Szczegóły publikacji
Opis bibliograficzny
The use of the XGBoost and kriging methods in the prediction of the microstructure of CGI cast iron / Ł. SZTANGERT, I. OLEJARCZYK-WOŻEŃSKA, K. REGULSKI, G. Gumienny, B. MRZYGŁÓD // Archives of Foundry Engineering [Dokument elektroniczny]. - Czasopismo elektroniczne ; ISSN 2299-2944. — Tytuł poprz.: Archiwum Odlewnictwa. — 2023 — vol. 23 no. 4, s. 22-33. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 32-33, Abstr. — Publikacja dostępna online od: 2023-11-07
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151529 |
|---|---|
| Data dodania do BaDAP | 2024-01-30 |
| Tekst źródłowy | URL |
| DOI | 10.24425/afe.2023.146671 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Archives of Foundry Engineering |
Abstract
Compacted Graphite Iron (CGI) is a unique casting material characterized by its graphite form and extensive matrix contact surface. This type of cast iron has a tendency towards direct ferritization and possesses a complex set of intriguing properties. The use of data mining methods in modern foundry material development facilitates the achievement of improved product quality parameters. When designing a new product, it is always necessary to have a comprehensive understanding of the influence of alloying elements on the microstructure and consequently on the properties of the analyzed material. Empirical studies allow for a qualitative assessment of the above-mentioned relationships, but it is the use of intelligent computational techniques that allows for the construction of an approximate model of the microstructure and, consequently, precise predictions. The formulated prognostic model supports technological decisions during the casting design phase and is considered as the first step in the selection of the appropriate material type.