- AutorIn
- Dipl.-Ing. (FH) Stefan Katzenburg Hochschule Bochum Campus Velbert/Heiligenhaus
- Prof. Dr.-Ing. Clemens FallerHochschule Bochum Campus Velbert/Heiligenhaus
- Titel
- Measurement Data Acquisition & Automation in Research
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-897717
- Konferenz
- 20. AALE-Konferenz. Bielefeld, 06.03.-08.03.2024
- Quellenangabe
- Tagungsband AALE 2024
Herausgeber: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsort: Leipzig
Erscheinungsjahr: 2024
ISBN: 978-3-910103-02-3 - Erstveröffentlichung
- 2024
- DOI
- https://doi.org/10.33968/2024.64
- Abstract (EN)
- Researchers often work on experimental setups that are highly specialized and usually designed and manufactured for a single or a few test series. The challenges for automation technology and measurement data acquisition in the apparatus are as unique as these experimental setups. In the following, Bochum University of Applied Sciences presents its approach to implementing different measurement and automation tasks in a flexible but also standardized way. Existing microcontrollers such as Raspberry Pi, Arduino or Controllino are used as the basis for the greatest possible flexibility. It is often not necessary to record high-precision measurement data. In this cases inexpensive development boards can be used that already include peripherals for direct connection to Arduinos or Raspberry Pi’s. Such development boards are often offered at very low cost and with short delivery times for many different measurement tasks. In this paper, the implementation of the requirements is discussed using a current project that deals with thermal energy recovery using 'smart materials' (shape memory alloys) as an example.
- Freie Schlagwörter (EN)
- Measurement Data Acquisition, Automation, Arduino, Controllino, Raspberry Pi, SPI, I2C, UART
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-897717
- Veröffentlichungsdatum Qucosa
- 14.02.2024
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0