IMT Generative Design Blower Coupler

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6 Minutes

Published

Author

Christoph Untersander, Kaspar Schlegel

Generative design: Blower coupler optimisation for lung simulator

Insight in Brief

This article deals with the following topics:

  • Development of a blower coupler: IMT optimised a blower coupler for a lung simulator, with a focus on minimal pressure loss, material consumption and flow-optimised geometries.

  • Use of generative design: Optimised geometries were developed, tested and precisely measured with the help of generative design and additive manufacturing.

  • Successful pressure reduction: The final version reduced the differential pressure by 13% compared to the first version, while at the same time using less material.

  • Engineering expertise and additive manufacturing: Generative design proved to be a valuable addition to classic engineering, but can only fully utilise its strengths in combination with additive manufacturing.

Background

The development of complex mechanical components requires innovative approaches to maximise efficiency and performance. IMT used generative design to optimise a blower coupler (connection piece) for a lung simulator.

The aim was to minimise the differential pressure and internal volume while keeping material consumption as low as possible. All design versions were additively manufactured and measured in order to validate the best possible solution. The final optimised version was able to reduce the differential pressure by 13% compared to the first version - a clear success for the hybrid development approach of engineering knowledge and generative design. It became clear that the full potential of generative design can only be exploited if the parts are additively manufactured, as this is the only way to implement flow-optimised structures.

Introduction

The demands placed on modern medical technology devices are constantly increasing - both in terms of performance and resource conservation. IMT was faced with the challenge of developing a blower coupler for a lung simulator for its sister company IMT Analytics. The aim was to minimise pressure loss and internal volume while optimising the use of materials. To solve this task, IMT combined classic engineering work with generative design and simulation tools such as Fusion 360 and Ansys FLUENT.

What is generative design?

Generative design is a computer-aided design approach that uses algorithms and artificial intelligence to develop optimised component geometries. Instead of an engineer specifying a fixed shape, they simply define the framework conditions such as fluidic requirements or material restrictions. The software then generates a large number of possible solutions, which are often inspired by natural structures and characterised by organic, flowing shapes. The best possible geometry is then determined through iterative simulations and optimisations. This approach is particularly effective in combination with additive manufacturing, as conventional manufacturing processes often come up against geometric limits, whereas 3D printing can realise almost any shape.

Optimisation through generative design

Figure 1 - Blower Coupler iterations

1. Manual design and simulation

An experienced engineer first modelled a conventional geometry for the blower coupler. This was simulated and tested with Ansys FLUENT. The first version was then additively manufactured and precisely measured in order to obtain real data to validate the simulation.

2. Generative design with Fusion 360

The same task was then passed to a generative design algorithm in Fusion 360. The generated mould was also simulated, then additively manufactured and measured. The test results showed that this variant performed worse than the model developed by hand due to unsuitable input parameters.

Figure 2 - Generatively created variants
Figure 3 - Solution space and fluid paths

However, a better result was subsequently achieved by correcting the parameters. Lattice structures were also used to further optimise the use of materials. These lattice structures enable a considerable reduction in weight and material while maintaining structural integrity. Especially in the area of flow-optimised components, these designs proved to be extremely advantageous, as they favoured an even distribution of the air flow.

Figure 4 - Reduction of over 25% of the material through the use of lattice structure

3. Fine optimisation with Ansys FLUENT and mesh morphing

The final iteration combined the engineering-driven shape with additional optimisations using mesh morphing in Ansys FLUENT.

Figure 5 - Velocity vectors in the final design

This version was also additively manufactured and measured. The result: a 13% lower differential pressure with the same volume compared to the first version - while simultaneously reducing the amount of material used. This reduction was achieved by changing the wall thicknesses and could even be improved in certain areas with Lattice Structure. It should be emphasised that this optimised shape could not have been achieved using conventional production methods. The complex, flow-optimised structures created by generative design could only be implemented using additive manufacturing.

Figure 6 - Final design

Conclusion

Working with generative design has shown that it is a powerful tool for shape optimisation. However, it cannot replace the experience and knowledge of an expert engineer. By combining classical engineering, simulation and generative design, the optimal solution was found. Additive manufacturing and precise measurement of each iteration played a crucial role in validating the results. The 13% reduction in differential pressure in the final version is particularly noteworthy.

It also became clear that the strengths of generative design can only be fully exploited through additive manufacturing, as this is the only way to realise flow-optimised geometries without restrictions. IMT will continue to utilise generative design in a targeted manner in order to develop innovative solutions for complex challenges faced by our customers.

 

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