize the overall effect of multiple response optimization. Applied this method for the metallized polypropylene film capacitor, which solving the parameters optimization problem of the polymerization temperature and polymerization time, and realizing the whole optimization of the capacitor value and the loss tangent
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