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Models to Predict the Viscosity of Metal Injection Molding Feedstock Materials as Function of Their Formulation

机译:预测金属注射成型原料材料粘度的模型

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The viscosity of feedstock materials is directly related to its processability during injection molding; therefore, being able to predict the viscosity of feedstock materials based on the individual properties of their components can greatly facilitate the formulation of these materials to tailor properties to improve their processability. Many empirical and semi-empirical models are available in the literature that can be used to predict the viscosity of polymeric blends and concentrated suspensions as a function of their formulation; these models can partly be used also for metal injection molding binders and feedstock materials. Among all available models, we made a narrow selection and used only simple models that do not require knowledge of molecular weight or density and have parameters with physical background. In this paper, we investigated the applicability of several of these models for two types of feedstock materials each one with different binder composition and powder loading. For each material, an optimal model was found, but each model was different; therefore, there is not a universal model that fits both materials investigated, which puts under question the underlying physical meaning of these models.
机译:原料的粘度直接与其在注塑过程中的可加工性有关。因此,能够基于其组分的各个性质来预测原料的粘度可以极大地促进这些材料的配制以调整性质以改善其可加工性。文献中提供了许多经验和半经验模型,这些模型可用来预测聚合物共混物和浓缩悬浮液的粘度随其配方的变化。这些模型还可以部分用于金属注射成型的粘合剂和原料。在所有可用的模型中,我们进行了狭窄的选择,仅使用了简单的模型,这些模型不需要了解分子量或密度,并且具有具有物理背景的参数。在本文中,我们研究了这些模型中的几种对两种类型的原料的适用性,每种原料具有不同的粘合剂组成和粉末负载量。对于每种材料,都找到了最佳模型,但是每种模型都不相同。因此,没有一个适用于所研究的两种材料的通用模型,这使这些模型的潜在物理意义受到质疑。

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