Experimental investigation and neural network prediction of brakes and clutch material frictional behaviour considering the sliding acceleration influence

The developers of innovative automotive active systems have recently stimulated new interest toward the analysis of the frictional behaviour of brake and clutch facings. This paper presents the experimental results acquired with a laboratory setup on brake and clutch facing samples in sliding motion for different operating conditions. An artificial neural network has been used to obtain a comprehensive view of the influence of the main sliding parameters. The study has also taken into account the not weak influence of the sliding acceleration to improve the friction coefficient prediction during transient operations of these dry friction based devices.

[1]  Dragan Aleksendrić,et al.  Development of neural network model of disc brake operation , 2010 .

[2]  Dragan Aleksendrić,et al.  Neural network prediction of disc brake performance , 2009 .

[3]  Abdellatif Khamlichi,et al.  Optimizing the thermal properties of clutch facings , 2003 .

[4]  Mario Milanese,et al.  Yaw stability control design through a mixed sensitivity approach , 2007 .

[5]  Ho Jang,et al.  Tribological properties of brake friction materials with steel fibers , 2009 .

[6]  Najib Laraqi,et al.  Temperature and division of heat in a pin-on-disc frictional device—Exact analytical solution , 2009 .

[7]  R. Bell,et al.  A Study of the Stick-Slip Motion of Machine Tool Feed Drives: , 1969 .

[8]  A. Soom,et al.  Friction at a Lubricated Line Contact Operating at Oscillating Sliding Velocities , 1990 .

[9]  Albert Albers,et al.  Fe thermal analysis of a ceramic clutch , 2009 .

[10]  B. F. Yousif,et al.  An artificial neural network for prediction of the friction coefficient of multi-layer polymeric composites in three different orientations , 2010 .

[11]  Adolfo Senatore,et al.  AN APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO PISTON RING FRICTION LOSSES PREDICTION , 2007 .

[12]  M. R Raghavan,et al.  Analysis of the performance characteristics of a two-inertia power transmission system with a plate clutch , 1989 .

[13]  D. Mackay,et al.  Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys , 1996 .

[14]  Staffan Jacobson,et al.  Tribological surfaces of organic brake pads , 2000 .

[15]  A. J. Day,et al.  Investigation of Disc/Pad Interface Temperatures in Friction Braking , 2007 .

[16]  Fuping Wang,et al.  Comparison of friction and wear performances of brake material dry sliding against two aluminum matrix composites reinforced with different SiC particles , 2007 .

[17]  Anders Gåård,et al.  Temperature effects on adhesive wear in dry sliding contacts , 2010 .

[18]  E. Rabinowicz.  The Intrinsic Variables affecting the Stick-Slip Process , 1958 .

[19]  B. Armstrong-Hélouvry.  Stick slip and control in low-speed motion , 1993, IEEE Trans. Autom. Control..

[20]  Yafei Lu,et al.  Performance and evaluation of eco-friendly brake friction materials , 2010 .

[21]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[22]  Saul Herscovici.  Determining the Static and Dynamic Coefficient of Friction and Its Causes for Variation , 1969 .

[23]  Homer Rahnejat,et al.  The influence of the interface coefficient of friction upon the propensity to judder in automotive clutches , 1999 .

[24]  H. So,et al.  Characteristics of wear results tested by pin-on-disc at moderate to high speeds , 1996 .

[25]  G. L. Gissinger,et al.  A mechatronic conception of a new intelligent braking system , 2000 .

[26]  Dragan Aleksendrić,et al.  Neural network prediction of brake friction materials wear , 2010 .

[27]  Zhenyu Jiang,et al.  Prediction on wear properties of polymer composites with artificial neural networks , 2007 .

[28]  Kyo Il Lee,et al.  Vehicle lateral stability management using gain-scheduled robust control , 2006 .

[29]  Hongguang Li,et al.  Non-reversible friction modeling and identification , 2008 .

[30]  José Divo Bressan,et al.  Influence of hardness on the wear resistance of 17-4 PH stainless steel evaluated by the pin-on-disc testing , 2008 .

[31]  A. W. Minns,et al.  The extrapolation of artificial neural networks for the modelling of rainfall-runoff relationships , 2005 .

[32]  Johan A. K. Suykens,et al.  Artificial neural networks for modelling and control of non-linear systems , 1995 .

[33]  F Vasca,et al.  Torque Transmissibility Assessment for Automotive Dry-Clutch Engagement , 2011, IEEE/ASME Transactions on Mechatronics.

[34]  A. Refenes.  Neural Networks in the Capital Markets , 1994 .

[35]  Dragan Aleksendrić,et al.  Prediction of brake friction materials recovery performance using artificial neural networks , 2010 .

[36]  Koji Kato,et al.  The effects of friction-induced vibration on friction and wear , 1982 .

[37]  J. B. Sampson,et al.  Studies in Lubrication: XII. Friction Behavior During the Slip Portion of the Stick‐Slip Process , 1943 .

[38]  Johannes Schneider,et al.  Development of Al2O3 based ceramics for dry friction systems , 2005 .

[39]  S. K. Rhee.  WEAR MECHANISMS FOR ASBESTOS-REINFORCED AUTOMOTIVE FRICTION MATERIALS , 1974 .

[40]  J. C. Heap.  Application of Variable Coefficient of Friction and Wear to Block Brakes and Clutches , 1966 .