Handling Ambient Temperature Changes in Correlative Thermal Error Compensation
1
Automation and Monitoring, Fraunhofer IWU Chemnitz, Germany
2
Machine Tools, Fraunhofer IWU Chemnitz, Germany
3
IWP, Production Systems and Processes, Chemnitz University of Technology, Germany
Submission date: 2023-07-25
Final revision date: 2023-11-15
Acceptance date: 2023-11-17
Online publication date: 2023-11-20
Publication date: 2023-12-14
Corresponding author
Christian Naumann
Automation and Monitoring, Fraunhofer IWU Chemnitz, Reichenhainer Str. 88, 09126, Chemnitz, Germany
Automation and Monitoring, Fraunhofer IWU Chemnitz, Reichenhainer Str. 88, 09126, Chemnitz, Germany
Journal of Machine Engineering 2023;23(4):43-63
KEYWORDS
TOPICS
ABSTRACT
Thermal errors are one of the lead causes for positioning inaccuracies in modern machine tools. These errors are caused by various internal and external heat sources and sinks which shape the machine tool’s temperature field and thus its deformation. Model based thermal error prediction and compensation is one way to reduce these inaccuracies. A new composite correlative model for the compensation of both internal and external thermal effects is presented. The composite model consists a submodel for slow long and medium-term ambient changes, one for short-term ambient changes and one for all internal thermal influences. A number of model assumptions is made to allow for this separation of thermal effects. The model was trained using a large number of FE simulations and validated online in a five-axis machine tool with measurements in a climate chamber. Despite the limitations, the compensation model achieved good predictions of the thermal error for both normal ambient conditions (21°C) and extreme ambient conditions (35°C).
REFERENCES (24)
1.
BRYAN J., 1990, International Status of Thermal Error Research, CIRP Annals, 39/2, 645–656.
2.
SCHÄFER W.J., 1993, Steuerungstechnische Korrektur thermoelastischer Verformungen an Werkzeugmaschinen, PhD thesis, RWTH Aachen.
3.
JUNGNICKEL G., GROSSMANN K., 2006, Korrektur thermisch bedingter Verformungen an Werkzeug-maschinen mit strukturbasiertem Zustandsmodell, Proc. 11th Dresdner Werkzeugmaschinen-Fachseminar, 124–135.
4.
CHEN J.S., YUAN J., NI J., 1996, Thermal Error Modelling for Real-Time Error Compensation, International Journal of Advanced Manufacturing Technology, 12, 266–275.
5.
TSENG P.-C., 1997, A Real-Time Thermal Inaccuracy Compensation Method on a Machining Centre, International Journal of Advanced Manufacturing Technology, 13, 182–190.
6.
BRECHER C., HIRSCH P., WECK M., 2004, Compensation of Thermo-Elastic Machine Tool Deformation Based on Control Internal Data, CIRP Annals - Manufacturing Technology, 53/1, 299–304.
7.
MARES M., HOREJS O., HORNYCH J., KOHUT P., 2011, Compensation of Machine Tool Angular Thermal Errors Using Controlled Internal Heat Sources, Journal of Machine Engineering, 11/4, 78–90.
8.
MARES M., HOREJS O., 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....
9.
DELBRESSINE F.L.M. et al, 2005, Modelling Thermomechanical Behaviour of Multiaxis Machine Tools, Precision Engineering, 30/1, 47–53.
10.
MAYR J., et al., 2008, Simulation and Prediction of the Thermally Induced Deformations on Machine Tools Caused by Moving Linear Axis Using the FDEM Simulation Approach, Proc. 23rd ASPE Annual Meeting, 168–171.
11.
ESS M., 2012, Simulation and Compensation of Thermal Errors of Machine Tools, PhD thesis, Zurich.
12.
GLÄNZEL J., IHLENFELDT S., NAUMANN C., PUTZ M., 2018, Efficient Quantification of Free and Forced Convection via the Decoupling of Thermo-Mechanical and Thermo-Fluidic Simulations of Machine Tools, Journal of Machine Engineering, 18/2, 41-53, https://doi.org/10.5604/01.300....
13.
THIEM X., RUDOLPH H., KRAHN R., IHLENFELDT S., FETZER C., MÜLLER J., 2023, Adaptive Thermal Model for Structure Model Based Correction, S. Ihlenfeldt (Ed.): ICTIMT 2023, Springer, LNPE, 67–82, https://doi.org/10.1007/978-3-....
14.
YANG H., NI J., 2005, Dynamic Neural Network Modeling for Nonlinear, Nonstationary Machine Tool Thermally Induced Error, International Journal of Machine Tools and Manufacture, 45/4–5, 455–465.
15.
NAUMANN C., PRIBER U., 2012, Modellierung des Thermo-Elastischen Verhaltens von Werkzeugmaschinen mittels Hochdimensionaler Kennfelder, Proc. Workshop Computational Intelligence, 22, Dortmund, 365–383.
16.
BLASER P., PAVLIČEK F., MORI K., MAYR J., WEIKERT S., WEGENER K., 2017, Adaptive Learning Control for Thermal Error Compensation of 5-axis machine tools, Journal of Manufacturing Systems, 44/2, 302–309, https://doi.org/10.1016/j.jmsy....
17.
MAYR J., BLASER P., RYSER A., HERNANDEZ-BECERRO P., 2018, An Adaptive Self-Learning Compensation Approach for Thermal Errors on 5-axis Machine Tools Handling an Arbitrary Set of Sample Rates, CIRP Annals, 67, 551–554, https://doi.org/10.1016/j.cirp....
18.
LANG S., ZIMMERMANN N., MAYR J., WEGENER K., BAMBACH M., 2023, Thermal Error Compensation Models Utilizing the Power Consumption of Machine Tools, S. Ihlenfeldt (Ed.): ICTIMT 2023, Springer, LNPE, 41–53, https://doi.org/10.1007/978-3-....
19.
NGOC H.V., MAYER J.R.R., BITAR-NEHME E., 2023, Deep Learning to Directly Predict Compensation Values of Thermally Induced Volumetric Errors, Machines, 11, 496, https://doi.org/10.3390/machin....
20.
LIU J., MA C., GUI H., WANG S., 2021, Thermally-Induced Error Compensation of Spindle System Based on Long Short Term Memory Neural Networks. Applied Soft Computing, 102, 107094, https://doi.org/10.1016/j.asoc....
21.
BERTAGGIA N., TZANETOS F., ZONTAR D., BRECHER C., 2022, Investigation of Thermally Induced TCP-Displacement Under Load of the Machine axes in Different Areas, Procedia CIRP 107, 600-604, https://doi.org/10.1016/j.proc....
22.
GEIST A., NAUMANN C., GLÄNZEL J., PUTZ M., 2023, Methods for Determining Thermal Errors in Machine Tools by Thermo-Elastic Simulation in Connection with Thermal Measurement in a Climate Chamber, MM Science Journal, June 2023, https://doi.org/10.17973/MMSJ.....
24.
NAUMANN C., NAUMANN A., BERTAGGIA N., GEIST A., GLÄNZEL J., HERZOG R., ZONTAR D., BRECHER C., DIX M., 2023, Hybrid Thermal Error Compensation Combining Integrated Deformation Sensor and Regression Analysis Based Models for Complex Machine Tool Designs, S. Ihlenfeldt (Ed.): ICTIMT 2023, Springer LNPE, 28–40, https://doi.org/10.1007/978-3-....
| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
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