We’ll be at the Farnborough Air Show in July, discussing how Intellegens clients are applying machine learning to accelerate their research in aerospace.
We’re partnering with Aerospace Xelerated at the event. Our CSO, Gareth Conduit, will be participating in a ‘fireside chat’ discussion panel and Bogdan Nenchev from our science team will be presenting a workshop on Design of Experiments for aerospace.
Connect with us ahead of the event to arrange a meeting.
![](https://intellegens.com/wp-content/uploads/2024/06/Farnborough-Air-Show-1.jpg)
Event details
Workshop – Design of Experiments made easy for aerospace R&D
Timing: July 22, 3-3.30pm
Location: The Aerospace Xelerated Innovation Zone (Hall 3, Area 3100).
Intellegens presenter: Bogdan Nenchev
Design of Experiments (DOE) can focus and accelerate aerospace research, for example, when designing new aerospace alloys, selecting parameters for additive manufacturing, or optimising composite materials. In conventional DOE, statistics are used to optimise coverage of design space while minimising the number of experiments. However, it takes an expert to select the right method and there will still be a high experimental burden. At Intellegens, we’d urge you to try an ML-led approach to adaptive DOE. Naturally, we’d suggest you start with our Alchemite™ solution for “Easy DOE”. But, whatever tool you consider, adaptive DOE is the way forward. Adaptive DOE takes a targeted, iterative approach which typically gets to a result with 50-80% fewer experiments.
Fireside chat with Aerospace Xelerated Founders
Timing: July 24, 10.40-11.20am
Location: The Aerospace Xelerated Innovation Zone (Hall 3, Area 3100).
Intellegens participant: Gareth Conduit
Gareth will participate in a discussion panel with other founders of innovative technology companies serving the aerospace sector.
Intellegens for aerospace
Find out more about how aerospace leaders such as NASA and Rolls-Royce have applied machine learning to accelerate their R&D.
Machine learning for alloy design and development
Machine learning for additive manufacturing
Case study – alloy and component design at NASA