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The State of AI in Engineering

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Empowering Aerospace and Defense engineers.

Reduce costly & labor-intensive testing, decrease risks to product performance & quality, and shorten product development duration with AI.

How Monolith supports Aerospace and Defense engineering professionals. 

 

Our no-code AI software is trusted by the world’s top aerospace and defense engineering teams, including Rolls Royce and BAE Systems, to develop better quality products in half the time. 

Featured in Forbes magazine and named one of the UK’s top 100 startups, Monolith founder Dr. Richard Ahlfeld received his PhD in Aerospace Engineering from Imperial College and was named in MIT Technology Review’s Top 10 Innovators under 35. 

Who we work with :

Rolls Royce
BAE Systems
Thales

“Our products create massive amounts of data throughout their entire lifecycle, bringing this data together requires a new set of skills, no blueprint exists yet because the ways of working have changed significantly.” ​

- Dave Holmes, Operations & Technology Director, BAE Systems​

Challenges facing Aerospace and Defense engineers

1
Knowledge retention is becoming harder, as engineers rarely remain at a company for the entire product lifecycle. As such, data-driven processes are the key to product knowledge and continuity. ​
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2
While products are growing in complexity, it’s imperative to shorten time-to-market and reduce costs.
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3
The aerospace industry is committed to carbon neutrality, but product testing within the aerospace industry is extremely harmful to that goal. Data-driven modeling enables the reduction of physical testing and makes it easier to achieve carbon neutrality.
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Featured Content 

 

Robust active learning for next test recommendations

 

Integrating Monolith in your verification and validation process can enhance operational efficiency and streamline testing procedures, reducing reliance on excessive physical tests. 

4 validation cases white paper battery testing
AI for simplifying validation testing
 

4 applications for AI in validation test

 

AI has a significant impact on validation testing in engineering product development. You can reduce testing by up to 73% based on battery test research from Stanford, MIT, and Toyota Research Institute. Learn more with Monolith:

On-Demand Webinar

 

Battery testing with AI: 
Build a more efficient test plan you can trust

 

In the first part of the EV webinar series, we reviewed the latest research on using AI models to significantly reduce the testing needed for EV batteries.  In this follow-up webinar, we’ll show how to implement these concepts using Monolith software.  

Aerospace and Defense use cases

Aircraft Wind Tunnel Testing

 

  • By simulating an aircraft using computational methods such as Computational Fluid Dynamics (CFD), engineers can mimic the physical domain and run virtual wind tunnel experiments for a fraction of the price. This allows aerospace engineers to understand the performance of designs in advance, thus narrowing down the right design choices for the prototype stage. 

  • Build 3D AI models that directly predict rolling road wind tunnel performance of a rim from the CAD design – faster and more accurate than CFD!​
  • By importing data from the sensors, test rig, and airflow, the customer can accurately predict the readings from sensors that have failed, the aerodynamic performance of the product, and the impact of air contamination on the test​.
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Flight dynamics_done

Flight Dynamics

 

  • Predict turbulent aircraft behavior with Monolith. ​Perform dynamics tests in 72–80% less time using Bayesian Deep Learning in a clever new way. 

  • As gusts occur at many different wind speeds, flight altitudes, and for differently loaded aircraft, engineers need to manually check 1000s of scenarios – a tremendously complicated task.​ 

  • With a deep neural network predicting changes in real-time as part of an interactive dashboard, this actually becomes quite fun to do.
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Composite Material Testing

 

  • Use existing data and handle the categorical information (type of fibers) to train machine learning models in Monolith.
  • Quickly calculate the properties of a composite material.
  • Quickly find an optimal microstructure.
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composite case study image _done
 
AI for crash testing 

 

Why crash test is an ideal use case for AI

 

BMW Group proves that product development timelines can be dramatically cut by using engineering data to eliminate repetitive, time-intensive testing.

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Talk to an expert

Sam

Head of Aerospace & Defense

A strong-willed Systems Engineer with a passion for all things Aerospace and Defense. I have experience across all areas of the lifecycle within Defense, and I am now utilizing that to drive innovation in wider industries.

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Sam Emeny Smith Monolith AI Aerospace and defense expert
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