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

机译:非等温条件下遗传算法的结晶动力学参数辨识

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Purpose - This 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.rnDesign/methodology/approach The 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.rnFindings - It 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.rnResearch limitations/implications Experimental 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.rnPractical implications - An 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/value - Simulation of crystallization in injection molding is very important for a later prediction of the end-use properties.
机译:目的-本文旨在证明遗传算法逆方法在等温和非等温条件下确定结晶动力学定律参数的可行性,并使用多个实验。rn设计/方法/方法结晶动力学确定的数学模型介绍了其分辨率的数值方法。提出了通过近似物理分析和逆遗传算法确定的结晶动力学参数。考虑到结晶的注塑成型模拟是使用有限元方法进行的。rnFindings-必须对两个参数(转换后的体积分数和球晶数量)进行优化,以获得正确的结果。尽管存在一些值的分散,但仍可以使用来自不同样品的结果。研究限制/意义使用了等温和非等温条件的实验数据,并获得了结晶动力学定律参数的良好结果。推导了整个结晶动力学和晶体微观结构。然而,关于用不同样品获得的球晶数量的实验数据的分散是重要的。球晶数量的变化是优化所需的,以获得正确的结果。rn实际意义-这项工作的重要结果是,遗传算法优化可以应用于无法用单个样本进行实验的遗传问题。实验数据中球晶的数量精度较低。即使仅考虑结晶动力学,也已显示出模制模拟的可行性。原创性/价值-注塑成型中的结晶模拟对于以后预测最终用途特性非常重要。

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