Prediction optimization of mechanical properties of ferrite stainless steels after rolling treatment with use of genetic algorithms

• Autorzy: Honysz R.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The article discusses the use of artificial neural networks for research and prediction of the impact of chemical elements and heat treatment parameters on the mechanical properties of stainless steels optimized by genetic algorithm. Design/methodology/approach: To improve the quality of artificial neural network models and improve their performance the number of input variables of artificial neural networks has been optimized with use of genetic algorithms. Then a computational model build with optimised artificial neural networks were trained and verified. Findings: Optimization, except of tensile strength Rm case, has allowed the development of artificial neural networks, which either showed a better or comparable result from base networks, and also have a reduced number of input variables. As a result, in computational model constructed with use of these networks the noise information is reduced. Research limitations/implications: Data analysis was needed to verify if obtained data used for modelling are relevant to use them in artificial neural networks training processes. Practical implications: The use of artificial intelligence allows the multifaceted development of stainless steels engineering, even if only a small number of descriptors is available. Constructed and optimised computational model build with use of optimised artificial neural networks allows prediction of mechanical properties of rolled ferritic stainless steels after normalization. Originality/value: Introduced model can be obtain in industry to reduce manufacturing costs of materials. It can also simplify material selection, when engineer must properly choose the chemical elements and adequate plastic and/or heat treatment of stainless steels with required mechanical properties.

Słowa kluczowe

EN

numerical techniques; computational material science; artificial algorithms; stainless steel

PL

techniki numeryczne; komputerowa nauka o materiałach; algorytmy sztucznej inteligencji; stal szlachetna

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2015
• Tom: Vol. 68, nr 1
• Strony: 17--24
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Honysz R.

• Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

Bibliografia

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