Neural-fuzzy optimization of thick composites curing process

Abstract

ABSTRACT This article addresses the optimization of curing process for thick composite laminates. The proposed methodology aims at the evaluation of the thermal cycle promoting a desired evolution of the degree of cure inside the material. At the same time, temperature overshooting as well as excessive temperature and cure degree gradient through the thickness of the material are prevented. The developed approach is based on the integrated application of artificial neural networks and a fuzzy logic controller. The neural networks promptly predict the behavior of composite material during curing process, while the fuzzy logic controller continuously and opportunely adjusts the proper variations on the imposed thermal cycle. The results highlighted the efficiency of the method in comparison with the cure profiles dictated by the material suppliers. For thick laminates, a reduction of 35% of cure time and improvements of approximately 10% of temperature overshooting was obtained compared to conventional curing cycles. The method was validated by experimental tests.