Capitalizing Data, Information and Knowledge on Mechanical Experiments through Ontologies

Abstract

Abstract. Experiments in a laboratory or a company, either physical or virtual, generate a large amount of data and information (raw data, consolidated results, experimental conditions, etc.) that has to be managed in order to preserve and enhance the knowledge and the expertise of the organization, especially when turnover happens. If simulation data management has been especially explored and standardized (STEP AP 209 for instance), experimental data did not create the same interests and only standard data management tools are proposed. In this paper, we propose an ontology to capitalize such information and knowledge, with an efficient reuse objective. This ontology, for which specific taxonomies have been defined according to the relevant literature of the domain, is illustrated on a set of data on previous experimental campaigns, corresponding to two kinds of mechanical experiments: tribology tests and material characterization tests.

tribAIn—Towards an Explicit Specification of Shared Tribological Understanding

“…One reason is the absence of capability to represent axioms, which add more semantics to the information [15]. Therefore, Xi et al [18] and Cheutet et al [14] proposed The second step (experimental procedure) is frequently carried out by standardized testing devices, producing measurement series as output files. Since tribological conclusions are based on the comparison of experimental results with those of a reference system, the experimental procedure involves different measurement series with a variation of the target variable.…”

“…Besides input and administration of test results, a suitable database would have to serve as a searching instrument for the selection of potential tribological pairings according to given requirements, and allow comparative evaluations of the behavior of different tribological systems or dependencies on testing conditions. To the authors' knowledge, most previously proposed approaches for creating holistic databases from tribological experiments focused on forcing test results into uniform relational schemes [14], using classical content management software (e.g., Tribocollect (https://agw1.bam.de/microsites/tribocollect/tribocollect_i.htm ), i-Tribomat (https://www.i-tribomat.eu/index.html )) or standard tools (e.g., Granta MI (https://grantadesign.com/ industry/products/granta-mi/ )). Classical databases are quite isolated due to their design to meet the requirements of a particular application or corporation [15].…”

“…One reason is the absence of capability to represent axioms, which add more semantics to the information [15]. Therefore, Xi et al [18] and Cheutet et al [14] proposed the usage of ontologies for knowledge representation in the field of tribology. Following the definition of Gruber [19], an ontology is an "explicit specification of a conceptualization", thus providing well-defined concepts and other objects of a domain as well as relations between, which are agreed upon by the respective interest group.…”