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Modelling of Resonant Acoustic Mixing Parameters

Modelling of Resonant Acoustic Mixing Parameters
共振声混合参数的建模
批准号:
2889976
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
翻译
传统上,多材料混合的部件在行星搅拌机中混合,浇注成块/坯料,然后经过减速机械加工形成成形部件。合成法和高填充树脂体系等复合材料长期混合在转鼓和其他传统的基于叶片的搅拌机中,倒出,模压成块,然后再次进行亚加工。在处理高能敏感材料时,这些都是耗时、浪费和潜在的危险。共振声学混合(RAM)是一种新型的粉末/粉末、粉末/液体和液体/液体混合技术,它有可能直接将材料混合到最终的净(或近净)成分形状中,而不需要进一步的加工,从而消除或显著减少材料浪费、时间和危险。Ram正在与一家国防承包商进行试验,到目前为止表现出了很好的结果。然而,这种混合技术的建模还处于初级阶段,还没有得到解决,因此,不能说是优化的。许多混合(强度、时间、压力、温度)、材料(颗粒大小、形状、预混合、添加顺序)和工具(形状、组成、混合头空间)参数会影响RAM的效率,因此,如果建模将在优化混合过程中增加重要价值。建模和/或试验将提高对能力的理解,既有局限性,也有机会。可以确定混合甜蜜点的相空间,通过避免刀锋场景来进一步降低潜在加工操作的风险。一旦混合,混合的粉末仍然需要倒到模具中。这提供了一个更可控的流动环境,但可能会导致相分离、分层和其他形式的混合,特别是在考虑不同大小或密度的颗粒时。
英文摘要
Traditionally, multi-material blended components are mixed inplanetary mixers, cast to blocks/blanks, and undergo subtractivemachining to form shaped components. Composites such assyntactics and highly filled resin based systems are blended in rotarydrums and other conventional blade based mixers over long periodsof time, decanted, moulded to blocks and again undergo subtractivemachining. These are both time consuming, wasteful and potentiallyhazardous when working with energetically-sensitive materials.Resonant Acoustic Mixing (RAM) is a novel powder/powder,powder/fluid and fluid/fluid mixing technology that has the potentialto directly mix materials into the final net (or near net) componentshape without further processing, removing or significantly reducingmaterial waste, time and hazards. RAM is being trialled with adefence contractor and is showing excellent results to-date. However,modelling of this mixing technique is in its infancy and has not beenaddressed and thus, cannot be said to be optimised. Numerous mixing(intensity, time, pressure temperature), material (particle size, shape,pre-blending, order of addition) and tooling (shape, composition,mixing head space) parameters impact the efficiency of RAM andthus if modelled would add significant value in optimising the mixingprocess.Modelling and/or trials would improve understanding of thecapability, both limitations and opportunities. A phase space ofmixing sweet-spots could be identified, further de-risking potentialprocessing operations by avoiding knife-edge scenarios. Once mixed,the blend of powders still needs to be decanted to a mouldtool. Thispresents a more controlled flowing environment but can lead to phaseseparation layering and other forms of de mixing especially when considering particles of different size or density.
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