A Contribution to the Understanding of Friction and Squeal of Automotive Brakes: Wear Particle Generation and Deformation of Sliding Surface Region Influencing the Friction Coefficients of Copper-Free Disc Pads

Rhee, Seong Kwan; Sriwiboon, Meechai; Kaewlob, Kritsana; Tiempan, Nipon

doi:10.4271/2021-01-1288

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Although the MCM approach can synthesise transient cycles in more than one dimension [25]- [27], there are relatively few examples of synthesis of 2D speed-torque. The most relevant work is found in [24] where the authors propose an approach to develop a 2D dynamic cycle for an off-road Wheel-Loader (WL) application using hybrid Markov chain and a GA.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling

Yang,

Mason,

Winward

et al. 2024

IEEE Access

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Transient test cycles are an essential part of the development of both on and off-highway vehicles and machines. These cycles are used to test and validate the dynamic operation and performance of systems, and they also form part of regulations such as Real-world Driving Emissions (RDE). A common approach to generating a transient cycle is to replicate recorded real-world transient operation. However, the dynamics of transient operation can vary significantly based on factors such as the operating environment, the use case, the user, and the type of vehicle or machine. The feasible duration of a transient test cycle is also often limited by development time and cost. Therefore, there is a need for a method to synthesize transient test cycles from real-world data in order to replicate a wide range of feasible dynamic operation in the shortest possible time. This paper proposes an innovative transient Cycle Decomposition and Reduction (CDR) method that generates a shortened representative driving cycle in speed-torque two-dimensional space. The steps include identifying micro-trajectories using critical points (e.g. minimum torque), identifying features of micro-trajectories, grouping similar micro-trajectories using k-means clustering and Gaussian process models, and using Markov chain models to efficiently splice together the micro-transients to form a new reduced cycle. The method is flexible and can be used to generate reduced transient cycles for both on-road and off-road applications. The proposed CDR method is demonstrated by using it to perturb a system and collect data for the identification of nonlinear dynamic system models. A 30-minute transient cycle is reduced to a 200-second representative cycle, which is used to train a Neuro-Fuzzy NOx emission model. The results show that this model can accurately predict NOx emissions behaviour over the original transient cycle. The proposed CDR method is easy to expand to higher dimensions and can be applied in a wide range of applications. Additionally, the information extracted during the process has useful physical meaning and can be further utilised, for example in driving pattern recognition, vehicle diagnosis, and autonomous vehicles. INDEX TERMS Clustering, Cycle decomposition, Markov chain model, Nonlinear dynamic modelling.

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Section: Introductionmentioning

confidence: 99%

Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling

Yang,

Mason,

Winward

et al. 2024

IEEE Access

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Effect of Moisture Adsorption on Low-Speed and Moderate-Speed Braking: Effect on In-Stop Friction Coefficient and Low Frequency Noise

Sriwiboon,

Koetniyom,

Rhee

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Copper-free NAO disc pads of passenger cars were investigated for a combination of prior braking conditions and moisture adsorption influencing in-stop friction and noise during low-speed stops, and in-stop-friction during moderate-speed stops. Prior braking conditions and moisture adsorption strongly influence subsequent in-stop friction behavior and noise at room temperature. The low-speed in-stop friction behavior looks totally different from that of moderate-speed stops. The low-speed in-stop friction increasingly oscillates with increasing moisture adsorption and goes down towards the end of a stop, which is accompanied by increasing low-frequency noise.</div><div class="htmlview paragraph">The moisture content needs to be quantified/specified to obtain repeatable/reproducible brake test results as the moisture is an unintended and uncontrolled ingredient of a friction material. As the disc surface roughness increases due to prior braking conditions, the friction coefficient of low-speed stops is found to decrease. The changing friction coefficient is explained by the nature of the friction film and its interactions with moisture.</div></div>

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A Contribution to the Understanding of Friction and Squeal of Automotive Brakes: Wear Particle Generation and Deformation of Sliding Surface Region Influencing the Friction Coefficients of Copper-Free Disc Pads

Cited by 2 publications

References 41 publications

Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling

Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling

Effect of Moisture Adsorption on Low-Speed and Moderate-Speed Braking: Effect on In-Stop Friction Coefficient and Low Frequency Noise

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