The ANN analysis and Taguchi method optimisation of the brake pad composition

ŞekerMuammer,; Mutluİbrahim,; Aysal, Faruk Emre; Sinan, Atliİsmail; YAVUZ, İbrahim; Akçin, ErgünYelda

doi:10.1680/jemmr.21.00036

Cited by 6 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The friction coefficient was calculated as the ratio of the brake friction material friction force to the normal load applied to the pad surface (Şeker et al , 2021). Scanning electron microscopy (SEM) examined the microscopic analysis of friction surfaces.…”

Section: Methodsmentioning

confidence: 99%

Effect of limestone usage on tribological properties in copper and asbestos-free brake friction materials

Yavuz¹

2023

ILT

View full text Add to dashboard Cite

Purpose This study aims to determine the braking performance of limestone as a filler in brake friction materials. Design/methodology/approach Samples containing limestone material (30-35-40%), which can be an alternative to brake friction material filler, were produced. The samples were weighed on precision scales, mixed homogeneously and produced using the hot molding method. The physical and tribological properties of the produced samples were determined, and their microscopic analyzes were made with scanning electron microscopy. Findings As the amount of limestone increased, the density of the samples decreased. The friction coefficient and wear rates were close to each other and within the optimum limits for all samples. Limestone materials can be used instead of barite materials studied in the literature on brake linings. Microcracks were observed only in samples containing 30% and 35% limestone in microscopic images. Originality/value In this study, the wear rate, coefficient of friction and microstructures on the friction surfaces of brake friction materials containing limestone were investigated. The usability of limestone as a filler in brake friction materials provides valuable information to researchers and industrial organizations in the brake friction material field.

show abstract

Section: Methodsmentioning

confidence: 99%

Effect of limestone usage on tribological properties in copper and asbestos-free brake friction materials

Yavuz¹

2023

ILT

View full text Add to dashboard Cite

show abstract

“…Machine learning technology can accurately simulate the whole process of brake performance degradation and compare it with historical data to complete the condition monitoring and fault diagnosis [6]. The most commonly used machine learning methods mainly include the BP neural network [7], artificial neural network (ANN) [8], support vector machine (SVM) [9], and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization

Wang

et al. 2023

Actuators

View full text Add to dashboard Cite

As the core component of the automobile braking system, brake pads have a complex structure and high failure rate. Their accurate and effective state monitoring can help to evaluate the safety performance of brake pads and avoid accidents caused by brake failure. The wear process of automobile brake pads is a gradual, nonlinear, and non-stationary time-varying system, and it is difficult to extract its features. Therefore, this paper proposes a CNN-LSTM brake pad wear state monitoring method. This method uses a Convolutional Neural Network (CNN) to complete the deep mining of brake pad wear characteristics and realize data dimensionality reduction, and a Long Short-Term Memory (LSTM) network to capture the time dependence of the brake pad wear sequence, so as to construct the nonlinear mapping relationship between brake pad wear characteristics and brake pad wear values. At the same time, the artificial Gorilla Troops Optimization (GTO) algorithm is used to perform multi-objective optimization of the network structure parameters in the CNN-LSTM model, and its powerful global search ability improves the monitoring effect of the brake pad wear status. The results show that the CNN-LSTM model based on GTO multi-objective optimization can effectively monitor the wear state of brake pads, and its coefficient of determination R2 value is 0.9944, the root mean square error RMSE value is 0.0023, and the mean absolute error MAE value is 0.0017. Compared with the BP model, CNN model, LSTM model, and CNN-LSTM model, the value of the coefficient of determination R2 is the closest to 1, which is increased by 8.29%, 5.52%, 4.47%, 3.30%, respectively, which can more effectively realize the monitoring and intelligent early warning of the brake pad wear state.

show abstract

“…The mid-to-late stage prediction mainly uses machine learning methods to extract and train features from the collected raw data, simulate the whole process of system degradation, and compare the current working state with historical data to complete the prediction of remaining life. The most commonly used machine learning methods mainly include BP neural networks [3] , artificial neural networks (ANN) [4] , Support vector machines (SVM) [5] etc. However, machine learning methods do not dig deep into the hidden information of the data and do not consider the intrinsic correlation of the time series, which still needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM based on attention mechanism for brake pad remaining life prediction

WANG,

YU,

et al. 2022

Mech. Eng. Sci.

View full text Add to dashboard Cite

In order to predict the remaining service life of brake pads accurately and efficiently, and to achieve intelligent warning, this paper proposes a CNN-LSTM brake pad remaining life prediction model based on an attention mechanism. The model constructs a non-linear relationship between brake pad features such as brake temperature, brake oil pressure and brake speed and brake pad wear data through convolutional neural network (CNN) and long and short term memory network (LSTM), as well as capturing the time dependence that exists in the brake pad wear sequence. The attention mechanism is also introduced to assign different weight values to the features output from multiple historical moments, highlighting the features with high saliency and avoiding the influence of invalid features, so as to improve the prediction effect of the remaining brake pad life. The results show that the proposed CNN-LSTM-Attention model can effectively predict the remaining life of brake pads, with the mean absolute error MAE value of 0.0048, root mean square error RMSE value of 0.0059 and coefficient of determination R2 value of 0.9636; and compared with the BP model, CNN model, LSTM model and CNN-LSTM model, the coefficient of determination R2 values are closest to 1, with an improvement of 8.26%, 5.25%, 3.99% and 1.85% respectively, enabling more effective monitoring and intelligent warning of the remaining brake pad life.

show abstract

The ANN analysis and Taguchi method optimisation of the brake pad composition

Cited by 6 publications

References 43 publications

Effect of limestone usage on tribological properties in copper and asbestos-free brake friction materials

Effect of limestone usage on tribological properties in copper and asbestos-free brake friction materials

Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization

CNN-LSTM based on attention mechanism for brake pad remaining life prediction

Contact Info

Product

Resources

About