Optimization of a commercial brake pad formulation

Abstract

A brake pad material used in a popular, commercially available vehicle that consisted of steel wool, iron powder, graphite, coke, styrene-butadiene rubber, MgO, BaSO 4 , and phenolic resin was tested with the friction assessment and screening test. The average friction coefficient (0.357) and total wear (19.75 wt %) were measured. An alternative friction material formulated with identical constituents but optimized with the golden section principle and relational grade analysis was produced in a laboratory environment. This material exhibited an average friction coefficient of 0.419 and a low total wear of 6.25 wt %. An analysis of component costs indicated that the large volume price of the commercial material, $1.01/kg, was less than that of the laboratory material, $1.21/kg. However, the performance/cost ratio of the new material was appreciably greater.

A combinatorial approach for automotive friction materials: Effects of ingredients on friction performance

“…Kato et al [6] developed a genetic algorithm combined with multiple regression analysis to evaluate raw materials. The wear and friction coefficient (l) sensitivity series of raw materials were obtained by relational grade analysis [7][8][9] and least-squares error [10]. However, the scientific standards for selection and evaluation of raw materials have not been established yet.…”

Formulation Optimization of Friction Material with Golden Section Approach

“…In addition, the number of experiments is large because it is a multi-level experiment. While for Genetic Algorithm and Chemometrics methods, to drawn conclusions, much data with complex computation are required (LU, et al (13)). …”

Sensitivity series and friction surface analysis of non-metallic friction materials

“…There are several methods that address these problems and can be used to choose raw materials and optimize brake formulations. These include combinatorial friction materials research [1][2][3], Golden Section principle coupled with relational grade analysis [4,5], genetic algorithm optimization [6,7], applications of chemometrics [8], Taguchi design [9] and multi-criteria optimization [10,11].…”