| Abstract: | ![]()
With the rapid development of rubber industry, it becomes more and more important to improve the performance of the quality control system of rubber mixing process. Unfortunately, the large measurement time delay of Mooney viscosity, one of the most important quality parameters of mixed rubber, badly blocks the further development of the issue. The independent component regression‐Gaussian process (ICR‐GP) algorithm is used to solve such typical nonlinear “black‐box” regression problem for the first time to predict Mooney viscosity. In the ICR‐GP method, the non‐Gaussian information is extracted by the independent component regression method firstly, and then the residual Gaussian information is extracted by the Gaussian process method. Meanwhile, both the linear and nonlinear relationships between the input and output variables can be extracted through the ICR‐GP method. With the fact that there is no need to optimize parameters, the ICR‐GP method is especially suitable for “black‐box” regression problems. The highest prediction accuracy was achieved at M = 0.8765 (the root mean square error), which was high enough considering the measuring accuracy (M = ±0.5) of the Mooney viscometer. It is by using the online‐measured rheological parameters as the input variables that the measurement time delay of Mooney viscosity could be dramatically decreased from about 240 to 2 min. Consequently, such Mooney‐viscosity prediction model is very helpful for the development of the rubber mixing process, especially of the emerging one‐step rubber mixing technique. The practical applications performed on the rubber mixing process in a large‐scale tire factory strongly proved the outstanding regression performance of this ICR‐GP Mooney‐viscosity prediction model. Copyright © 2012 John Wiley & Sons, Ltd. |
