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    Wyświetlanie 1-5 z 5
    Tytuł:
    Information and Decision System Supporting the Production of ADI Cast Iron Products
    Autorzy:
    Opaliński, Andrzej
    Wilk-Kołodziejczyk, Dorota
    Powiązania:
    https://bibliotekanauki.pl/articles/2049745.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    decision support
    information system
    ADI casting
    austempered ductile cast iron
    ADI
    Opis:
    The presented article concerns the issue of supporting the ADI cast iron product manufacturing process and presents an IT system dedicated mainly to designers and technologists. Designers can be supported at the stage of selecting types of materials and technologies (including ADI cast iron) to produce products with required properties. Technologists can obtain support in determining the parameters (temperature and chemical) of the ADI cast iron manufacturing process in order to obtain products with specific properties. The system also contains an information resources (standards, documentation, examples) concerning ADI cast iron and products made of it. Examples of use by individual system users are presented as a case study.
    Źródło:
    Archives of Metallurgy and Materials; 2021, 66, 2; 651-657
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of Selected Mechanical Properties in Austempered Ductile Iron with Different Wall Thickness by the Decision Support Systems
    Autorzy:
    Jaśkowiec, Krzysztof
    Opaliński, Andrzej
    Kustra, Piotr
    Jach, D.
    Wilk-Kołodziejczyk, Dorota
    Powiązania:
    https://bibliotekanauki.pl/articles/27314159.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    ADI
    austempered ductile iron
    mechanical properties
    prediction
    decision support systems
    decision tree
    żeliwo sferoidalne
    właściwości mechaniczne
    prognozowanie
    systemy wspomagania decyzji
    Opis:
    The structure of Austempered Ductile Iron (ADI) is depend of many factors at individual stages of casting production. There is a rich literature documenting research on the relationship between heat treatment and the resulting microstructure of cast alloy. A significant amount of research is conducted towards the use of IT tools for indications production parameters for thin-walled castings, allowing for the selection of selected process parameters in order to obtain the expected properties. At the same time, the selection of these parameters should make it possible to obtain as few defects as possible. The input parameters of the solver is chemical composition Determined by the previous system module. Target wall thickness and HB of the product determined by the user. The method used to implement the solver is the method of Particle Swarm Optimization (PSO). The developed IT tool was used to determine the parameters of heat treatment, which will ensure obtaining the expected value for hardness. In the first stage, the ADI cast iron heat treatment parameters proposed by the expert were used, in the next part of the experiment, the settings proposed by the system were used. Used of the proposed IT tool, it was possible to reduce the number of deficiencies by 3%. The use of the solver in the case of castings with a wall thickness of 25 mm and 41 mm allowed to indication of process parameters allowing to obtain minimum mechanical properties in accordance with the PN-EN 1564:2012 standard. The results obtained by the solver for the selected parameters were verified. The indicated parameters were used to conduct experimental research. The tests obtained as a result of the physical experiment are convergent with the data from the solver.
    Źródło:
    Archives of Foundry Engineering; 2023, 23, 2; 137--144
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Computer modelling of the ablation casting process and prediction of the strength properties of AC-42000 castings
    Autorzy:
    Małysza, Marcin
    Puzio, Sabina
    Major-Gabryś, Katarzyna
    Głowacki, Mirosław
    Wilk-Kołodziejczyk, Dorota
    Kamińska, Jadwiga
    Powiązania:
    https://bibliotekanauki.pl/articles/29520071.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    casting
    simulations
    ablation
    gravity sand casting
    gravity die casting
    mechanical properties
    Opis:
    The demand for castings with superior properties has compelled the development and optimization of manufacturing technologies. By further developing already known techniques, we are able to contribute to the introduction of new research possibilities. The article presents the methodology of conducting simulation tests of the gravity casting process into sand moulds with the use of ablation. The ablation technique consists in spraying water through evenly spaced nozzles onto a mould into which the liquid casting alloy has been poured. The conducted research focuses on an alloy from the group of Al-Si alloys. In order to compare the effects of different techniques, additional tests were carried out for gravity casting into sand and metal die moulds. At the same time, virtual experiments were conducted to develop a simulation methodology for ablation casting technology, taking into account mould degradation. Additionally, the possibility of predicting the final mechanical properties of various manufacturing technologies was tested. Destructive tests were carried out to determine the mechanical properties in the cast samples, as well as microstructure tests and secondary dendrite spacing. The results of the mechanical tests are compared with the predicted simulation properties.
    Źródło:
    Computer Methods in Materials Science; 2022, 22, 2; 79-88
    2720-4081
    2720-3948
    Pojawia się w:
    Computer Methods in Materials Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
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