CNC Machine Tool Focused Edge Computing in Manufacturing

Tonejca, Lea; Mayer, Christoph; Trautner, Thomas Fabian; Mauthner, Gernot; Bleicher, Friedrich

doi:10.1016/j.procir.2024.08.309

Datensatz Zitierlink:

http://hdl.handle.net/20.500.12708/209524

Titel:

CNC Machine Tool Focused Edge Computing in Manufacturing

Zitat:

Tonejca, L., Mayer, C., Trautner, T. F., Mauthner, G., & Bleicher, F. (2024). CNC Machine Tool Focused Edge Computing in Manufacturing. In 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘23) (pp. 111–116). Elsevier. https://doi.org/10.1016/j.procir.2024.08.309

CatalogPlus:

AC17417371

Verlags-DOI:

10.1016/j.procir.2024.08.309

Publikationstyp:

Konferenzbeitrag - Full-Paper Contribution

Sprache:

Englisch

Autor_innen:

Tonejca, Lea
Mayer, Christoph
Trautner, Thomas Fabian
Mauthner, Gernot
Bleicher, Friedrich

Organisationseinheit:

E311-01-3 - Forschungsgruppe Steuerungstechnik und integrierte Systeme

Erschienen in:

17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘23)

Band:

124

Datum (veröffentlicht):

2024

Veranstaltungsname:

CIRP ICME '23 Conference: 17th CIRP International Conference on Intelligent Computation in Manufacturing Engineering

Veranstaltungszeitraum:

12-Jul-2023 - 14-Jul-2023

Veranstaltungsort:

Ischia, Italien

Umfang:

Verlag:

Elsevier

Peer Reviewed:

Keywords:

digital twin; dockerisation; edge computing; smart manufacturing

Abstract:

This paper presents a method for standardized integration of external sensors and actuators into CNC machine tool systems utilizing edge computing technology. Different communication protocols and various means for triggering external devices at a chosen machining operation were studied and evaluated using a sensory tool holder. The benefits of the presented architecture are the independence of the host operating system, easy customization, lightweight architecture, and standard protocols. Focusing on the demands for modern edge devices led to containerized modules with customizable interfaces. During manufacturing processes, the system was evaluated in terms of performance and reliability.

Projekttitel:

Development of Self-Learning NC-Programming System for Data-Driven Surface Prediction and Adaptive Cutting Parameter Optimization: FO999891268 (FFG - Österr. Forschungsförderungs- gesellschaft mbH)

Forschungsschwerpunkte:

Digital Transformation in Manufacturing: 70%
Information Systems Engineering: 30%

Wissenschaftszweig:

2030 - Maschinenbau: 70%
2050 - Werkstofftechnik: 15%
2020 - Elektrotechnik, Elektronik, Informationstechnik: 15%

Lizenz:

CC BY-NC-ND 4.0