- 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