Development of Semantic Description for Multiscale Models of Thermo-Mechanical Treatment of Metal Alloys

Abstract

We present a process of semantic meta-model development for data management in an adaptable multiscale modeling framework. The main problems in ontology design are discussed, and a solution achieved as a result of the research is presented. The main concepts concerning the application and data management background for multiscale modeling were derived from the AM3 approach-object-oriented Agile multiscale modeling methodology. The ontological description of multiscale models enables validation of semantic correctness of data interchange between submodels. We also present a possibility of using the ontological model as a supervisor in conjunction with a multiscale model controller and a knowledge base system. Multiscale modeling formal ontology (MMFO), designed for describing multiscale models' data and structures, is presented. A need for applying meta-ontology in the MMFO development process is discussed. Examples of MMFO application in describing thermo-mechanical treatment of metal alloys are discussed. Present and future applications of MMFO are described.

Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements

“…In the control of industrial processes, hybrid or heuristic models have long been used; they enjoy great popularity giving better results than uniform tools [47][48][49][50][51][52]. The integration of input data [53] or of the results of such differentiated solutions is a separate issue, where an important role will have the semantic modelling, including ontologies [54], but this is the topic for an entirely new publication.…”

The Selection of Heat Treatment Parameters to Obtain Austempered Ductile Iron with the Required Impact Strength

“…Further studies should broaden the scope of approximation to other mechanical properties and can even take into account other parameters, e.g., the measurement data from foundry equipment (Ref 43). On the other hand, the acquired knowledge in the form of rules can be successfully used in the construction of knowledge bases for intelligent systems in foundry applications (Ref [44][45][46][47][48][49][50]. Material experiments conducted in the conditions presented in the article (with a specific chemical composition, with specific heat treatment parameters) constitute new knowledge that may be interesting for material researchers.…”