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2023 | OriginalPaper | Buchkapitel

Artificial Intelligence for Friction Brakes: Applications and Potentials

verfasst von : Merten Stender

Erschienen in: XL. Internationales μ-Symposium 2023 Bremsen-Fachtagung

Verlag: Springer Berlin Heidelberg

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Abstract

While new generative methods (e.g., ChatGPT) of artificial intelligence (AI) have been accessible and well-known to the general public since the beginning of 2023, data-based or data-centric engineering is still largely in its infancy. This overview article sheds light on several AI approaches for application during the development and operation of (automotive) friction brakes. The increasing regulatory requirements on particulate emissions, ongoing electrification, and fundamentally new vehicle and operational concepts pose new challenges to the development of friction brakes. At this juncture, data-based methods, novel decision-making processes, and the overall utilization of Artificial Intelligence hold significant potential for the future. This contribution focuses on some promising applications of AI methods in the context of brake development, discusses data management requirements, and provides an outlook on the importance of AI methods in the context of trends in the automotive industry. Since successful (and especially publicly accessible) use scenarios are rare, this overview article does not claim to be exhaustive regarding the current use of AI methods in the development of braking systems.

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Literatur
1.
Zurück zum Zitat Hornik, K. et al.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989) CrossRefMATH Hornik, K. et al.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989) CrossRefMATH
2.
Zurück zum Zitat Stender, M. et al.: Deep Learning for Brake Squeal: Brake Noise Detection, Characterization and Prediction. Mechanical Systems and Signal Processing 149 (2021) Stender, M. et al.: Deep Learning for Brake Squeal: Brake Noise Detection, Characterization and Prediction. Mechanical Systems and Signal Processing 149 (2021)
3.
Zurück zum Zitat von Wagner, U. et al.: Minimal Models for Disk Brake Squeal. Journal of Sound and Vibration 302, 527–539 (2007) CrossRef von Wagner, U. et al.: Minimal Models for Disk Brake Squeal. Journal of Sound and Vibration 302, 527–539 (2007) CrossRef
4.
Zurück zum Zitat Massi, F. et al.: Brake Squeal: Linear and Nonlinear Numerical Approaches. Mechanical Systems and Signal Processing 21, 2374–2393 (2007) CrossRef Massi, F. et al.: Brake Squeal: Linear and Nonlinear Numerical Approaches. Mechanical Systems and Signal Processing 21, 2374–2393 (2007) CrossRef
5.
Zurück zum Zitat Sinou, J.: Transient non-linear dynamic analysis of automotive disc brake squeal – On the need to consider both stability and non-linear analysis. Mechanics Research Communications 37, 96–105 (2010) CrossRefMATH Sinou, J.: Transient non-linear dynamic analysis of automotive disc brake squeal – On the need to consider both stability and non-linear analysis. Mechanics Research Communications 37, 96–105 (2010) CrossRefMATH
6.
Zurück zum Zitat Geier, C. et al.: Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations. Nonlinear Dynamics (2023) Geier, C. et al.: Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations. Nonlinear Dynamics (2023)
7.
Zurück zum Zitat Vater, K.: Towards neural network-based numerical friction models. Proceedings in Applied Mathematics and Mechanics 22 (2023) Vater, K.: Towards neural network-based numerical friction models. Proceedings in Applied Mathematics and Mechanics 22 (2023)
8.
Zurück zum Zitat Steffan, J. et al.: Prediction of Brake Pad Wear Using Various Machine Learning Algorithms. Recent Trends in Design, Materials and Manufacturing, 529–543 (2022) Steffan, J. et al.: Prediction of Brake Pad Wear Using Various Machine Learning Algorithms. Recent Trends in Design, Materials and Manufacturing, 529–543 (2022)
9.
Zurück zum Zitat Alamelu Manghai, T. et al.: Vibration based real time brake health monitoring system – A machine learning approach. IOP Conference Series: Materials Science and Engineering 624 (2019) Alamelu Manghai, T. et al.: Vibration based real time brake health monitoring system – A machine learning approach. IOP Conference Series: Materials Science and Engineering 624 (2019)
Metadaten
Titel
Artificial Intelligence for Friction Brakes: Applications and Potentials
verfasst von
Merten Stender
Copyright-Jahr
2023
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-68167-1_12

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