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Hybrid modelling methods in materials science - selected examples

Keywords: Steels , Artificial intelligence methods , Modelling , Simulation

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Purpose: The paper presents selected examples of application of computational tools, including artificial intelligence methods to solve examples of tasks in the area of materials science. (i) Selection method of steel grade with required hardenability; (ii) Modelling of CCT diagrams for engineering and constructional steels; (iii) Application of neural networks for selection of steel with the assumed hardness after cooling from the austenitising temperature; (iv) Designing of high-speed steels chemical compositionDesign/methodology/approach: In the paper been applied a hybrid approach that combined application of various mathematical tools including artificial neural networks, linear regression and genetic algorithms to solve selected tasks from the area of materials science.Findings: Computer modelling and simulation make improvement of engineering materials properties possible, as well as prediction of their properties, even before the materials are fabricated, with the significant reduction of expenditures and time necessary for their investigation and application. Methods used in hybrid systems are complementary and disadvantages of one method are compensated by the advantages of another method.Practical implications: Solutions presented in the work, based on using the adequate material models may feature an interesting alternative in designing of the new materials with the required properties. The practical aspect has to be noted, resulting form the developed models, which may successfully replace the above mentioned technological investigations, consisting in one time selection of the chemical composition and heat treatment parameters and experimental verification of the newly developed materials to check of its properties meet the requirements.Originality/value: The presented approach to new materials design assumes the maximum possible limitation of carrying out the indispensable experiments, to take advantage of the existing experimental knowledge resources in the form of databases and most effective computer science tools, including neural networks and evolutionary algorithms.

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