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    Wyświetlanie 1-2 z 2
    Tytuł:
    Optimization of Ferrite Stainless Steel Mechanical Properties Prediction with artificial Intelligence Algorithms
    Autorzy:
    Honysz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/354759.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    analysis and modelling
    numerical techniques
    computational material science
    artificial algorithms
    stainless steel
    Opis:
    The article presents a computational model build with the use of artificial neural networks optimized by genetic algorithm. This model was used to research and prediction of the impact of chemical elements and heat treatment conditions on the mechanical properties of ferrite stainless steel. Optimization has allowed the development of artificial neural networks, which showed a better or comparable prediction result in comparison to un-optimized networks has reduced the number of input variables and has accelerated the calculation speed. The introduced computational model can be applied in industry to reduce the manufacturing costs of materials. It can also simplify material selection when an engineer must properly choose the chemical elements and adequate plastic and/or heat treatment of stainless steels with required mechanical properties.
    Źródło:
    Archives of Metallurgy and Materials; 2020, 65, 2; 749-753
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction optimization of mechanical properties of ferrite stainless steels after forging treatment with use of genetic algorithms
    Autorzy:
    Honysz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/378963.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    numerical techniques
    Computational Material Science
    artificial algorithms
    stainless steel
    techniki numeryczne
    komputerowa nauka o materiałach
    algorytmy sztucznej inteligencji
    Stal nierdzewna
    Opis:
    Purpose: The paper describes the use of artificial neural networks to research and predict the effect of chemical components and thermal treatment conditions on stainless steel's mechanical characteristics optimized by genetic algorithm. Design/methodology/approach: The quantity of input variables of artificial neural networks has been optimized using genetic algorithms to enhance the prediction quality of artificial neural network and to enhance their efficiency. Then a computational model was trained and evaluated with optimized artificial neural networks. Findings: Optimization, with the exception of tensile strength, has enabled the creation of artificial neural networks, which either showed a better or similar performance from base networks, as well as a decreased amount of input variables As a consequence, noise data is decreased in the computational model built with the use of these networks. Research limitations/implications: Data analysis was required to confirm the relevance of obtaining information used for modelling to use in training procedures for artificial neural networks. Practical implications: Using artificial intelligence enables the multi-faceted growth of stainless steel engineering, even though there is only a relatively small amount of descriptors. Built and optimized computational model building using optimized artificial neural networks enables prediction of mechanical characteristics after normalization of forged ferritic stainless steels. Originality/value: In order to decrease production expenses of products, an introduced model can be obtained in manufacturing industry. It can also simplify the selection of materials if the engineer has to correctly choose chemical elements and appropriate plastics and/or heat processing of stainless steels, having the necessary mechanical characteristics.
    Źródło:
    Archives of Materials Science and Engineering; 2019, 100, 1/2; 13-20
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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