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Brake Squeal Prediction Using Deep Learning

Name: Merten Stender

Presentation Title: Brake Squeal Prediction Using Deep Learning (EB2020-STP-003)

Session: NVH Vehicle Applications, Wednesday 19th May 2021, 11:30 - 13:10 (Central European Summer Time - CEST)

What do you find most interesting about the topic of your presentation?

Using machine learning, we can replace much of the costly and time-consuming NVH tests in the fields by digital twin models. Using the twin models, we can run cars through “virtual NVH endurance runs”, analyze sensitivities and predict the occurrence of NVH sounds on your desktop computer, thereby increasing the development speed and cutting costs.

When discussing this topic with industry peers, what question are you most frequently asked? How do you answer it?

Most frequent question: “How much data is required?” My response: Current brake system development departments currently collect more data than necessary – the amount of data has not been an issue yet.

Who do you think will be most interested in your presentation, and who would you most like to ask questions about it?

Brake system designers that aim to shift towards virtual development processes, and make more out of the cost-intensive testing than is being done today using classical analysis.

What specific topics or technology are you hoping to see in other presentations or in the exhibition?

I’m hoping to see more contributions on data-driven techniques and applications of Machine Learning in the field of brake system design.

Register for EuroBrake 17-21 May 2021 to see over 100 technical presentations and posters, access technical content and replay sessions for three months after the event, and network with the community in the online event platform.

About Merten Stender.

Merten is a 30-year old researcher and entrepreneur. He studied Mechanical Engineering at Hamburg University of Technology (TUHH) with a special focus on dynamics and numerical simulations. In 2016 he started his PhD with Prof. Norbert Hoffmann at TUHH on chaotic friction-excited vibrations of brake systems considering AI methods. Since 2020 he is head of the Complex Dynamics & ML Group at TUHH as a PostDoc. At the same time, he founded the startup “tensorDynamic GmbH”, which is bringing machine learning and data science to industrial reality of brake system design and testing. tensorDynamic GmbH is building measurement data warehouse systems, data analytics tools and interactive dashboards to extract more insights from the data.


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