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Brake linings have complex microstructure and consist of different components. Fast growing automotive industry requires new brake lining materials to be developed at considerably shorter time periods. The purpose of this research was to generate the knowledge for optimizing of brake friction materials formula with mathematical methods which can result in minimizing the number of experiments/test, saving development time and costs with optimal friction performance of brakes. A combination of processing methods, raw materials and testing supported with the Artificial Neural Network (ANN) and Taguchi design of experiment (DOE) allowed achieving excellent results in a very short time period. Friction performance and wear data from a series of Friction Assessment and Screening Test (FAST) were used to train an artificial neural network, which was used to optimize the formulations. The averaged COF, COF variation and wear were used as the output parameters.

Automotive brake lining materials are complex composites consisting of numerous ingredients allowing for their optimal performance. Since regulations are increasingly limiting Cu content in brake pads and Cu exhibits extremely high thermal conductivity, graphites being excellent heat conducting materials themselves, are often considered for use as potential Cu replacement. This paper surveys the role of two types of carbons (Superior Graphite) with high thermal conductivity but different mechanical properties and morphology: the so-called i) purified flake graphite (PFG) and the ii) resilient graphitic carbon (RGC). A successful “high-end” commercial low-metallic brake pad was re-formulated (SIU Carbondale) by removing of over 20 wt. % of Cu and replacing it with a cocktail of ingredients including 15 wt. % of these two graphite types (RGC and PFG).