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Multiobjective Optimization Design of Heating System in Electric Heating Rapid Thermal Cycling Mold for Yielding High Gloss Parts

Identifieur interne : 000056 ( Main/Exploration ); précédent : 000055; suivant : 000057

Multiobjective Optimization Design of Heating System in Electric Heating Rapid Thermal Cycling Mold for Yielding High Gloss Parts

Auteurs : Cheng-Long Xiao [République populaire de Chine] ; Han-Xiong Huang [République populaire de Chine]

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Mots-clés :

Abstract

An optimization design method is developed for the electric heating system in rapid thermal cycling molding (RTCM) mold. First, a multiobjective optimization model is established, in which the distance between the mold cavity surface and the center of heating elements and the number and power density of heating elements are the design variables, the required heating time th and the highest cavity surface temperature Tmax at time th are the objective functions. Then, an optimization strategy consisting of design of experiment, finite element analysis, artificial neural network (ANN) and response surface methodology (RSM) models, and Pareto-based genetic algorithm is proposed to solve the multiobjective optimization model. Finally, the optimization strategy is applied for the design of the heating system for an automotive spoiler blow mold. The results show that the temperature distribution uniformity on the blow mold cavity surface is obviously improved and high heating efficiency is also ensured with the optimized design parameters. Moreover, the ANN model exhibits its superiority over the RSM model in terms of modeling and predictive abilities. A RTCM blow mold with the optimized electric heating system is constructed and successfully utilized to mold high gloss automotive spoiler.


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<term>Electric heating</term>
<term>Esthetic properties</term>
<term>Experimental design</term>
<term>Experimental test</term>
<term>Finite element method</term>
<term>Genetic algorithm</term>
<term>Gloss</term>
<term>Modeling</term>
<term>Molding</term>
<term>Neural network</term>
<term>Numerical simulation</term>
<term>Optimization</term>
<term>Plastics</term>
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<term>Optimisation</term>
<term>Brillant</term>
<term>Moulage</term>
<term>Plan expérience</term>
<term>Méthode élément fini</term>
<term>Réseau neuronal</term>
<term>Méthodologie surface réponse</term>
<term>Algorithme génétique</term>
<term>Modélisation</term>
<term>Cycle thermique</term>
<term>Matière plastique</term>
<term>Relation mise en oeuvre propriété</term>
<term>Chauffage électrique</term>
<term>Simulation numérique</term>
<term>Etude théorique</term>
<term>Vérification expérimentale</term>
<term>Propriété esthétique</term>
<term>Moulage cycle thermique rapide</term>
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<div type="abstract" xml:lang="en">An optimization design method is developed for the electric heating system in rapid thermal cycling molding (RTCM) mold. First, a multiobjective optimization model is established, in which the distance between the mold cavity surface and the center of heating elements and the number and power density of heating elements are the design variables, the required heating time t
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and the highest cavity surface temperature T
<sub>max</sub>
at time t
<sub>h</sub>
are the objective functions. Then, an optimization strategy consisting of design of experiment, finite element analysis, artificial neural network (ANN) and response surface methodology (RSM) models, and Pareto-based genetic algorithm is proposed to solve the multiobjective optimization model. Finally, the optimization strategy is applied for the design of the heating system for an automotive spoiler blow mold. The results show that the temperature distribution uniformity on the blow mold cavity surface is obviously improved and high heating efficiency is also ensured with the optimized design parameters. Moreover, the ANN model exhibits its superiority over the RSM model in terms of modeling and predictive abilities. A RTCM blow mold with the optimized electric heating system is constructed and successfully utilized to mold high gloss automotive spoiler.</div>
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