Industry

AI Automotive engineering optimization 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.

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

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.

Trusted by leading automotive industry brands:

BMW
Honda
Mercedes
Jota
Kautex

“With Monolith’s machine learning method, we not only solved the challenge, we also reduced design iteration times and 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

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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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:
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. 

How Engineers Use AI to Improve Vehicle Acoustics 

Kautex-Textron Case Study

 

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

Automotive industry applications

Automotive Wind Tunnel Testing

Customers use Monolith to create a Digital Twin of the product to be tested and can manipulate the Digital Twin’s properties while in the wind tunnel​.

By importing data from the sensors, test rig, and airflow, the customer can accurately predict readings from sensors that have failed, the aerodynamic performance of the product, and the impact of air contamination on the test​.

The resulting reduction in wind tunnel time saves tens of thousands of pounds per day.​

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automotive industry Vehicle Dynamics artificial intelligence solution for self-driving vehicles

Vehicle Dynamics ​

Using Monolith’s intelligent exploration tools, the data can be sorted and easily visualized within the platform to inform engineers.

Machine Learning models can be trained on certain manoeuvers, to predict the forces on the vehicle during manoeuvers that were not performed, thereby reducing total testing time.

This allows for a test campaign to be optimized, reducing the number of tests performed to characterize the vehicle behavior.

Automotive Crash Testing

The Monolith platform allowed the customer’s engineering team to quickly import and process sensor data, then transform it and create machine learning models.

​Models created from a small initial number of tests and tests from previous vehicles enabled the engineers to predict the outcome of the regulatory tests.

This enabled them to adjust the safety aspects of the design of new vehicles to satisfy regulatory tests much faster, with fewer resources and lower costs.​

Automotive Crash Testing automotive industry self driving cars machine learning platform
Engine calibration solution automotive industry autonomous vehicle technology ai

Engine Calibration​

Our solution was to learn from previous tests and perform new virtual tests in the Monolith platform to quickly validate or invalidate options to reduce fuel consumption. In this use case, we looked at the effect of three design changes on the fuel consumption: 1) earlier combustion, 2) lower charging loss, and 3) use of an intercooler.

Combustion Chamber

“Combining simulation tools with artificial intelligence offers new design freedom to our customers,” said Jean-Claude Ercolanelli, Vice President of Simulation and Test Solutions, at Siemens Digital Industries Software. “With the ability to instantaneously explore new design options, while still satisfying target goals and constraints, AI extends the abilities of engineers to save time and unleashes innovation even without the benefit of CAD access.”

Combustion chamber optimization for automotive industry engineers autonomous cars

Key Automotive industry challenges

1

Wind tunnel tests are expensive, and almost every test is rushed toward the end of the shift. Self-learning models offer high-value insights into wind tunnel measurements, thereby improving test planning and execution.

Test engineers can draw complex data dependencies between multiple design variables, without being constrained by the limits of traditional physics-based methods.

2

Track tests are a fun day out, but preparation for track days can be a headache. Failing sensors, loss of data, noisy data, and poor weather conditions can all jeopardize a successful track test outcome.

Self-learning models can significantly enhance the value of track tests. For example, a transient track test is often sufficient to produce results that are otherwise obtained from an additional steady-state test. 

3

Crash tests are one of the most expensive destructive tests that every auto OEM must perform, as each requires building a new prototype car.

Using self-learning models has enabled our customers to find a direct correlation between different crash scenarios and parameters linked to achieving a 5-star Euro NCAP rating.

 

"If your model is in your data, Monolith will find it. Built by engineers for engineers, Monolith helps you make better models faster." 
Dr. Ted Duclos, Monolith Advisor and Former CTO at Freudenberg Sealing Technologies

 

ted
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
Aerospace and Defense
Aerospace and Defense

Ready to get started?

BAE Systems
Jota
Aptar
Mercedes
Honda
Siemens