Driving cycles for studying brake wear particle emissions on an inertial brake dynamometer

Vasiljević, Saša; Lukić, Jovanka; Miloradović, Danijela; Glišović, Jasna

doi:10.46793/tribomat.2023.006

Cited by 2 publications

(2 citation statements)

References 22 publications

(25 reference statements)

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“…An application of Taguchi analysis is shown in the paper [75], where the influence of parameters is also analyzed. As explained in [76], by applying the Taguchi method, it is possible to determine the influence of certain factors on some output parameters and to optimize the way of influence. This method can be applied to analyze the impact of making the output parameter have a higher or lower value.…”

Section: Applied Statistical Analyses and Datamentioning

confidence: 99%

Analysis of Parameters Influencing the Formation of Particles during the Braking Process: Experimental Approach

Vasiljević,

Glišović,

Lukić

et al. 2023

Atmosphere

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Knowing and understanding the parameters influencing the concentration of particles created by brake wear, as one of the main contributors to non-exhaust emissions from vehicles, is important for the further development of systems on vehicles to reduce the concentration of particles, and also for further research in the field of developing new friction pairs. In this research, a brake inertial dynamometer was used to measure brake particles, and four different brake pads were examined. Based on a previous review of the applied tests and driving cycles, the braking parameters were determined, i.e., the initial simulated speed of the vehicle, the load of one-quarter of the vehicle, and the brake pressure. The ambient temperature, air humidity, coefficient of friction between friction pairs, deceleration, and braking time can have an influence depending on the brake pad. Further, during the measurement, the temperatures of the brake pads were also measured, where the initial temperature of the brake pads was always the same. In order to process the data, several methods were used, including the presentation of the obtained results in a time domain, the application of the Taguchi design of the experiment with the analysis of the parameters, and a correlation analysis using the Pearson and Spearman correlation coefficients. In this research, the authors concluded that the influences of the parameters primarily depend on the applied brake pads. The vehicle speed turned out to have a large influence in all cases, as did the load, i.e., the influence of the vehicle weight (indirectly through the kinetic energy of the vehicle). In this case, the pressure showed less influence on the particle concentration. An important braking parameter that has a significant impact on the concentrations of PM2.5 and PM10 particles is the final temperature of the brake pads.

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Section: Applied Statistical Analyses and Datamentioning

confidence: 99%

Analysis of Parameters Influencing the Formation of Particles during the Braking Process: Experimental Approach

Vasiljević,

Glišović,

Lukić

et al. 2023

Atmosphere

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“…The friction coefficient (FC), wear rate (WR) and weight loss (WL) are the criteria to measure their performance (Bijwe, 1997;Mann et al, 2021). The wear generated due to braking may produce particles, which constitute the primary components of particulate matter in the air (Vasiljevic et al, 2022(Vasiljevic et al, , 2023. During the braking process, the friction performance is subject to a comprehensive influence from various factors, including but not limited to, production method, surface roughness and contact load value.…”

Section: Introductionmentioning

confidence: 99%

Prediction and comparative analysis of friction material properties using a GA-SVM optimization model

Zhang,

Wang,

Wang

2024

ILT

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Purpose With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties. Design/methodology/approach Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance. Findings The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model. Originality/value The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

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Driving cycles for studying brake wear particle emissions on an inertial brake dynamometer

Cited by 2 publications

References 22 publications

Analysis of Parameters Influencing the Formation of Particles during the Braking Process: Experimental Approach

Analysis of Parameters Influencing the Formation of Particles during the Braking Process: Experimental Approach

Prediction and comparative analysis of friction material properties using a GA-SVM optimization model

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