Sie haben Javascript deaktiviert!
Sie haben versucht eine Funktion zu nutzen, die nur mit Javascript möglich ist. Um sämtliche Funktionalitäten unserer Internetseite zu nutzen, aktivieren Sie bitte Javascript in Ihrem Browser.

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

Members of the Chair of Dynamics and Mechatronics

Lars Muth, M.Sc.

 Lars Muth, M.Sc.

Dynamics and Mechatronics (LDM)

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

+49 5251 60-1808
+49 5251 60-1803
Office hours:

By appointment

Pohlweg 47-49
33098 Paderborn

Open list in Research Information System


Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data

L. Muth, C. Noll, W. Sextro, in: Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021, Springer, 2022

Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation.


Rule-based Diagnostics of a Production Line

O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: Proceedings of the European Conference of the PHM Society 2021, 2021, pp. 527-536

In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.

Open list in Research Information System

The University for the Information Society