Theory and practice of material development under imperfect information

Abstract

Material development is an important research problem in material science and engineering. Nowadays, computational approaches to these problems are used to alternate natural experiments. These approaches include data mining, machine learning and computational intelligence tools that rely on big data on material characteristics collected over long period experiments. One of the important issues in solving these problems is imperfect nature of information. In the present study we outline fuzzy logic and Z-number concept-based computational methodologies for material synthesis and selection to account for imprecision and partial reliability of relevant information. Several examples are provided to confirm validity of the study.