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Vulcanizing Process Parameter Robust Optimization Based on an Improved Genetic Algorithm

Abstract Vulcanization is an important process to manufacture water-lubricated metal rubber bearing. Since vulcanizing quality of the bearing is mostly influenced by process parameters, how to determine the optimal process parameters becomes the key to improving the part quality. In this paper, a radial basis function artificial neural network is developed to map the non-linear relationship between process parameters and quality indexes of the water-lubricated metal rubber bearing, and an improved genetic algorithm method is applied in the process parameters robust optimization. At the same time, converting multiple performance indexes into a single overall performance index by compromise programming based on expert fuzzy eclectic preferences, and building product quality sensitivity function as the fitness function of the improved genetic algorithm. The result shows that the method based on an improved GA is an effective tool for the process robust optimization, and comparing with the Taguchi DOE technique , the overall performance of the bearing has significantly improved under the robust optimal process parameters.