Master Thesis: Machine Learning-Based Optimization Method for Enhanced NVH Performance

The Institute for Machine Elements and Systems Engineering researches the fundamental structural and tribological behavior of machine elements and represents this in experimentally validated model descriptions. These model de-scriptions are used to analyze and design the functional, loss, and structural behavior of complete technical systems, with a focus on drive technology. The developed models are also used for the research and development of methods in Model-Based Systems Engineering (MBSE) as a central element of fu-ture industrial product development processes.
As part of this work, a simulation of a cylindrical structure is considered as a simplified representation of an electric motor housing. A ribbing layout is introduced around the cylinder to reduce radiated noise. The parameters of this ribbing layout are systematically explored within the design space, and for each design, the vibration and noise metrics are calculated using simulation. Based on the dataset obtained, machine learning is then applied to identify the optimized design for the best vibroacoustic performance.

Tasks:

• Finite Element dynamic simulation of a simplified cylindrical housing structure with integrated ribbing stiffeners
• Calculating the vibroacoustic response metrics under excitation across a predefined frequency range
• Performing simulations for various ribbing layout parameters based on Design of Experiments (DoE), and extracting the corresponding output metrics for each design point
• Train a machine learning model (e.g., NN or GPR) using the dataset generated from the simulations
• Utilizing the trained model to identify the optimal design

Requirements:

• Interest in structural dynamics and artificial intelligence
• Basics of machine learning
• Basics of vibroacoustic
• Reliable and structured working approach

We offer:

• Future-oriented field of study
• Learning practical application of artificial intelligence in engineering
• Fast processing and intensive supervision
• Independent work organization and flexible hours
• Remote work possible via home office access

We look forward to your application by email:

Esmaeil Nouri, M.Sc.
Institute for Machine Elements and Systems Engineering

Eilfschornsteinstr. 18, 52062 Aachen
esmaeil.nouri@imse.rwth-aachen.de