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The Use of Artificial Intelligence in Tribology—A Perspective

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The Use of Artificial Intelligence in Tribology—A Perspective lubricants Perspective The Use of Artificial Intelligence in Tribology—A Perspective Andreas Rosenkranz 1,*, Max Marian 2,* , Francisco J. Profito 3, Nathan Aragon 4 and Raj Shah 4 1 Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Santiago 7820436, Chile 2 Engineering Design, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), 91058 Erlangen, Germany 3 Department of Mechanical Engineering, Polytechnic School, University of São Paulo, São Paulo 17033360, Brazil; fprofi[email protected] 4 Koehler Instrument Company, Holtsville, NY 11742, USA; [email protected] (N.A.); [email protected] (R.S.) * Correspondence: [email protected] (A.R.); [email protected] (M.M.) 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 pa- rameters 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. Keywords: artificial intelligence; machine learning; artificial neural networks; tribology 1. Introduction and Background Citation: Rosenkranz, A.; Marian, M.; There have been very recent advances in applying methods of deep or machine Profito, F.J.; Aragon, N.; Shah, R. The Use of Artificial Intelligence in learning (ML) to improve tribological characteristics of materials by means of artificial Tribology—A Perspective. Lubricants intelligence (AI). AI is generally concerned with the design and construction of intelligent 2021, 9, 2. https://dx.doi.org/ agents, which is anything that acts in the best way possible in any situation [1]. ML refers 10.3390/lubricants9010002 to a vast set of data-driven methods and computational tools for modelling and under- standing complex datasets. These methods can be used to detect automatically patterns in Received: 29 November 2020 datasets thus creating models to predict future data or other outcomes of interest under Accepted: 23 December 2020 uncertainty [2–4]. Generally, ML methods can be divided into supervised learning and Published: 26 December 2020 unsupervised learning [3,5], see Figure1. Regarding predictive or supervised learning approaches, the aim is to learn a mapping from input vectors (training data) to their Publisher’s Note: MDPI stays neu- corresponding output vectors (target data). Depending on the nature of the target data, su- tral with regard to jurisdictional claims pervised approaches can be subdivided into classification or regression methods. When the in published maps and institutional output is a categorical or nominal variable from a finite set of discrete categories (e.g., type affiliations. 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.), Copyright: © 2020 by the authors. Li- the problem is defined as regression. The second type of machine learning approaches is censee MDPI, Basel, Switzerland. This denoted as descriptive or unsupervised learning. In this case, only inputs are provided article is an open access article distributed without any corresponding output vectors. The goal is to find meaningful patterns and under the terms and conditions of the groups of similar features within the dataset (clustering), to determine the distribution of Creative Commons Attribution (CC BY) data in the input space (density estimation), or to reduce high-dimensional data space to license (https://creativecommons.org/ two or three dimensions for visualization purposes (dimensionality reduction) [5]. Unlike licenses/by/4.0/). supervised learning, for which comparisons can be made between the predictions to the Lubricants 2021, 9, 2. https://dx.doi.org/10.3390/lubricants9010002 https://www.mdpi.com/journal/lubricants Lubricants 2021, 9, x FOR PEER REVIEW 2 of 11 Lubricants 2021, 9, x FOR PEER REVIEW 2 of 11 Lubricants 2021, 9, 2 2 of 11 data space to two or three dimensions for visualization purposes (dimensionality reduc- data space to two or three dimensions for visualization purposes (dimensionality reduc- tion) [5]. Unlike supervised learning, for which comparisons can be made between the tion) [5]. Unlike supervised learning, for which comparisons can be made between the predictionsobserved values, to the problemsobserved involvingvalues, problems unsupervised involving learning unsupervised are not well-defined learning are since not predictionsno additional to informationthe observed or values, obvious problems error metric involving is provided unsupervised about the learning patterns are to not be well-defined since no additional information or obvious error metric is provided about ’discovered’well-defined insince the datasetno additional [5]. information or obvious error metric is provided about the patterns to be ’discovered’ in the dataset [5]. the patterns to be ’discovered’ in the dataset [5]. Figure 1. Diagram generally classifying existing machine learning methods and algorithms. Figure 1. Diagram generally classifying existing machine learning methods and algorithms. A prominent method that machines employ to learn is by using artificial neural net- A prominent prominent method method that that machines machines employ employ to tolearn learn is by is using by using artificial artificial neural neural net- works (ANNs). These networks are based upon the network of neurons in the human worksnetworks (ANNs). (ANNs). These These networks networks are are based based up uponon the the network network of of neurons neurons in in the the human brain and have the ability to “learn” in a fashion similar to the way humans do. An ANN brain and have thethe abilityability toto “learn”“learn” in in a a fashion fashion similar similar to to the the way way humans humans do. do. An An ANN ANN is is made of a network of model neurons, which can use algorithms to make them function ismade made of of a networka network of of model model neurons, neurons, which whic canh can use use algorithms algorithms to make to make them them function function like like biological neurons. In this context, each model neuron has a threshold. The model likebiological biological neurons. neurons. In this In context, this context, each model each model neuron neuron has a threshold. has a threshold. The model The neurons model neurons will receive many different inputs, which are summed up and sent an output neuronswill receive will many receive different many inputs,different which inputs, are summedwhich are up summed and sent up an and output sent equal an output to 1, if equalthe sum to 1, is if larger the sum than is the larger threshold. than the Otherwise, threshold. the Otherwise, output is the 0. Machinesoutput is are0. Machines able to learn are equal to 1, if the sum is larger than the threshold. Otherwise, the output is 0. Machines are ableby modifying to learn by the modifying thresholds the of thresholds each model of neuron,each model when neuron, a new when example a new is introduced,example is able to learn by modifying the thresholds of each model neuron, when a new example is introduced,until the thresholds until the reach thresholds a point reach to where a point they to don’twhere change they don’t much change [6]. much [6]. 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 Figure 2. CorrelationCorrelation between between material material properties properties and and testing testing cond conditionsitions using using artificial artificial neur neuralal networks. networks. Redrawn Redrawn from [7,8]. from[7,8]. [7,8]. Lubricants 2021, 9, 2 3 of 11 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 (Figure2), 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 tribological- related problems. 2. Application Fields of AI in Tribology As briefly evidenced by several examples below, the use of AI and ML approaches already covers various fields of tribology, ranging from condition monitoring over the design of
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