Computational analysis of new product performance
Our client was developing an advanced dressing to be used together with their negative pressure wound therapy (NPWT) systems for improved closure and healing of surgical incisions.
The client team wanted to understand why certain prototypes performed better, and how various design parameters could optimise performance for different wound sizes and locations.
What we did
Simulation engineers at 42T performed finite element analysis (FEA) on a parameterised version of the design and delivered detailed insight into the effects of different features and alternative material properties.
We validated the models through targeted testing, and synthesised our findings into design rules that describe how different aspects affect the product’s performance.
The learnings enabled the team to create new optimised versions of the design for different applications, strengthen their IP, and ensure performance can be maintained as they progress through design-for-manufacture and industrialisation phases.
Project achievements
- Improved design of a next-generation wound care device via deep understanding of the impact of design parameters
- Client design team could use tools we developed and validated to evaluate requested changes during industrialisation and beyond
- Analysis and design improvements from 42T were used to strengthen the patent application for the technology
42T's simulation expertise
Sarah Knight, Head of Healthcare Technology at 42T, says “The expertise of our simulation engineers was invaluable in understanding the behaviour of the design.
The most impressive outcome is that our valuable insights enabled our client’s industrialisation time to improve manufacturability while simultaneously improving performance.
Since delivering this project, we have invested in even more powerful tools, so we have continued to uncover deep engineering insights for our clients.”
Find out more - contact our Director of Healthcare, Craig Townsend.
craig.townsend@42T.com | +44 (0)1480 309461 | LinkedIn: Craig
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