Hybrid Thermal Error Compensation Combining Integrated Deformation Sensor and Regression Analysis Based Models for Complex Machine Tool Designs

Lecture notes in production engineering, pp. 28–40

Abstract

Abstract Thermal errors remain the dominant sources of positioning inaccuracies in machine tools. Various methods of reducing them have already been developed, ranging from cooling and air conditioning strategies, thermally optimized machine tool designs and component optimization to accurate model based thermal error estimation methods used for control-integrated compensation. Some reasons for the limited success of these strategies are the general complexity of the thermo-elastic and thermodynamic processes involved and the fact that many models’ effectiveness is dependent on the machine tool type, workpiece, climatic conditions, tool, CAM strategy, condition of the machine tool and other factors. Depending on these circumstances, all model-based estimation and compensation methods have specific strengths and weaknesses, which make them more suitable in some aspects and less suitable in others. A possible way of circumventing this is via the combination of several methods to hybrid compensation. The goal of this investigation is to develop an effective hybrid compensation strategy for the 5-axis machine tool DMU 80 eVo by combining measurement-based compensation using integrated deformation sensors (IDS) with characteristic diagram based compensation. The former is used for the compensation of thermal errors in the vertical column containing the larger assemblies of the x, y, and z-axis and the latter is used for the table, which contains two rotational axes. The IDS placement has been determined through expert knowledge and compared to placement, which minimizes the variance of the TCP displacement. The hybrid compensation is tested on the DMU 80 eVo.

Authors 9

1. Christian Naumann corresponding

   Fraunhofer Institute for Machine Tools and Forming Technology

   Affiliation as printed

   Fraunhofer IWU Chemnitz, 09126, Chemnitz, Germany

2. Andreas Naumann

   Heidelberg University · Chemnitz University of Technology

   Affiliation as printed

   Chemnitz University of Technology, 09126, Chemnitz, Germany

   Heidelberg University, 69117, Heidelberg, Germany

3. Nico Bertaggia Aachen Fraunhofer IPT Aachen

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer IPT Aachen, 52074, Aachen, Germany

4. Alexander Geist

   Fraunhofer Institute for Machine Tools and Forming Technology

   Affiliation as printed

   Fraunhofer IWU Chemnitz, 09126, Chemnitz, Germany

5. Janine Glänzel

   Fraunhofer Institute for Machine Tools and Forming Technology

   Affiliation as printed

   Fraunhofer IWU Chemnitz, 09126, Chemnitz, Germany

6. Roland Herzog

   Heidelberg University · Chemnitz University of Technology

   Affiliation as printed

   Chemnitz University of Technology, 09126, Chemnitz, Germany

   Heidelberg University, 69117, Heidelberg, Germany

7. Daniel Zontar Aachen Fraunhofer IPT Aachen

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer IPT Aachen, 52074, Aachen, Germany

8. Christian Brecher Aachen Fraunhofer IPT Aachen

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer IPT Aachen, 52074, Aachen, Germany

   WZL RWTH Aachen University, 52062, Aachen, Germany

9. Martin Dix

   Chemnitz University of Technology · Fraunhofer Institute for Machine Tools and Forming Technology

   Affiliation as printed

   Chemnitz University of Technology, 09126, Chemnitz, Germany

   Fraunhofer IWU Chemnitz, 09126, Chemnitz, Germany