Feedstock properties and injection molding simulations of bimodal mixtures of nanoscale and microscale aluminum nitride
Feedstock properties and injection molding simulations of bimodal mixtures of nanoscale and microscale aluminum nitride
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DOI：
10.1016/j.ceramint.2013.02.023
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发表时间：
2013-08
影响因子：
5.2
通讯作者：
K. Kate;R. Enneti;V. Onbattuvelli;S. Atre
中科院分区：
文献类型：
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作者：
K. Kate;R. Enneti;V. Onbattuvelli;S. Atre
粉末注射成型（PIM）是有用的制造小，复杂的金属和陶瓷部件在高产量。混合纳米粒子(n)和微粒子（μ）在我们的研究小组之前已经确定了一种有前途的方法来实现高烧结密度和低收缩率的注射成型AlN。烧结研究表明，双峰μ-n AlN样品在~ 1500°C时形成液相，该温度比文献中通常报道的值至少低100°C。双峰μ-n AlN混合物的烧结部分烧结密度相当，但收缩率（~ 14%）低于相应的单峰μ-AlN（~ 20%）。这些烧结特性的好处是粉末-聚合物混合物中添加纳米颗粒后填充密度显著增加的直接结果。然而，目前很少有研究关注纳米颗粒的加入对双峰原料流变学和热性能的影响。本研究将实验测量原料特性与模型相结合，用于估算粉末含量范围内的特性。这些特性随后被用于充型模拟，以了解粉末含量对PIM工艺参数和缺陷演变的影响。这些协议和发现可用于改进PIM设计实践中的材料选择、组件几何属性和优化的工艺参数。
Powder injection molding (PIM) is useful to manufacture small, complex metal and ceramic components in high production volumes. Mixing nanoparticles (n) with microparticles (μ) has been previously identified in our research group as a promising approach to achieve high sintered density and low shrinkage in injection molded AlN. Sintering studies showed a liquid phase formation at ∼1500°C in bimodal μ–n AlN samples, a temperature that is atleast 100°C lower than typically reported values in the literature. Sintered parts of bimodal μ–n AlN mixtures exhibited comparable sintered density but lower shrinkage (∼14%) than corresponding monomodal μ-AlN (∼20%). These benefits in sintered attributes are a direct consequence of a significant increase in the packing density in powder–polymer mixtures with the addition of nanoparticles. However, there are few studies focused on understanding the effects of nanoparticle addition on the rheological and thermal properties of the bimodal feedstock. The present study combines experimental measurement feedstock properties with models for estimating properties over a range of powder content. The properties were subsequently used in mold-filling simulations to understand the effects of powder content on process parameters and defect evolution in PIM. These protocols and findings can be used to improve PIM design practices in material selection, component geometry attributes, and optimized process parameters.