CAFS Researchers are seeking better methods for developing and producing friction materials.

Solutions for Formulating Automotive Friction Materials
The objective is to solve four main problems related to automotive friction materials: (1) selection of raw materials with good performance and low cost; (2) interaction effects among raw materials; (3) optimization of friction material formulations, and (4) minimal environmental impact. Formulation solutions include a combinatorial formulation approach to evaluate raw materials, establish raw materials selection criteria, and find the interactions among raw materials, the Golden Section, multi-objective, and genetic algorithm optimization approaches to optimize friction material formulations have been developed.


Combinatorial Formulation Approach to Selection of Raw Materials
A combinatorial approach is shown schematically below. The binder systems (B) were used to evaluate organic binders: phenolic, benzoxazine, boron-phenolic, and phenolic triazine. A + B system was utilized to classify the raw materials into a good wear resistant ingredient and a poor wear resistant ingredient and to find the adhesion between filler or fiber and binder. A + B + C systems were performed to find the combined effects of A and C on friction performance where A and C are ingredients of group one or A is steel wool or Twaron (aramid pulp) and C is ingredients of group two. N component system was used to design and optimize new friction materials.

We have divided all raw materials into two groups: Group One, which has good wear resistance, includes Al2O3, Twaron (aramid pulp), graphite and coke, MoS2, steel wool and organic binders (phenolic, boron-phenolic, phenolic triazine and benzoxazine) and Group Two, which has poor wear resistance, includes BaSO4, BN, B2O3, brass chips, CaCO3, Ca(OH)2, cashew, copper chips, CuS, Cu2S, H3BO3, iron powder, MgO, oxidized PAN fiber, PMF (SiO2+CaO), Sb2S3, Ultrafibe (CaSiO3) and ZrSiO4. We recommend raw materials belonging to group one be primarily chosen in any formulation due to their good friction and wear performance. Good friction performance can also be obtained from the combinations of the raw materials belonging to Group Two including BaSO4, BN, B2O3, CaCO3, Ca(OH)2, copper chips, iron powder, MgO, oxidized PAN fiber, PMF, Sb2S3 and Ultrafibe with steel wool for developing semi-metallic or with Twaron (aramid pulp) for developing non-metallic friction materials. These components in group two can be also selected as raw materials due to their low cost.

The Golden Section Approach to Optimization of Friction Material Formulations
The Golden Section (0.618 principle) is shown below. The line meets a/(a+b)=b/a=0.618. It can be continuously divided and form a Golden Section sequence 0.618n. Several semi-metallic, non-metallic friction materials formulations and two industrial brake pad formulations have been optimized using the Golden Section approach. We divided the ingredients into three groups: fibers (F), fillers (f), and binder (b) for non-metallic friction materials, and metal (m), non-metal (nm), and binder (b) for semi-metallic friction materials. Then permutation of fibers or metal group (VF or Vm = 0.382), fillers group (Vf or Vnm = 0.382), and binder group (Vb = 0.236) according to the 0.618 principle, all possible formulations can be calculated (software for such calculations has been developed at CAFS).

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a (0.618) ............................b (0.382)

Multi-objective and Genetic Algorithm Optimization Techniques
The development of quality brake pad materials is a multi-objective optimization subject. The center's FAST machine has been modified to measure the wear of both the pad material and rotor. The structure of automotive friction materials is diverse since it contains fibers and fillers with different shapes and sizes. The Genetic Algorithm can be used to search the material structure that results in a desired friction performance. This method, in general, has a better chance to locate the near global optimum than other classical search methods, especially in problems with multiple variables and large search space. The genetic algorithm has been applied to establish a wear-composition model for optimization of morphology and friction performance of friction materials.

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