Optimization of die-temperature in pultrusion of thermosetting composites for improved cure

Abstract : In this work, we will present a swram optimization based approach to optimize die-temperature and pull-speed in pultrusion of thermosetting composite. Pultrusion is a composite manufacturing technique for processing continuous composite profiles with a constant cross section. The materials which are used for pultrusion in the industry are continuous glass fibers with polyester or epoxy resins. During composite processing, the reinforcing fibers are impregnated with a liquid resin in an injection box or resin bath, fibers and resin are preheated in a mold in which the curing process takes place. High productivity and low operating costs are the main advantages of this processing method. During processing, the heat flux provided by the mold must be sufficient to promote the polymerization reaction of the thermosetting matrix (curing). Furthermore, curing of a composite should be uniform and sufficient in order to provide a good quality of the end product. The exothermic character of the curing reaction induces, inside the composite, exceed temperatures. This temperature rise can cause degradation of the final product. Also, in pultrusion process, transport phenomena are involved and mathematical models are necessary to predict the physico-chemical behavior of the process. For such studies, the region enclosed by the mold is usually considered the main part of the process in which the curing reaction occurs and heat is transfered. Thus, the optimization process is quite important for the prediction of die-heating temperature and pull-speed.
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Contributor : Hacène Ouzia <>
Submitted on : Wednesday, September 7, 2016 - 5:49:29 PM
Last modification on : Thursday, March 21, 2019 - 1:04:45 PM


  • HAL Id : hal-01360403, version 1


Rita de Cassia Costa Dias, Hacene Ouzia, Andralf Schledjewski. Optimization of die-temperature in pultrusion of thermosetting composites for improved cure. International Conference on Swarm Intelligence Based Optimization (ICSIBO 2016), Jun 2016, Mulhouse, France. ⟨hal-01360403⟩



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