Seamless and modular architecture for autonomous machine tools

• Autorzy: Fleischer Jürgen , Puchta Alexander , Gönnheimer Philipp

Identyfikatory

DOI

10.36897/jme/141565

• Języki publikacji: EN

Abstrakty

EN

In machine tools, existing solutions for process monitoring and condition monitoring rely on additional sensors or the machine control system as data sources. For a higher level of autonomy, it becomes necessary to combine several data sources, which may be within or outside of the machine. Another requirement for autonomy is additional computing power, which may be hosted on edge devices or in the cloud. A seamless and modular architecture, where sensors are integrated in smart machine components or smart sensors, which are in turn connected to edge devices and cloud platforms, provides a good basis for the incremental realisation of autonomy in all phases of the machine life cycle.

Słowa kluczowe

EN

machine tool; cloud computing; Industry 4.0; edge computing

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2021
• Tom: Vol. 21, No. 3
• Strony: 40--46
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Fleischer Jürgen

• wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany

autor

Puchta Alexander

• wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany

autor

Gönnheimer Philipp

• wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany

Bibliografia

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• [3] SCHLAGENHAUF T., FEURING C.P., HILLENBRAND J., FLEISCHER J., 2019, Camera Based Ball Screw Spindle Defect Classification System, Production at the leading edge of technology, Springer Vieweg, Berlin, Heidelberg, 503–512.
• [4] ZHANG L., GAO H., et al., 2017, A Deep Learning-Based Recognition Method for Degradation Monitoring of Ball Screw with Multi-Sensor Data Fusion, Microelectronics Reliability, 75, 215–222.
• [5] ELFORJANI M., SHANBR S., 2017. Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning, IEEE Transactions on industrial electronics, 65/7, 5864–5871.
• [6] HILLENBRAND J., SPOHRER A., FLEISCHER J., 2018., Zustandsüberwachung bei Kugelgewindetrieben, wt Werkstatttechnik, online, 8, 493–498.
• [7] MÖHRING H.C., BERTRAM O., 2012, Integrated Autonomous Monitoring of Ball Screw Drives, CIRP Annals, 61/1, 355–358.
• [8] SPOHRER A., 2019, Steigerung der Ressourceneffizienz und Verfügbarkeit von Kugelgewindetrieben Durch Adaptive Schmierung, Shaker Verlag.
• [9] GÖNNHEIMER P., PUCHTA A., FLEISCHER J., 2020, Automated Identification of Parameters in Control Systems of Machine Tools, Congress of the German Academic Association for Production Technology, Springer, Berlin, Heidelberg, 568–577.
• [10] BARTON D., GÖNNHEIMER P., SCHADE F., EHRMANN C., BECKER J., FLEISCHER J., 2019, Modular Smart Controller for Industry 4.0 Functions in Machine Tools, Procedia CIRP, 81, 1331–1336.
• [11] BARTON D., FEDERHEN J., FLEISCHER J., 2021, Retrofittable Vibration-Based Monitoring of Milling Processes Using Wavelet Packet Transform, Procedia CIRP, 96, 353–358.