Szczegóły publikacji
Opis bibliograficzny
Exploration and learning algorithms used for predicting casting properties / Sandra GAJOCH, Łukasz MARCJAN, Mateusz Witkowski, Adam Bitka, Krzysztof Jaśkowiec, Marcin Małysza, Dorota WILK-KOŁODZIEJCZYK // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings, Pt. 3 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15909). — ISBN: 978-3-031-97563-9; e-ISBN: 978-3-031-97564-6. — S. 261–275. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-06. — A. Bitka, K. Jaśkowiec, M. Małysza - afiliacja: Łukasiewicz Research Network-Krakow Institute of Technology, Kraków ; D. Wilk-Kołodziejczyk - dod. afiliacja: Łukasiewicz Research Network-Krakow Institute of Technology, Kraków
Autorzy (7)
- AGHGajoch Sandra
- AGHMarcjan Łukasz
- AGHWitkowski Mateusz
- Bitka Adam
- Jaśkowiec Krzysztof
- Małysza Marcin
- AGHWilk-Kołodziejczyk Dorota
Dane bibliometryczne
| ID BaDAP | 161060 |
|---|---|
| Data dodania do BaDAP | 2025-07-18 |
| DOI | 10.1007/978-3-031-97564-6_21 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This article presents an analysis of the application of machine learning algorithms for predicting the mechanical properties of austempered ductile iron (ADI) castings. As part of the study, predictive models were developed and optimized to forecast strength parameters based on chemical composition, thickness, and heat treatment process parameters. A detailed analysis of the impact of hyperparameters on algorithm effectiveness was conducted, along with a comparison of different parameter space exploration methods. The study evaluated the performance of various machine learning algorithms, identifying Gradient Boosting as the most effective for predicting mechanical properties. An additional outcome of this research is the development of a web application integrating the predictive models, allowing users to analyze the expected properties of castings based on input data. This solution has potential applications in the foundry industry, enabling better control over production processes and reducing costs associated with experimental selection of technological parameters. The results confirm that applying machine learning algorithms can significantly improve the prediction of ADI iron′s mechanical properties, paving the way for further automation and optimization of metallurgical production processes.
