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    Wyświetlanie 1-2 z 2
    Tytuł:
    Simulation Modeling in Production Effectiveness Improvement – Case Study
    Autorzy:
    Burduk, Anna
    Łapczynska, Dagmara
    Popiel, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/1841424.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    simulation
    model
    batch
    resource
    efficiency
    computer application
    production
    Opis:
    The paper deals with the problem of production material flow management. The proper way of logistic tasks management has an impact on the production process effectiveness and the cycle time, which is a very important factor in manufacturing. Reducing the production process cycle time results not only in the ability to provide more customers with orders but also in increasing the level of resources usage (machines, operators etc.). In order to reach the aim of improving production effectiveness, the simulation modeling was used. It is a computer method that supports a decision-making process and allows to perform experiments on production without interfering with the real process. The paper also includes a risk analysis performed to evaluate the imperfections of simulation modeling, based on the rules of fuzzy logic.
    Źródło:
    Management and Production Engineering Review; 2021, 12, 2; 75-85
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of instance-based learning for cast iron casting defects prediction
    Autorzy:
    Sika, Robert
    Szajewski, Damian
    Hajkowski, Jakub
    Popielarski, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/407199.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    soft modelling
    instance-based learning
    k-nearest neighbours algorithm
    cast iron casting defects
    computer application
    Opis:
    The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors’ computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to “product index”.
    Źródło:
    Management and Production Engineering Review; 2019, 10, 4; 101-107
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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