Reduction of brake emission by optimizing the curing condition for brake pads using an artificial neural network

The influence of brake pad curing conditions on brake emissions was studied to determine the optimum manufacturing conditions for minimum brake emissions with appropriate friction effectiveness. The Box-Behnken method and an artificial neural network were applied to determine an optimal curing condition by training with limited friction test results. The results showed that the curing condition considerably changed the friction level, pad wear, and brake emissions. The amount of pad wear was proportional to the mass concentration of the airborne particles, whereas no correlation was found with the number concentration due to the incoherent size distribution of ultrafine particles less than 0.1 μm. The pad hardness, which was changed by the degree of crosslinking of the binder resin, correlated well with the pad wear and brake emission. The artificial neural network applied to find the optimum curing condition for minimum brake emissions produced reliable results while satisfying the friction effectiveness for brake performance. •Curing conditions for the brake pad considerably affected brake emission.•The mass of PM and pad wear were inversely proportional to pad hardness.•The number of PM showed no correlation with the pad wear due to ultrafine particles.•ANN predicted optimal curing conditions efficiently with limited test results.