Identification of crystallization kinetics parameters by genetic algorithm in non‐isothermal conditions

Abstract

PurposeThis paper sets out to show the feasibility of the genetic algorithm inverse method for the determination of the parameters of crystallization kinetics laws in isothermal and non‐isothermal conditions, using multiple experiments.Design/methodology/approachThe mathematical model for crystallization kinetics determination and the numerical methods of its resolution are introduced. Crystallization kinetic parameters determined by approximate physical analysis and the inverse genetic algorithm method are presented. Injection molding simulations taking into account crystallization are performed using the finite element method.FindingsIt is necessary to perform the optimization on two parameters, transformed volume fraction and number of spherulites to obtain correct results. It is possible to use results from different samples, in spite of the dispersion of some values.Research limitations/implicationsExperimental data for isothermal and non‐isothermal conditions were used and obtained good results for the parameters of crystallization kinetics laws from which the evolutions of overall crystallization kinetics and crystalline microstructure were deduced. Nevertheless, the dispersion of the experimental data concerning the number of spherulites obtained with different samples is important. The evolution of the number of spherulites is required for the optimization to get correct results.Practical implicationsAn important result of this work is that the genetic algorithm optimization can be applied to this problem where the experiments cannot be performed with a single sample and the experimental data for the number of spherulites have low precision. Even if only the crystallization kinetics was considered, the feasibility in molding simulation has been shown.Originality/valueSimulation of crystallization in injection molding is very important for a later prediction of the end‐use properties.