Prediction of automotive friction material characteristics using artificial neural networks-cold performance

Aleksendrić, Dragan; Duboka, Cedomir

doi:10.1016/j.wear.2005.10.006

Cited by 57 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results were collected, but there was no theoretical value to use for comparison. Other approaches, such as those using neural networks, focus on post processing the brake gain offline [36].…”

Section: Estimation Algorithm Equationsmentioning

confidence: 99%

A high performance pneumatic braking system for heavy vehicles

Miller

Cebon

2010

Vehicle System Dynamics

View full text Add to dashboard Cite

Section: Estimation Algorithm Equationsmentioning

confidence: 99%

A high performance pneumatic braking system for heavy vehicles

Miller

Cebon

2010

Vehicle System Dynamics

View full text Add to dashboard Cite

“…In [61] a neural model is proposed to predict the BLCF by using as input parameters the brake pad composition, the manufacturing process conditions and the brake operating conditions. In [62] another approach is proposed that in addition to the temperature, speed and load dependence of the BLCF also considers the role of hysteresis phenomenon by including the sliding acceleration influence.…”

Section: Neural Network For Blcf Estimationmentioning

confidence: 99%

Survey on Modelling and Techniques for Friction Estimation in Automotive Brakes

et al. 2017

View full text Add to dashboard Cite

Abstract:The increased use of disc brakes in passenger cars has led the research world to focus on the prediction of brake performance and wear under different working conditions. A proper model of the brake linings' coefficient of friction (BLCF) is important to monitor the brake operation and increase the performance of control systems such as ABS, TC and ESP by supplying an accurate estimate of the brake torque. The literature of the last decades is replete with semi-empirical and analytical friction models whose derivation comes from significant research that has been conducted into the direction of friction modelling of pin-disc couplings. On the contrary, just a few models have been developed and used for the prediction of the automotive BLCF without obtaining satisfactory results. The present work aims at collecting the current state of art of the estimation techniques for the BLCF, with special attention to the models for automotive brakes. Moreover, the work proposes a classification of the several existing approaches and discusses the relative pro and cons. Finally, based on evidence of the limitations of the model-based approach and the potentialities of the neural networks, the authors propose a new state observer for BLCF estimation as a promising solution among the supporting tools of the control engineering.

show abstract

“…As discussed in [20,21], hybrid intelligent systems based on different integration schemes of neural networks, fuzzy logic and genetic algorithms have recently received much attention. Artificial neural networks (ANNs) and genetic algorithms (GA) have proved to be very effective in substitution of direct simulation and optimization in several contests, including manufacturing processes [22][23][24][25][26][27], composite material performance [28,29], mechanical and/or microstructural properties [30], and assisted process planning [31,32]. As far as the application of ANNs to curing simulation and optimization is concerned, early contributions can be individuated in [33,34], discussing the development of a static neural network to simulate the curing process, recalled by a nonlinear programming scheme.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

2016

View full text Add to dashboard Cite

An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures

show abstract

Prediction of automotive friction material characteristics using artificial neural networks-cold performance

Cited by 57 publications

References 24 publications

A high performance pneumatic braking system for heavy vehicles

A high performance pneumatic braking system for heavy vehicles

Survey on Modelling and Techniques for Friction Estimation in Automotive Brakes

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

Contact Info

Product

Resources

About