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    Wyświetlanie 1-2 z 2
    Tytuł:
    Selection of Casting Materials for Working Parts of Machines for the Forestry Sector
    Autorzy:
    Pirowski, Zenon
    Bitka, Adam
    Grudzień-Rakoczy, Małgorzata
    Małysza, Marcin
    Pysz, Stanisław
    Wieliczko, Piotr
    Wilk-Kołodziejczyk, Dorota
    Powiązania:
    https://bibliotekanauki.pl/articles/2106604.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    mulching
    forestry tools
    tribological wear
    abrasion-resistant cast steel
    hardfacing surface
    Opis:
    The article was created as a result of the work TECHMATSTRATEG 1 program “Modern Material Technologies” as part of the project with the acronym INNOBIOLAS entitled “Development of innovative working elements of machines in the forestry sector and biomass processing based on high-energy surface modification technologies of the surface layer of cast elements”; agreement No. TECHMATSTRATEG1/348072/2/NCBR/2017. The article discusses the procedure for selecting casting materials that can meet the high operational requirements of working tools of mulching machines: transfer of high static and dynamic loads, resistance to tribological wear, corrosion resistance in various environments. The mulching process was briefly described, then the alloys were selected for experimental tests, model alloys were made and perform material tests were carried out in terms of functional and technological properties. The obtained results allowed to select the alloy where the test castings were made.
    Źródło:
    Archives of Metallurgy and Materials; 2022, 67, 2; 743--752
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Developing a Methodology for Building the Knowledge Base and Application Procedures Supporting the Process of Material and Technological Conversion
    Autorzy:
    Wilk-Kołodziejczyk, Dorota
    Jaśkowiec, Krzysztof
    Bitka, Adam
    Pirowski, Zenon
    Grudzień-Rakoczy, Małgorzata
    Chrzan, Konrad
    Małysza, Marcin
    Doroszewski, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/2134111.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    artificial intelligence
    material conversion
    technological conversion
    selection of parameters
    prediction of mechanical properties
    Opis:
    The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.
    Źródło:
    Archives of Metallurgy and Materials; 2022, 67, 3; 1085--1091
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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