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Experiments with the bonding machine Show image information
Quality evaluation of bonded interconnects using a shear tester. Show image information
Reliability analysis of a friction clutch. Show image information
Lab work in teaching. Show image information
Transport of fine powder using ultrasonic vibrations Show image information

Experiments with the bonding machine

Quality evaluation of bonded interconnects using a shear tester.

Reliability analysis of a friction clutch.

Lab work in teaching.

Transport of fine powder using ultrasonic vibrations

AI in Vehicle Engineering

In the focus area “AI in Vehicle Engineering”, we combine the great potential of Artificial Intelligence (AI) with our domain knowledge to pave the way for more efficient, reliable, and safer vehicles.

AI encompasses a large pool of methods that can be used in vehicle engineering to accelerate development processes, calculate complex phenomena, and predict the properties or behavior of vehicles and their components. The focus here is on Machine Learning (ML), which is a subset of AI and can draw conclusions from experimental or simulated data that are too difficult or costly for humans to formulate.

Conversely, classical approaches based on physics provide reliable and comparatively easy-to-understand predictions and are thus still of great value for, e.g., design decisions. Therefore, in many cases, we strive for a smart combination of AI and physics-based approaches.

Contact

Lars Muth, M.Sc.

Dynamics and Mechatronics (LDM)

Team Leader "AI in Vehicle Engineering", Computationally Efficient Prediction of Tire Wear

Lars Muth
Phone:
+49 5251 60-1808
Fax:
+49 5251 60-1803
Office:
P1.3.32
Web:

Office hours:

By appointment

The University for the Information Society