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Chapters written by professional and academic experts in the field cover: analytical modeling and analysis, CEA modeling and numerical methods, techniques for dynamometer and road test evaluation, critical parameters that contribute to brake squeal, robust design processes to reduce/prevent brake squeal via up-front design, and more.

Experimental verification is an important approach for understanding the mechanisms of disc brake squeal. One mechanism of disc brake squeal, i.e., coupling of in-plane and out-of-plane vibration modes of disc brake rotor, was found by experiments. Despite the vast amount of experimental data available, little effort has been dedicated to exploring what the time series information can reveal in relevance to squeal. In this paper, a new signal processing tool employing the Empirical Mode Decomposition Method (EMD) and its application to the identification of the characteristics of disc brake squeal will be discussed. The EMD was originally developed for ocean wave mechanics [1] and is particularly useful for the type of non-stationary data found in disc brake squeal. EMD is a time series analysis method that extracts a set of basis functions describing the fundamental characteristics of the response of a system.

Dynamic data, forces, moments and displacements are widely used parameters in a simulation environment for design and testing. These results may be obtained from field tests, laboratory measurements, and numerical simulations. The correctness of the simulation results depends strongly on the models and numerical solution techniques. This paper presents a preliminary examination of the differences between results obtained from the computer code DADS (Dynamic Analysis and Design System) [1] and the field data for the response of a military tank. The differences are analyzed by standard statistical methods in the frequency domain. The statistical tests show that DADS results differ from the measured field data and that the errors are not white noise. Moreover, the principal frequencies of the differences are identified.