Szczegóły publikacji
Opis bibliograficzny
Analysis of the possibility of making a digital twin for devices operating in foundries / Artur Lehrfeld, Krzysztof JAŚKOWIEC, Dorota WILK-KOŁODZIEJCZYK, Marcin MAŁYSZA, Adam BITKA, Łukasz MARCJAN, Mirosław GŁOWACKI // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2024 — vol. 13 iss. 2 art. no. 349, s. 1–12. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12, Abstr. — Publikacja dostępna online od: 2024-01-14. — K. Jaśkowiec, D. Wilk-Kołodziejczyk, M. Małysza, A. Bitka - dod. afiliacja: Łukasiewicz Research Network—Krakow Institute of Technology, Kraków, Poland. — M. Głowacki - dod. afiliacja: Faculty of Natural Sciences, Jan Kochanowski University of Humanities and Sciences in Kielce
Autorzy (7)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151504 |
|---|---|
| Data dodania do BaDAP | 2024-01-19 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics13020349 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author’s device, created by the “Łukasiewicz” Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device.