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Most glass composites products in engineering are manufactured by a pultrusion process. Because the pultrusion process is affected by many factors, it is not easy to control the performance of products. In order to obtain good performance and meet the requirements of glass composites products in specific parameters, artificial neural network technology was adopted to optimize and design the pultrusion process. The results showed that the curing degree of products increased gradually with the preheated mould temperature. During the reverse design of glass composites products, it was found that when the spread is 0.3, the measured temperature values of every stage based on curing degree were in good agreement with the forecasting values, which proved the correctness of the reverse design. The research method in this paper provides important theoretical basis and technical support for similar products.

Performance Prediction and Reverse Design of a Glass Composites Pultrusion Process Based on RBF