Cyber-Physical Test Environment for the Identification of Interacting Wear Effects in Feed Axes
1
wbk - Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT), Germany
Submission date: 2023-01-31
Final revision date: 2023-03-15
Acceptance date: 2023-03-15
Online publication date: 2023-03-16
Publication date: 2023-04-12
Corresponding author
Alexander Bott
wbk - Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
wbk - Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
Journal of Machine Engineering 2023;23(1):123-132
KEYWORDS
TOPICS
ABSTRACT
For a comprehensive optimization and control of production processes, cyber-physical systems are necessary to include machines' time-dependent properties. These wear effects in machine tools, especially the feed axes, can significantly influence the process quality and are a steady research focus. However, the interaction of wear effects between different feed axes has received little attention. Especially models that represent the combined wear influence of different interacting feed axes on the control parameters and machine dynamics hold great potential. To close this knowledge gap, this paper proposes a cyber-physical test environment to identify the interaction of wear effects in feed axes. The relevant boundary conditions of different feed axes in machine tools and their systematic interaction are presented for this test environment. A physical test setup is derived through these conditions, and a virtual model is created analogous to this. This holistic approach represents the physical and virtual interaction between different components.
REFERENCES (29)
1.
DAVIS J., EDGAR T., PORTER J., BERNADEN J., SARLI M., 2012, Smart Manufacturing, Manufacturing Intelligence and Demand-Dynamic Performance, Computers & Chemical Engineering, 47, 145–156.
2.
LUO W., HU T., ZHANG C., WEI Y., 2019, Digital Twin for CNC Machine Tool: Modeling and Using Strategy, Journal of Ambient Intelligence and Humanized Computing, 10/3, 1129–1140.
3.
BRECHER C., WECK M., 2019, Werkzeugmaschinen Fertigungssysteme 1, Springer Vieweg, Berlin, Heidelberg.
4.
XU X.W., NEWMAN S.T., 2006, Making CNC Machine Tools More Open, Interoperable and Intelligent–A Review of the Technologies, Computers in Industry, 57/2, 141–152.
5.
FLEISCHER J., BROOS A., SCHOPP M., WIESER J., HENNRICH H., 2009, Lifecycle-Oriented Component Selection for Machine Tools Based on Multibody Simulation and Component Life Prediction, CIRP Journal of Manufacturing Science and Technology, 1/3, 179–184.
6.
SPATH D., ROSUM J., HABERKERN A., WEULE H., 1995, Kinematics, Frictional Characteristics and Wear Reduction by PVD Coating on Ball Screw Drives, CIRP Annals, 44/1, 349–352.
7.
MIURA T., MATSUBARA A., KONO D., OTAKA K., HOSHIDE K., 2017, Design of High-Precision Ball Screw Based On Small-Ball Concept, Precision Engineering, 47, 452–458.
8.
CZICHOS H., HABIG K.-H., 2015, Tribologie-Handbuch, Tribometrie, Tribomaterialien, Tribotechnik, Springer Vieweg, Wiesbaden.
9.
ZHAO J., LIN M., SONG X., GUO Q., 2020, Analysis of the Precision Sustainability of the Preload Double-Nut Ball Screw with Consideration of the Raceway Wear, Proceedings of the Institution of Mechanical Engineers, Part J. Journal of Engineering Tribology, 234/9, 1530–1546.
10.
VEITH M., ZIMMERMANN A., HILLENBRAND J., FLEISCHER J., 2020, Detektion des Vorspannungsverlusts in Kugelgewindetrieben/Detection of Preload Loss in Ball Screw Drives – Optimization of machine tool maintenance with the Guard Plus system, wt Werkstattstechnik online, 110/07-08, 485–490.
11.
SCHLAGENHAUF T., SCHEURENBRAND T., HOFMANN D., KRASNIKOW O., 2022, Analysis of the Visually Detectable Wear Progress on Ball Screws, CIRP Journal of Manufacturing Science and Technology, 40, 2023, 1–9, https://doi.org/10.1016/j.cirp....
12.
SCHOPP M., 2009, Sensorbasierte Zustandsdiagnose und -prognose von Kugelgewindetrieben, Karlsruhe, Univ., Diss., Shaker, Aachen.
13.
TSAI P.,C., CHENG C.C., HWANG Y.C., 2014, Ball Screw Preload Loss Detection Using Ball Pass Frequency, Mechanical Systems and Signal Processing, 48/1–2, 77–91.
