Szczegóły publikacji
Opis bibliograficzny
Dataset for anomalies detection in 3D printing / Tomasz SZYDŁO, Joanna SENDOREK, Mateusz Windak, Robert BRZOZA-WOCH // W: Computational Science – ICCS 2021 : 21st international conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 4 / eds. Maciej Paszyński, [et al.]. — Cham : Springer Nature Switzerland, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12745. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77969-6; e-ISBN: 978-3-030-77970-2. — S. 647–653. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134760 |
|---|---|
| Data dodania do BaDAP | 2021-06-28 |
| DOI | 10.1007/978-3-030-77970-2_50 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 21st International Conference on Computational Science |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
Nowadays, the Internet of Things plays a significant role in many domains. Especially, Industry 4.0 is making significant usage of concepts like smart sensors and big data analysis. IoT devices are commonly used to monitor industry machines and detect anomalies in their work. This paper presents and describes a set of data streams coming from a working 3D printer. Among others, it contains accelerometer data of printer head, intrusion power and temperatures of the printer elements. In order to gain data, we lead to several printing malfunctions applied to the 3D model. The resulting dataset can therefore be used for anomalies detection research.