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AI automotive engineering optimisation with Monolith

 

Empower your automotive engineers with artificial intelligence for product development. Spend less time running expensive, repetitive tests and more time learning from test data.

BMW
BAE Systems
Mercedes
Honda
Siemens
Honeywell
Kautex-Textron
Michelin
Aptar
Jota

Monolith software empowers engineering domain experts across all facets of the automotive industry with AI and machine learning.

Reduce cost
Reduce expensive & labour-intensive testing
Data Warning
Decrease risks to product performance & quality
Shorten Product Development
Shorten product development duration significantly
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.  
battery testing plan optimization-1

“With Monolith’s machine learning method, we not only solved the challenge, we also reduced design iteration times, prototyping and testing costs. We are thrilled with the results, and we are confident we have found a way to improve future design solutions.” ​

-Dr. Bernhardt Lüddecke, Director Validation Global at Kautex

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

 

Four 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 70% based on battery test research from Stanford, MIT, and Toyota Research Institute. Learn more with Monolith.
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. 

Kautex-Textron case study

How engineers use AI to improve vehicle acoustics 

 

Learn how test engineers at Kautex Textron use self-learning models to more accurately predict intractable fuel sloshing noise faster.

webasto-2

There are many potential use cases for AI to speed up the battery test and validation process. After an extensive evaluation, we found Monolith to be an excellent option for scaling AI across our R&D.

-Markus Meiler, VP Research & Development, Webasto

Resources

Discover more AI resources for automotive

 

Automotive engineering customers have reported up to a 70% reduction in track testing time, plus a 45% reduction in overall associated costs, while increasing the ROI of costly wind tunnel testing. How can you apply AI to automotive engineering workflows? Get in touch with our team today.

Other industries

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Industrial
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Aerospace and Defense
Aerospace and Defence
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