**1. Introduction and Background**

There have been very recent advances in applying methods of deep or machine learning (ML) to improve tribological characteristics of materials by means of artificial intelligence (AI). AI is generally concerned with the design and construction of intelligent agents, which is anything that acts in the best way possible in any situation [1]. ML refers to a vast set of data-driven methods and computational tools for modelling and understanding complex datasets. These methods can be used to detect automatically patterns in datasets thus creating models to predict future data or other outcomes of interest under uncertainty [2–4]. Generally, ML methods can be divided into supervised learning and unsupervised learning [3,5], see Figure 1. Regarding predictive or supervised learning approaches, the aim is to learn a mapping from input vectors (training data) to their corresponding output vectors (target data). Depending on the nature of the target data, supervised approaches can be subdivided into classification or regression methods. When the output is a categorical or nominal variable from a finite set of discrete categories (e.g., type of surface finish, oil grade, lubricant additive, etc.), the problem is known as classification or pattern recognition. In contrast, when the output consists of one or more real-valued continuous variables (e.g., coefficient of friction, film thickness, temperature rise, etc.), the problem is defined as regression. The second type of machine learning approaches is denoted as descriptive or unsupervised learning. In this case, only inputs are provided without any corresponding output vectors. The goal is to find meaningful patterns and groups of similar features within the dataset (clustering), to determine the distribution of data in the input space (density estimation), or to reduce high-dimensional data space to two or three dimensions for visualization purposes (dimensionality reduction) [5]. Unlike supervised learning, for which comparisons can be made between the predictions to the

**Citation:** Rosenkranz, A.; Marian, M.; Profito, F.J.; Aragon, N.; Shah, R. The Use of Artificial Intelligence in Tribology—A Perspective. *Lubricants* **2021**, *9*, 2. https://dx.doi.org/ 10.3390/lubricants9010002

Received: 29 November 2020 Accepted: 23 December 2020 Published: 26 December 2020

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observed values, problems involving unsupervised learning are not well-defined since no additional information or obvious error metric is provided about the patterns to be 'discovered' in the dataset [5].

**Figure 1.** Diagram generally classifying existing machine learning methods and algorithms.

A prominent method that machines employ to learn is by using artificial neural networks (ANNs). These networks are based upon the network of neurons in the human brain and have the ability to "learn" in a fashion similar to the way humans do. An ANN is made of a network of model neurons, which can use algorithms to make them function like biological neurons. In this context, each model neuron has a threshold. The model neurons will receive many different inputs, which are summed up and sent an output equal to 1, if the sum is larger than the threshold. Otherwise, the output is 0. Machines are able to learn by modifying the thresholds of each model neuron, when a new example is introduced, until the thresholds reach a point to where they don't change much [6].

**Figure 2.** Correlation between material properties and testing conditions using artificial neural networks. Redrawn from [7,8].

In addition to ANNs, fuzzy systems are another type of models used in AI. These systems are based on fuzzy logic and represent a more human way of thinking in their application of inference. They are characterized by displaying a range of truth from 0 to 1 instead of displaying Boolean true/false results [9]. In the field of tribology, many tests on materials are typically performed, which define a set of tribological properties. This dataset can for example be incorporated to develop an ANN (Figure 2), which can be used for further optimization [8,10]. This perspective attempts to display some of the recent advances done in implementing AI, specifically but not limited to ANNs. Furthermore, we intend to address current challenges and future research directions towards tribologicalrelated problems.
