Szczegóły publikacji
Opis bibliograficzny
Machine learning application in the metal casting industry / MALINOWSKI Paweł // W: WFC 2022 [Dokument elektroniczny] : 74th World Foundry Congress : October 16–20, 2022, Busan, Korea : [abstract book]. — Wersja do Windows. — Dane tekstowe. — Korea : Korea Foundry Society, 2022. — S. [1–2]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dysk.agh.edu.pl/s/S5DkZgq6kRym244 [2023-02-13]. — Bibliogr. s. [2], Abstr.
Autor
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Dane bibliometryczne
| ID BaDAP | 145269 |
|---|---|
| Data dodania do BaDAP | 2023-02-22 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
Abstract
Machine learning is adopted across many industries to optimise production processes, improve quality, reduce production costs, or shorten manufacturing cycle time. It is no different in the foundry industry. In addition to solving a regression problem, random forest algorithms such as CatBoost, XGBOOST, LightGBM or neural networks, such as ANN - Artificial Neural Network, CNN - Convolutional Neural Network, RNN - Recursive Neural Network, allow classification or cluster analysis, image analysis or time series forecasting. This paper presents the examples of regression used to predict the mechanical properties of ADI cast iron and aluminium alloys and classification with image analysis for casting defects identification.