14.
ZHOU C.-G., ZHOU H.-X., FENG H.-T., 2020, Experimental Analysis of the Wear Coefficient of Double-Nut Ball Screws, Wear, 446–447, 203201, https://doi.org/10.1016/j.wear....
15.
KIM S.K., CHO D.W., 1997, Real-Time Estimation of Temperature Distribution in a Ball-Screw System, International Journal of Machine Tools and Manufacture, 37/4, 451–464.
16.
CHANG Y.-C., WANG C.-C., JIAN W.-D., CHANG C.-C., FENG G.-H., LEE H.-C., 2016, Wireless Sensors for Intelligent Ball Screws Monitoring, IEEE Topical Conference on Wireless Sensors and Sensor Networks, 24–27, Austin, Texas, USA, IEEE, Piscataway, NJ, 44–47.
17.
NGUYEN T.L., RO S.-K., PARK J.-K., 2019, Study of Ball Screw System Preload Monitoring During Operation Based on the Motor Current and Screw-Nut Vibration, Mechanical Systems and Signal Processing, 131, 18–32.
18.
ZHOU C.-G., GU Y.-C., NIE C.-H., OU Y., FENG H.-T., 2022, Detecting Preload Degradation of a Ball Screw Feed System Using High-Frequency Reconstruction and Support Vector Machine, Journal of Vibration and Control, https://doi.org/10.1177/107754....
19.
LEE J., BAGHERI B., KAO H.-A., 2015, A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems, Manufacturing Letters, 3, 18–23.
20.
DEMETGUL M., YICHENG Z., MINJIE G., HILLENBRAND J., FLEISCHER J., 2022, Motor Current Based Misalignment Diagnosis on Linear Axes with Short- Time Fourier Transform (STFT), Procedia CIRP, 106, 239–243.
21.
DEMETGUL M., GU M., HILLENBRAND J., ZHAO Y., GONNHEIMER P,. and FLEISCHER J., 2022, Misalignment Detection on Linear Feed Axis with FFT and Statistical Analysis using Motor Current, Journal of Machine Engineering, 22/2, 31–42, https://doi.org/10.36897/jme/1....
22.
QIANG C., BAOBAO Q., ZHIFENG L., CAIXIA Z., DEYI X., 2019, An Accuracy Degradation Analysis of Ball Screw Mechanism Considering Time-Varying Motion and Loading Working Conditions, Mechanism and Machine Theory, 134, 1–23.
23.
JUNKER T., BUCHT A., NAVARRO Y., DE SOSA I., PAGEL K., DROSSEL W., 2014, Design of a Ball Screw Drive Wear Compensation Using Shape Memory Alloy Actuators, Proceedings of the 14th Mechatronics Forum International Conference.
24.
SHAOWEI Z., GUOFU D., SHENGFENG Q., JIANG L., LI Z., KAIYIN Y., 2012, Integrated Geometric Error Modeling, Identification and Compensation of CNC Machine Tools, International Journal of Machine Tools and Manufacture, 52/1, 24–29.
25.
LEI W.T., HSU Y.Y., 2003, Accuracy Enhancement of Five-Axis CNC Machines Through Real-Time Error Compensation, International Journal of Machine Tools and Manufacture, 43/9, 871–877.
26.
KVRGIC V., DIMIC Z., CVIJANOVIC V., VIDAKOVIC J., KABLAR N., 2014, A Control Algorithm for Improving the Accuracy of Five-Axis Machine Tools, International Journal of Production Research, 52/10, https://doi.org/10.1080/002075....
27.
LASPAS T., THEISSEN N., ARCHENTI A., 2020, Novel Methodology for the Measurement and Identification for Quasi-Static Stiffness of Five-Axis Machine Tools, Precision Engineering, 65, 164–170.
28.
FLEISCHER J., SPOHRER A., LEBERLE U., DOSCH S., 2015, Adaptive and Adequate Lubrication for Highest Component-Lifetimes in Feed Drive Axes with Ball Screws, Procedia CIRP, 9, 335–340.
29.
HANSJOSTEN M., BOTT A., PUCHTA A., GONNHEIMER P., FLEISCHER J., 2023, Model-Based Diagnosis of Feed Axes with Contactless Current Sensing, Production at the Leading Edge of Technology, WGP 2022, Lecture Notes in Production Engineering, Springer Cham.
| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
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