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Modeling the anisotropic temperature-dependent viscoplastic deformation behavior of short fiber reinforced thermoplastics

机译:模拟短纤维增强热塑性塑料的各向异性温度依赖性粘塑变形行为

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摘要

Short fiber reinforced thermoplastics (SFRT) are widely used for automotive components. One of the significant challenges in designing industrial SFRT components is an efficient prediction of their mechanical response under mechanical and thermal loads. In this work, an anisotropic temperature-dependent elasto-viscoplastic model is implemented using the available macroscopic material models in commercial FE solver to describe polybutylene terephthalate with 30 wt.-% short glass fibers. The elastic behavior is described by the orthotropic linear elastic model generated through the mean-field homogenization method and the anisotropy in plastic region by Hill yield criterion dependent on fiber orientation. The rate-dependent plasticity is described by the unified viscoplasticity framework of Chaboche. To describe the continuous temperature dependency, model parameters are systematically determined as a function of temperature in the typical automotive temperature range, including regions below and above glass transition temperature. Further, an optimization method based on genetic algorithm is adopted for parameter optimization. The optimized model accurately describes the anisotropic material behavior observed in tensile and stress relaxation tests in a wide range of temperature, specimen orientation, and strain rate. The model's prediction capability is validated by simulating tensile tests at three intermediate temperatures, which are not included during the calibration process.
机译:短纤维增强热塑性塑料(SFRT)广泛用于汽车部件。设计工业SFRT组件的重要挑战之一是在机械和热负荷下有效地预测其机械响应。在这项工作中,使用商业FE求解器中的可用宏观材料模型来实现各向异性温度依赖性的弹性粘塑料模型,以描述与30重量%的玻璃纤维的聚对苯二甲酸丁二醇酯。通过平均场均质化方法和塑料区的各向异性通过山屈服标准所产生的正极线性弹性模型来描述弹性行为,取决于纤维取向。统计依赖性可塑性由Chaboche的统一粘质框架描述。为了描述连续温度依赖性,模型参数被系统地确定为典型的汽车温度范围内的温度的函数,包括低于玻璃化转变温度的区域和高于玻璃化转变温度。此外,采用了基于遗传算法的优化方法进行参数优化。优化的模型准确地描述了在宽范围,样本取向和应变率的拉伸和应力松弛试验中观察到的各向异性材料行为。通过在校准过程中不包括的三个中间温度下模拟拉伸试验来验证模型的预测能力。

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