Exploring the mechanical behavior of friction material composites using artificial intelligence

Abstract

The application of artificial intelligence techniques is becoming increasingly valuable for several areas of knowledge, allowing extracting information, predict patterns, work with complex problems, and generate solutions that would not be achievable by other techniques without an extremely high computational cost or else numerous physical experiments, with high execution costs and which often do not achieve the desired result. In this work, artificial intelligence (AI) was used to create mathematical models for four properties of friction material composites from an extensive database containing the chemical composition and the respective mechanical properties. An algorithm capable of predicting mechanical results of friction materials based on chemical composition, optimizing an existing composition, and proposing new (previously nonexistent) compositions was proposed based on the desired values of each mechanical property. The algorithm combines rule-based instructions, neural networks, and particle swarm optimization. Physical samples based on the algorithm prediction were produced, making it possible to assess the predictive power of the models and better understand the improvements needed for predicting new friction materials. Notably, artificial neural networks, once trained, demonstrated an average root mean square error (RMSE) that was 30.8 % smaller compared to using multilinear fittings to predict new results from previously non-existent compositions. [resumo fornecido pelo autor]