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

Wansu Song, Jongsung Park, Hyungjo Seo, Jinsoo Choi, Jung Ju Lee, Seok Su Sohn, Ho Jang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number204606
Publication statusPublished - 2023 Mar 15

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (Grant number: 20003598 , Development of the high performance brake for passenger car and commercial vehicle to reduce particulate matters) funded by the Ministry of Trade, Industry & Energy (MOTIE, South Korea).

Publisher Copyright:
© 2022 Elsevier B.V.


  • Artificial neural network
  • Brake emission
  • Friction materials
  • Phenolic resin

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Surfaces and Interfaces
  • Surfaces, Coatings and Films
  • Materials Chemistry


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