Novel friction stabilization technology for surface damage conditions using machine learning

Abstract

The surface damage is a serious cause of failure in tribosystems. In the present paper, we propose a new damage avoidance method that combines a contact position control system (e.g., morphing surface) and artificial-intelligence-based control (e.g., genetic algorithm: GA) to achieve stable friction and long life of sliding surfaces. In the case of the single-damage condition, the GA sequentially avoided contact with the damaged position, and finally complete damage avoidance was achieved. In the multiple-damage condition, we confirmed that learning by GA effectively stabilized friction, although the learning time was longer. In summary, the contact-position control method should provide new capabilities on real machine surfaces where unexpected damage occurs.