Modelling of Resonant Acoustic Mixing Parameters
Lead Research Organisation:
University of Birmingham
Department Name: Chemical Engineering
Abstract
Traditionally, multi-material blended components are mixed in
planetary mixers, cast to blocks/blanks, and undergo subtractive
machining to form shaped components. Composites such as
syntactics and highly filled resin based systems are blended in rotary
drums and other conventional blade based mixers over long periods
of time, decanted, moulded to blocks and again undergo subtractive
machining. These are both time consuming, wasteful and potentially
hazardous 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 potential
to directly mix materials into the final net (or near net) component
shape without further processing, removing or significantly reducing
material waste, time and hazards. RAM is being trialled with a
defence contractor and is showing excellent results to-date. However,
modelling of this mixing technique is in its infancy and has not been
addressed 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 and
thus if modelled would add significant value in optimising the mixing
process.
Modelling and/or trials would improve understanding of the
capability, both limitations and opportunities. A phase space of
mixing sweet-spots could be identified, further de-risking potential
processing operations by avoiding knife-edge scenarios. Once mixed,
the blend of powders still needs to be decanted to a mouldtool. This
presents a more controlled flowing environment but can lead to phase
separation layering and other forms of de mixing especially when considering particles of different size or density.
planetary mixers, cast to blocks/blanks, and undergo subtractive
machining to form shaped components. Composites such as
syntactics and highly filled resin based systems are blended in rotary
drums and other conventional blade based mixers over long periods
of time, decanted, moulded to blocks and again undergo subtractive
machining. These are both time consuming, wasteful and potentially
hazardous 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 potential
to directly mix materials into the final net (or near net) component
shape without further processing, removing or significantly reducing
material waste, time and hazards. RAM is being trialled with a
defence contractor and is showing excellent results to-date. However,
modelling of this mixing technique is in its infancy and has not been
addressed 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 and
thus if modelled would add significant value in optimising the mixing
process.
Modelling and/or trials would improve understanding of the
capability, both limitations and opportunities. A phase space of
mixing sweet-spots could be identified, further de-risking potential
processing operations by avoiding knife-edge scenarios. Once mixed,
the blend of powders still needs to be decanted to a mouldtool. This
presents a more controlled flowing environment but can lead to phase
separation layering and other forms of de mixing especially when considering particles of different size or density.
People |
ORCID iD |
Christopher Windows-Yule (Primary Supervisor) | |
Hazal Sezer (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S023070/1 | 01/10/2019 | 31/03/2028 | |||
2889976 | Studentship | EP/S023070/1 | 01/10/2023 | 30/09/2027 | Hazal Sezer |