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    Wyświetlanie 1-6 z 6
    Tytuł:
    The Use of Fuzzy Systems for Forecasting the Hardenability of Steel
    Autorzy:
    Sitek, W.
    Irla, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/356485.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    computational material science
    artificial intelligence methods
    materials design steels
    modelling
    simulation
    Opis:
    The goal of the research carried out was to develop the fuzzy systems, allowing the determination of the Jominy hardenability curve based on the chemical composition of structural steels for quenching and tempering. Fuzzy system was created to calculate hardness of the steel, based on the alloying elements concentrations, and to forecast the hardenability curves. This was done based on information from the PN-EN 10083-3: 2008. Examples of hardenability curves calculated for exemplar steels were presented. Results of the research confirmed that fuzzy systems are a useful tool in evaluation the effect of alloying elements on the properties of materials compared to conventional methods. It has been demonstrated the practical usefulness of the developed models which allows forecasting the steels’ Jominy hardenability curve.
    Źródło:
    Archives of Metallurgy and Materials; 2016, 61, 2A; 797-802
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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:
    Modelling of impact of temperature gradient on content of polymer ampoule during its forming
    Autorzy:
    Koloskova, G. M.
    Koloskov, V. Yu.
    Powiązania:
    https://bibliotekanauki.pl/articles/368021.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    computational material science and mechanics
    polymer ampoule
    heating packaging method
    temperature modelling
    komputerowa nauka o materiałach
    polimery
    modelowanie temperatury
    Opis:
    Purpose: The aim of the represented study was to model the impact of temperature gradient on content of polymer ampoule during its forming. Design/methodology/approach: The model of polymer ampoules forming is built in SolidWorks software on the basis of finite element method. Using the developed model the study of temperature condition changes is carried out. Numerical modelling was carried out for two types of polymer packaging materials – polypropylene and polyvinylchloride – in similar conditions. Findings: During polymer ampoule forming the highest temperature of liquid is obtained at the bottom of it. The most effective packaging method is to form the ampoule from polypropylene by means of aluminium die. Investigation results have shown that the highest obtained liquid temperature has linear dependence from initial one. Linear coefficients of heating were evaluated for polypropylene (equal to 0.72) and polyvinylchloride (equal to 0.58). Practical implications: Decrease of initial liquid temperature value gives an opportunity to expand the range of products allowed to be packed in polymer ampoules in represented method. Safe conditions for packaging of liquid products in polymer ampoules are formulated, The results of the study may be used to improve the quality of liquid products packaging in polymer ampoules. Originality/value: For the first time the model was developed for determination of liquid heating degree during its packaging in polymer ampoules. The calculations of the temperature distribution are represented for polypropylene and polyvinylchloride ampoules forming by means of aluminium and ceramic dies. The results of the study may be of interest to specialists in the field of polymer packaging manufacturing for food or pharmaceutical industry.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2020, 100, 1; 12-19
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing Engineering
    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
    Tytuł:
    Modelling of stresses and strains in two-layer combined materials at their formation
    Autorzy:
    Koloskova, G. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/379456.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    computational material science and mechanics
    multi-layer combined material
    stress and strain modelling
    calendering method
    komputerowa nauka o materiałach
    wielowarstwowy materiał łączony
    modelowanie naprężeń i odkształceń
    Opis:
    Purpose: The aim of the represented study was to model the behaviour of two-layer combined material during its manufacturing. Design/methodology/approach: The model of material layers joining by means of calender method is built in LS-DYNA software on the basis of finite element method. Using the developed model the study of stress and strain condition changes is carried out. Numerical modelling was carried out for two types of two-layer combined materials in similar conditions. First combination was of high-density polyethylene top layer and aluminium foil bottom layer. Second combination was of high-density polyethylene top layer and low- density polyethylene bottom layer. Joining materials had equal thicknesses. Findings: During formation of two-layer combined materials the primary strain always occurs at the bottom plate of the bottom layer. However, the maximum plastic strain will be represented for the layer with lower elastic modulus value. At the point of the highest loading applied to the two-layer combined material the elasticity condition is changed to the plasticity one and the yield process is registered. Practical implications: Multi-layer combined materials are some of the most advanced types of materials. The quality of the joining of the layers, the strains and the stresses arising in their manufacturing process are the main causes of low interlayer strength. It leads to easy exfoliation and destruction of the material. The results of the study may be used to improve the quality of multi-layer combined materials. Originality/value: For the first time the model was developed for the determination of strains and stresses arising during the formation of multi-layer combined materials by means of calendering method. The calculations of the stresses and strains distribution dynamics for two-layer combined materials are represented for polymer-metal and polymer-polymer layers combinations. The results of the study may be of interest to specialists in the field of multi-layer combined materials designing and manufacturing.
    Źródło:
    Archives of Materials Science and Engineering; 2019, 97, 1/2; 12-19
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Artificial Neural Networks in Modeling of Manufactured Front Metallization Contact Resistance for Silicon Solar Cells
    Zastosowanie sztucznych sieci neuronowych w modelowaniu rezystancji kontaktu wytwarzanej przedniej metalizacji krzemowych ogniw słonecznych
    Autorzy:
    Musztyfaga-Staszuk, M.
    Honysz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/356591.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    computational material science
    artificial neural networks
    silicon solar cell
    selective laser sintering
    screen printing
    co-firing in the furnace
    zastosowanie sztucznych sieci neuronowych
    krzemowe ogniwa słoneczne
    selektywne spiekanie laserowe
    sitodruk
    wypalanie w piecu
    Opis:
    This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP) method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace) and unconventional (2. Selective Laser Sintering). Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM). Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.
    Artykuł przedstawia zastosowanie sztucznych sieci neuronowych do predykcji rezystancji przedniej metalizacji w krzemowych ogniwach słonecznych. Oceniono wpływ tak wytworzonej elektrody przedniej na własności elektryczne ogniw fotowoltaicznych. Przednią elektrodę ogniw fotowoltaicznych naniesiono metodą sitodruku SP (ang. Screen Printing) i następnie wytwarzano dwoma metodami: konwencjonalną (1. wypalanie w piecu taśmowym) i niekonwencjonalną (2. selektywne spiekanie laserowe). Do wyznaczenia rezystancji elektrod przednich zastosowano metodę linii transmisyjnych TLM (ang. Transmission Line Model). Sztuczne sieci neuronowe zostały opracowane z wykorzystaniem pakietu Statistica Neural Network firmy Statsoft. Opracowane sztuczne sieci neuronowe umożliwią modelowanie rezystancji wytworzonej przedniej metalizacji i ułatwią lepszy dobór parametrów produkcji. Następujące zalecenia technologiczne sitodruku połączonego z wypalaniem w piecu i selektywnym spiekaniem laserowym takie jak optymalny skład pasty, morfologię podłoża krzemowego, temperaturę wypalania oraz moc i prędkość skanowania wiązki laserowej, do wytworzenia przedniej elektrody krzemowych ogniw słonecznych dobrano eksperymentalnie celem uzyskania celem uzyskania jednolicie stopionej struktury dobrze przylegającej do podłoża, małej wartości rezystancji połączenia elektrody przedniej z podłożem. Możliwość estymacji rezystancji przedniej metalizacji jest wartościowa dla producentów i konstruktorów. Pozwala ona na dotrzymanie wymagań klienta i przynosi wymierne zyski.
    Źródło:
    Archives of Metallurgy and Materials; 2015, 60, 3A; 1673-1678
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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