Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites in order to improve the mechanical properties of the final product. The cycle design i.e. the definition and optimization of the temperature-time curve is a key issue for a competitive production. In this paper a very fast and effective procedure, based on the coupling of a finite element thermochemical model of the process and an artificial neural network, is proposed to predict the evolution of temperature in predefined control points inside the processing material. The model has been tested against the imposed thermal cycle used as an input. The procedure is tested simulating the curing process of a three-dimensional double-curved shape. Obtained outcomes highlighted the remarkable capabilities of the implemented procedure in terms of reliability of temperature predictions and of drastic reduction of the computational time with respect to classic computational models.

Modelling of thermoset matrix composite curing process

CARLONE, PIERPAOLO;PALAZZO, Gaetano Salvatore
2014

Abstract

Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites in order to improve the mechanical properties of the final product. The cycle design i.e. the definition and optimization of the temperature-time curve is a key issue for a competitive production. In this paper a very fast and effective procedure, based on the coupling of a finite element thermochemical model of the process and an artificial neural network, is proposed to predict the evolution of temperature in predefined control points inside the processing material. The model has been tested against the imposed thermal cycle used as an input. The procedure is tested simulating the curing process of a three-dimensional double-curved shape. Obtained outcomes highlighted the remarkable capabilities of the implemented procedure in terms of reliability of temperature predictions and of drastic reduction of the computational time with respect to classic computational models.
9783038351061
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4386853
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