Combinatorial Screening of Ingredients for Steel Wool Based Semimetallic and Aramid Pulp Based Nonasbestos Organic Brake Materials

Abstract

Two important factors, good friction performance and low cost, were considered for the selection of raw materials. Ternary composites (A þ B þ C systems where A is steel wool or aramid pulp belonging to good wear resistant raw material group, B is an organic binder benzoxazine and C is an ingredient belonging to poor wear resistant raw material group) were designed to evaluate the interaction effects of A and C on friction performance. Seventeen ingredients, BaSO 4 , B 2 O 3 , BN, brass chips, CaCO 3 , Ca(OH) 2 , copper chips, CuS, Cu 2 S, H 3 BO 3 , iron powder, MgO, oxidized polyacrylonitrile (PAN) fiber, PMF (slag fiber), Sb 2 S 3 , Ultrafibe (wollastonite) and ZrSiO 4 combined respectively with steel wool or aramid pulp, tested using Friction Assessment and Screening Test (FAST) were classified into good (FAST can be lasted for 90 min), intermediate (FAST can be lasted for 90 min at one given composition) and poor (FAST cannot be lasted for 90 min at two given compositions) wear resistant performance. CaCO 3 , Ca(OH) 2 , MgO, oxidized PAN fiber, Sb 2 S 3 and Ultrafibe with lighter density have good wear resistant performance when they combined with steel wool. BaSO 4 , PMF and Ultrafibe have good wear resistant performance when they are combined with aramid pulp. The roles of steel wool and aramid pulp in the ternary friction materials were identified. The formation of iron-containing friction layer is an important factor to improve wear in steel wool based ternary composites and the excellent wear resistant performance of aramid pulp contributes to aramid pulp based ternary composites.

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

“…The components of group two (group II), which produced poor wear resistance, include BaSO 4 (FAST running time t = 15 min), BN (t = 10 s), B 2 O 3 (t = 10 min), brass chips (t = 41 min), CaCO 3 (t = 33 min), Ca(OH) 2 (t = 27 min), cashew (t = 62 min), copper chips (t = 75 min), CuS (t = 14 min), Cu 2 S (t = 26 min), H 3 BO 3 (t = 9 min), iron powder (t = 50 s), MgO (t = 2 min), oxidized PAN fiber (t = 2 min), PMF (SiO 2 + CaO, t = 33 min), Sb 2 S 3 (t = 3 min), Ultrafibe (CaSiO 3 , t = 36 min), and ZrSiO 4 (t = 19 min). However, by using A + B + C systems the components of group two can be further classified into group II-A and group II-B, where the components of group II-A have good wear resistant performance and the components of group II-B have poor wear resistant performance when they combined with the components of group one, respectively [2,15].…”

“…Fibers and fillers can be screened and the interaction effects between a binder and an additive can be screened using the two component systems (A + B systems, where A is an additive and B is a binder). The interaction effects between two additives can be evaluated using the three component systems (A + B + C systems, where A and C are two different ingredients) [2,15]. The interaction effects among three, four, five, and more additives can be evaluated using four, five, six, and more component systems.…”

Effect of Filler-Filler Interactions on Mechanical Properties of Phenol Formaldehyde Based Hybrid Composites

“…Especially in automotive brakes and clutches, Non-asbestos organic (NAO) based friction materials are used, which are essentially multi ingredient systems (containing more than 10 ingredients, in general) in order to achieve the desired amalgam of performance properties [16][17][18][19][20].…”

Modeling and optimization of friction materials based on genetic programming and experimental frictional data

“…3,4 In order to reduce such experimental workloads, relatively high costs of screening ingredients in the friction materials and obtaining a more reliable formulation, attempts have been taken to predict the behavior of friction materials using appropriate theoretical models. So far, several methods have been presented to design and optimize friction materials including: one variable at a time method (OVAT design), 5 using a database, 6 combinatorial approach, [7][8][9] Taguchi design, 10 uniform design, 11 application of chemometrics, 12 golden section principle coupled with relational grade analysis, 3,4,13 multi-criteria optimization, 14 and application of neural networks. 15,16 However, reliability of most of these techniques in terms of accuracy and efficiency is still controversial because of significant nonlinear behavior and strong interactions between ingredients.…”