- Tytuł:
- Machine learning methods for diagnosing the causes of die-casting defects
- Autorzy:
-
Okuniewska, Alicja
Perzyk, Marcin
Kozłowski, Jacek - Powiązania:
- https://bibliotekanauki.pl/articles/29519775.pdf
- Data publikacji:
- 2023
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
fault diagnosis
machine learning tools
neural network
classification trees
support vector machine - Opis:
- The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world’s leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.
- Źródło:
-
Computer Methods in Materials Science; 2023, 23, 2; 45-56
2720-4081
2720-3948 - Pojawia się w:
- Computer Methods in Materials Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł