tribAIn—Towards an Explicit Specification of Shared Tribological Understanding

Abstract

Within the domain of tribology, the science and technology for understanding and controlling friction, lubrication, and wear of relatively moving interacting surfaces, countless experiments are carried out and their results are published worldwide. Due to the variety of test procedures and a lack of consistency in the terminology as well as the practice of publishing results in the natural language, accessing and reusing tribological knowledge is time-consuming and experiments are hardly comparable. However, for the selection of potential tribological pairings according to given requirements and to enable comparative evaluations of the behavior of different tribological systems or testing conditions, a shared understanding is essential. Therefore, we present a novel ontology tribAIn (derived from the ancient Greek word “tribein” (= rubbing) and the acronym “AI” (= artificial intelligence)), designed to provide a formal and explicit specification of knowledge in the domain of tribology to enable semantic annotation and the search of experimental setups and results. For generalization, tribAIn is linked to the intermediate-level ontology EXPO (ontology of scientific experiments), supplemented with subject-specific concepts meeting the needs of the domain of tribology. The formalization of tribAIn is expressed in the W3C standard OWL DL. Demonstrating the ability of tribAIn covering tribological experience from experiments, it is applied to a use case with heterogeneous data sources containing natural language texts and tabular data.

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

“…For the training of the latter, the data set was randomly split into training (60%), validation (20%), and test (20%) data. To find the best prediction, 73 different ANNs (cascade forward back propagation, feed forward back propagation and layer recurrent) with Levenberg-Marquardt (LM) training function and a varying number of hidden layers (1-4), number of neurons (7)(8)(9)(10)(11)(12)(13)(14)(15), and different transfer functions (Logsig, Purelin) were tested stepwise (see Figure 3a-d). It was found that the linear regressions were able to describe the results within errors of ±8%.…”

“…This is certainly also due to the interdisciplinarity and the quantity of heterogenous data from simulations on different scales or manyfold measurement devices with individual uncertainties. Furthermore, friction and wear characteristics do not represent hard data, but irreversible loss quantities with a dependence on time and test conditions [9].…”

The Use of Artificial Intelligence in Tribology—A Perspective

“…With the fast-paced developments in the area of algorithms and computing power as well as the increasing availability and reusability of data [35], the utilization of AI in tribology will certainly increase in the upcoming years. To increase the range of applications and enhance the accuracy of the AI models, an online open platform could be created, on which the tribology community could share data of numerical simulations, surface characterizations and experiments.…”

“…This would enable controllers with ML/AI algorithms to be incorporated directly into these applications, e.g., rolling/sliding bearings, gears, brakes, clutches or the piston assembly, and used for performance prediction and adaptation to discontinuous and critical operating conditions. With the fast-paced developments in the area of algorithms and computing power well as the increasing availability and reusability of data [35], the utilization of AI in t bology will certainly increase in the upcoming years. To increase the range of applicatio and enhance the accuracy of the AI models, an online open platform could be created, which the tribology community could share data of numerical simulations, surface cha acterizations and experiments.…”

A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology

“…Within the domain of materials science, the "European Materials Modelling Ontology" (EMMO, https://emmc.info/emmo-info/, accessed on 14 December 2021) provides a representational ontology based on materials modelling and characterization knowledge. Furthermore, we recently introduced the tribAIn ontology [29] for reusing knowledge from tribological experiments. The domain ontology was built for the purpose of providing a common and machine-readable schema for structuring tribological experiments intending to improve reuse and shareability of testing results from different sources.…”