An Investigation of the Relationship Between Encoder Difference and Thermo-Elastic Machine Tool Deformation
1
Chair of Machine Tools, Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Germany
Submission date: 2023-03-31
Final revision date: 2023-06-21
Acceptance date: 2023-06-22
Online publication date: 2023-06-26
Publication date: 2023-09-30
Corresponding author
Mathias Dehn
Chair of Machine Tools, Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Germany
Chair of Machine Tools, Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Germany
Journal of Machine Engineering 2023;23(3):26-37
KEYWORDS
TOPICS
ABSTRACT
New approaches, using machine learning to model the thermo-elastic machine tool error, often rely on machine internal data, like axis speed or axis position as input data, which have a delayed relation to the thermo-elastic error. Since there is no direct relation to the thermo-elastic error, this can lead to an increased computation inaccuracy of the model or the need for expensive sensor equipment for additional input data. The encoder difference is easy to obtain and has a direct relationship with the thermo-elastic error and therefore has a high potential to improve the accuracy thermo-elastic error models. This paper first investigates causes of the encoder difference and its relationship with the thermos-elastic error. Afterward, the artificial neural network is presented, which uses the encoder difference to compute the thermo-elastic error. To conclude, the potential of the encoder difference as an input of the model is evaluated.
REFERENCES (21)
1.
HACKSTEINER M., DUER F., AYATOLLAHI I., BLEICHER F., 2017, Automatic Assessment of Machine Tool Energy Efficiency and Productivity, Procedia CIRP, 62, 317–22.
2.
GAO W., IBARAKI S., DONMEZ M.A., KONO D., MAYER J., CHEN Y.-L., SZIPKA K., ARCHENTI A., LINARES J.-M., SUZUKI N., 2023, Machine Tool Calibration: Measurement, Modeling, and Compensation of Machine Tool Errors, International Journal of Machine Tools and Manufacture, 187, https://doi.org/10.1016/j.ijma....
3.
DONMEZ M.A., HAHN M.H., SOONS J.A., 2007, A Novel Cooling System to Reduce Thermally-Induced Errors of Machine Tools, CIRP Annals – Manufacturing Technology, 56/1, 521–524, https://doi.org/10.1016/j.cirp. 2007.05.124.
4.
HOREJS O., MARES M., FIALA S., HAVLIK L., STRITESKY P., 2020, Effects of Cooling Systems on the Thermal Behaviour of Machine Tools and Thermal Error Models, Journal of Machine Engineering, 20/4, 5–27, https://doi.org/10.36897/jme/1....
5.
BRECHER C., NEUS S., DEHN M., EFFICIENT FE., 2020, Efficient FE-Modelling of the Transient Thermo-Elastic Machine Behaviour of 5-Axes Machine Tools, Euspen, Thermal Issues, Northampton, 148–149.
6.
BRECHER C., FEY M., WENNEMER M., 2016, Volumetric Measurement of the Transient Thermo-Elastic Machine Tool Behavior, Prod. Eng. Res. Devel., 10/3, 345–350.
7.
NAUMANN C., GLÄNZEL J., DIX M., IHLENFELDT S., KLIMANT P., 2022, Optimization of Characteristic Diagram Based Thermal Error Compensation via Load Case Dependent Model Updates, Journal of Machine Engineering, 22/2, 43–56, https://doi.org/10.36897/jme/1....
8.
NAUMANN C., GLÄNZEL J., PUTZ M., 2020, Comparison of Basis Functions for Thermal Error Compensation Based on Regression Analysis – a Simulation Based Case Study, Journal of Machine Engineering, 20/4, 28–40, https://doi.org/10.36897/jme/1....
9.
GUO Q., YANG J., WU H., 2010, Application of ACO-BPN to Thermal Error Modeling of NC Machine Tool, International Journal of Advanced Manufacturing Technology, 50/5–8, 667–675.
10.
LIU P.-L., DU Z.-C., LI H.-M., DENG M., FENG X.-B., YANG J.-G., 2021, Thermal Error Modeling Based on BiLSTM Deep Learning for CNC Machine Tool, Advances in Manufacturing, 9/2, 235–249.
11.
WENNEMER M., 2018, Methode zur messtechnischen Analyse und Charakterisierung volumetrischer thermo-elastischer Verlagerungen von Werkzeugmaschienen, Diss. Aachen: Apprimus.
12.
CZWARTOSZ R., JEDRZEJEWSKI J., 2022, Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review, Journal of Machine Engineering, 22/3, 59–77, https://doi.org/10.36897/jme/1....
13.
Dr. JOHANNES HEIDENHAIN GmbH, 2006, Accuracy of Feed Axes: Technical Information, Traunreuth.
14.
WANG H., LI F., CAI Y., LIU Y., YANG Y., 2020, Experimental and Theoretical Analysis of Ball Screw Under Thermal Effect, Tribology International, 152, 106503, https://doi.org/10.1016/j.trib....
15.
XI T., BENINCÁ I.M., KEHNE S., FEY M., BRECHER C., 2021, Tool Wear Monitoring in Roughing and Finishing Processes Based on Machine Internal Data, International Journal of Advanced Manufacturing Technology, 113/11–12, 3543–3554.
16.
BRECHER C., LEE TH., TZANETOS F., ZONTAR D., 2019, Hybrid Modeling of Thermo-Elastic Behavior of a Three-Axis Machining Center Using Integral Deformation Sensors, Procedia CIRP, 8,1301–1306, https://doi. org/10.1016/j.procir.2019.04.017.
17.
International Organization for Standardization, 2007, Test Code for Machine Tools: Determination of Thermal Effects; (230 Teil 3).
18.
MA C., ZHAO L., MEI X., SHI H., YANG J., 2017, Thermal Error Compensation of High-Speed Spindle System Based on a Modified BP Neutral Network, International Journal of Advanced Manufacturing Technology, 89/9–12, 3071–3085.
19.
CHEN Y., CHEN J., XU G., 2021, A Data-Driven Model for Thermal Error Prediction Considering Thermoelasticity with Gated Recurrent Unit Attention, Measurement, 184, 109891, https://doi.org/10.1016/j.meas....
20.
MIZE C.D., ZIEGERT J.C., 1999, Neural Network Thermal Error Compensation of a Machining Center, Journal of the International Societies for Precision Engineering and Nanotechnology.
21.
HOCHREITER S., SCHMIDHUBER J., 1997, Long short-term memory, Neural Computation, 9/8, 1735–1780, https://doi.org/10.1162/neco.1....
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| ISSN: | 1895-7595 |
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