Diagnostics of machine tool linear axes via separation of geometric error sources
Manufacturers need automated, efficient, and robust methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Recently, a method was developed to use data from an inertial measurement unit (IMU) to measure changes in geometric error motions. A linear axis testbed, established for verification and validation purposes, revealed that the IMU-based method was capable of measuring translational and angular deviations with acceptable test uncertainty ratios. In this study, a rail of the linear axis testbed was mechanically degraded to simulate spalling, a common degradation mechanism that can occur during machine tool operations. The rail was degraded in discrete steps from its nominal state (no degradation) to its final state (a failure state of the rail), and IMU and laser-based reference data was collected at each test stage. The contribution of geometric errors from the rail-based degradation were then separated with a technique that utilizes the various data for each run. Diagnostic metrics can then be defined for use with the IMU to facilitate industrial applications by informing the user of the magnitude and location of wear and any violations of performance tolerances.
How to Cite
wear, degradation, Machine tools, Error
InvenSense Incorporated (2016). MPU-6050 six-axis (gyro + accelerometer) MEMS MotionTracking™ device: https://www.invensense.com/products/motiontracking/6-axis/mpu-6050/
Khan, A. W. & Chen, W. (2009). Calibration of CNC milling machine by direct method. 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications (p. 716010), November 16-19, 2008, Beijing, China. doi: 10.1117/12.807066
Li, Y., Wang, X., Lin, J., & Shi, S. (2014). A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring. Sensors, vol. 14(2), pp. 2071-2088.
Sato, R. & Nagaoka, K. (2011). Motion trajectory measurement of nc machine tools using accelerometers. International Journal of Automation Technology, vol. 5(3), pp. 387-394.
Sato, R., Nagaoka, K., & Sato, T., "Machine motion trajectory measuring device, numerically controlled machine tool, and machine motion trajectory measuring method," USA Patent US9144869 B2, Sep. 29, 2015.
Smith, K. S. & Hocken, R. J., "Dynamic metrology methods and systems," USA Patent US8401691 B2, Mar. 19, 2013.
Teti, R., Jemielniak, K., O’Donnell, G., & Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRP Annals - Manufacturing Technology, vol. 59(2), pp. 717-739. doi: 10.1016/j.cirp.2010.05.010
Uhlmann, E., Geisert, C., & Hohwieler, E. (2008). Monitoring of slowly progressing deterioration of computer numerical control machine axes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 222(10), pp. 1213-1219.
Vogl, G. W., Weiss, B. A., & Donmez, M. A., "A sensorbased method for diagnostics of machine tool linear axes," presented at the Annual Conference of the Prognostics and Health Management Society 2015, Coronado, CA, 2015.
Vogl, G. W., Donmez, M. A., & Archenti, A. (2016). Diagnostics for geometric performance of machine tool linear axes. CIRP Annals - Manufacturing Technology, vol. 65(1), pp. 377-380.
Zhou, Y., Mei, X., Zhang, Y., Jiang, G., & Sun, N. (2009). Current-based feed axis condition monitoring and fault diagnosis. 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 (pp. 1191-1195), May 25-27, 2009, Xi'an, China. doi: 10.1109/ICIEA.2009.5138390
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