Machine Learning in Tribology – More than Buzzwords?

Abstract

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or
artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications.
To help pave the way, this Special Issue (SI) aimed to present the latest research on ML or AI approaches for solving tribology-related issues. The focus was less on presenting new ML or AI methods but rather on demonstrating the possible applications of existing methods and their adaptation to problems in tribology. We are pleased that the SI has collected ten articles including a perspective [1], a technical note [2], seven original research articles [3–9], and a review [10]. The contributions came from both academia and industry all around the globe and presented cutting-edge research in the field and provided deep insights into the development or the application of sophisticated ML or AI approaches to resolve problems broadly related to friction, lubrication and wear.
Rosenkranz et al. [1] opened the SI by highlighting successful case studies using AI methods in a tribological context, e.g., online condition monitoring, designing material compositions, lubricant formulations, or lubrication and fluid film formation.
Almqvist [2] derived a physics-informed neural network (PINN) applicable to solve
initial and boundary value problems described by linear ordinary differential equations in
the context of hydrodynamic lubrication. In contrast to finite-element- or finite-differencebased methods, the fully explicit mathematical description of the PINN is a meshless method, and the training did not require large amounts of data as are typically employed for other AI/ML training procedures.
Prost et al. [3] trained a semi-supervised Random Forest (RF) online classifier for the operational state of a self-lubricating steel shaft/bronze pairing using experimental data.
Thereby, automatically generated labels or full manual labelling by an expert user can be employed. They reported that the labelling of the individual cycles from the lateral force tribometer data was crucial for a high prediction accuracy.
Zambrano et al. [4] utilized Reduced Order Modeling (ROM) to predict the friction
behavior of dynamic rubber applications under different operating conditions and to find optimized micro-texture parameters such as depth, diameter, or distance. The approach was also used to evaluate the influence manufacturing deviations of the surface textures on friction. With respect to an industrial context, it is believed that the product performance of rubber products could be optimized by tailoring micro-textures and controlling nominal texture tolerances prior to production.