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Brake pads to be used in the automotive sector are complex mixtures composed of several raw materials of different types (metals, resins, abrasives, lubricants, rubbers…) which undergo mechanical and thermal treatment during their fabrication. Because of the large number of variables affecting the properties of these materials a customization is needed for each specific application, which increases the cost of the final product. In this study a machine learning approach based on regression trees theory has been adopted aiming to support the formulation of friction materials. A model has been constructed to correlate material composition, fabrication and application parameters to the tribological behavior of brake pads tested on dyno benches. The validation of the model has been carried out with reference to a dataset composed of 1000 patterns involving 130 input variables and using as target output the mean friction coefficient measured during standard AK-Master test.

The aim of this work is to characterize the formation of metal pick-ups onto Automotive brake pads, that can lead to major wear problems for the friction material and for the rotors. The characterization of these metal pick-ups has been obtained by means of microscopy techniques (optical microscope and SEM analysis), by X-ray diffraction and thermal analysis, while it has been tried to evaluate the influence of morphological and compositional parameters (like amount of lubricants and of organic compounds) of the pad on the pick-ups formation by performing “true-scale” simulations following specific procedures.