Simulation of the development of the microstructure of injection-moulded semi-crystalline thermoplastics by means of a multi-scale approach under consideration of shear-induced crystal forms (alpha and beta)
在考虑剪切诱导晶型(α 和 β)的情况下,通过多尺度方法模拟注塑半结晶热塑性塑料的微观结构的发展
基本信息
- 批准号:408012354
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2019
- 资助国家:德国
- 起止时间:2018-12-31 至 2021-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Semicrystalline thermoplastics have partially spherical spherulitic microstructure. Despite the geometrical similarity of all spherulites, there are three distinct forms (α-, β- and γ-form) that arise from unique unit cells. This results in different local mechanical, thermal and optical component properties. The α-form represents the predominant crystal modification. In general, the lamellae are formed in the form of folded chains in the crystallization of semi-crystalline homopolymers in a quiescent melt. Isotactic polypropylene (iPP) in the α-form shows the unique phenomenon that lamellae are arranged in a so-called "cross-hatched" structure. The β-form has a lower degree of order compared to the α-form, which results inter alia in a higher degree of crystallization and a lower melting point. The formation of the crystalline regions of the β-form can be promoted by the addition of nucleating agents, by the choice of thermal conditions as well as by shear stress. The formation of crystallites of the γ-form occurs relatively rarely in industrially produced components and only in small proportions. It should be noted that the developed model at IKV for describing the crystallization process can not distinguish between different crystal forms. Which means all spherulites are considered the same and in the α-form, which does not correspond to reality, since the influence of β-spherulites are neglected. In the scientific literature, no work or approach is known which simulates the crystallization process in an injection-moulded component made of semicrystalline thermoplastics taking into account the different crystal forms.The aim of this project is the simulation of microstructure formation taking into account the shear-induced crystallization and consequently two dominating crystal forms (α and β) to calculate realistic microstructures and thus to precisely predict the component properties. To this end, the developed model at the IKV will be adapted so that unique nucleation and growth models for each crystal form will be derived and the calibrated using experimental experiments. These models are then implemented in the software SphäroSim, developed at IKV. The boundary conditions for the microstructure simulation come from filling simulation results based on a self-developed approach that describes the boundary conditions with a high resolution. The simulated microstructure will be then compared with the components, which will be produced under the same injection moulding process, and verified.
半结晶热塑性塑料具有部分球形的球晶显微结构。尽管所有球晶的几何形状相似,但有三种不同的形式(α-,β-和γ-形式)来自独特的晶胞。这导致不同的局部机械、热和光学部件特性。α-型代表主要的晶体变型。通常,层状结构在静止熔体中的半结晶均聚物的结晶中以折叠链的形式形成。α-型等规聚丙烯(iPP)具有独特的层状结构,即层状结构中的“交叉影线”结构。与α-形式相比,β-形式具有较低的有序度,这阿利亚导致较高的结晶度和较低的熔点。通过加入成核剂、通过选择热条件以及通过剪切应力,可以促进β-形式的结晶区域的形成。在工业生产的部件中,γ-型微晶的形成相对较少,并且仅占很小的比例。应该注意的是,在IKV开发的用于描述结晶过程的模型不能区分不同的晶体形式。这意味着所有的球晶都被认为是相同的,并且是α-形式,这与现实不符,因为忽略了β-球晶的影响。在科学文献中,没有已知的工作或方法来模拟由半结晶热塑性塑料制成的注塑部件中的结晶过程,同时考虑不同的晶体形式。本项目的目的是模拟微观结构的形成,同时考虑剪切诱导结晶和两种主要的晶体形式(α和β)。计算真实的微观结构,从而精确预测部件性能。为此,将调整IKV开发的模型,以便导出每种晶体形式的独特成核和生长模型,并使用实验实验进行校准。这些模型然后在IKV开发的软件SphäroSim中实现。微观组织模拟的边界条件来自基于自主开发的方法的填充模拟结果,该方法以高分辨率描述边界条件。然后将模拟的微观结构与在相同注塑工艺下生产的部件进行比较,并进行验证。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Professor Dr.-Ing. Christian Hopmann其他文献
Professor Dr.-Ing. Christian Hopmann的其他文献
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{{ truncateString('Professor Dr.-Ing. Christian Hopmann', 18)}}的其他基金
Interactions in laser joining of metals to polymers
金属与聚合物激光连接中的相互作用
- 批准号:
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- 资助金额:
-- - 项目类别:
Research Grants
Development of a databased model for the prediction of effective mechanical and thermal properties of injection-moulded semi-crystalline thermoplastics by means of an artificial neural network (KNN) taking into account the microstructure
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