The Use of Artificial Intelligence in Tribology—A Perspective

Abstract

Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

Recent Advances in Preparation and Testing Methods of Engine-Based Nanolubricants: A State-of-the-Art Review

“…Tribological Characteristics [197] A clear investigation was concluded about the effect of sliding velocity and load capacity on the formation of the nano tribo-films at the contacted surfaces [198] Frictional heating and anti-wear properties of the friction pair is controlled through the existence of nanoparticles [199] Simulations of MD clarified that Gr nano additives could form a thick layer of tribo-film that can help in reducing the coefficient of friction and friction force [200] Nanofluids have a higher transition pressure than the base fluid, with an excellent load-carrying capacity [201] Theoretical guidance was implemented for the lubrication mechanism of MOS2 nanoparticles [193] Confirmation of the ball-bearing lubrication mechanism of nanoparticles under mild velocities and loads [189] The load-carrying capacity of nanofluid is improved regarding the base oil before the rupture of the lubricant film [120] Atomistic simulations confirmed the mending mechanism of nanolubricants through which nanoparticles fill in valleys of the sliding asperities Over and above that, artificial intelligence models can also predict the different behaviours of lubricant performance, regarding their tribological and other behaviours [202][203][204]. One of these tools is the artificial neural network (ANN), which is considered a widely accepted novel modelling approach [203].…”

“…Basically, the ANN is composed of a network of mathematical functions, based upon the working methodology of neurons in the human brain. They can learn in a fashion similar to the way our brains do [202]. The mathematical functions use a complex dataset of experimental results as inputs, and then build a model that can predict and optimize the future results under different variations (Figure 27) [204].…”

Current Trends and Applications of Machine Learning in Tribology—A Review

“…Moreover, tribology is characterized by the fact that it is not yet possible to fully describe underlying processes with mathematical terms, e.g., by differential equations. 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 [5]. Thus, their potential also goes beyond purely academic aspects into actual industrial applications.…”

“…Therefore, this contribution aims to introduce the trends and applications of ML algorithms with relevance to the domain of tribology. While other reviews were more generic [10], had a more concise scope [5], or focused on a specific technique (i.e., artificial neural networks [11]), this review article is also intended to cover a wider range of techniques and in particular to shed light on the broad applicability to various fields with tribological issues. Thus, the interested reader shall be provided with a high-level understanding of the capabilities of certain methods with respect to the tribological applications ranging from composite materials over drive technology or manufacturing to surface engineering and lubricant formulations.…”

Effects of Self-Lubricant Coating and Motion on Reduction of Friction and Wear of Mild Steel and Data Analysis from Machine Learning Approach

“…The combined motion is used at the same time in different mechanical systems, robotics components, electronic devices, and similar real-life and industrial applications [ 22 , 23 , 24 , 25 , 26 , 27 ]. Several pieces of research have been done on friction and wear of different types of materials under different operating and processing conditions with computing approaches [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”