Production Engineering

, Volume 11, Issue 2, pp 107–115 | Cite as

Model-based predictive force control in milling: determination of reference trajectory

  • Max Schwenzer
  • Oliver Adams
  • Fritz Klocke
  • Sebastian Stemmler
  • Dirk Abel
Machine Tool


Today, powerful process simulation tools allow an offline process planning and optimization of metal cutting processes. The quality of the optimization strongly depends on the model and its parameters. Real cutting processes are influenced by uncertainties such as tool wear status or material properties, which are both unknown. To overcome this limitation, sensors and process control systems are used. Model-based Predictive Control (MPC) was developed in the 1970s for the chemical process industry. This control method was found to be very suitable to control complex manufacturing processes such as milling processes. Using MPC in metal cutting processes allows considering technological boundary conditions explicitly. Adapting the feed velocity and thus the process force increases the productivity and process stability in milling. A core element of the MPC is the use of a reference trajectory representing the time-dependent set point value in the optimization procedure. The tool path information, however, is given position-based. Thus, calculating the reference trajectory is not trivial and strongly influences the control quality. This paper presents two methods for determining the reference trajectory. The first method is based on an adaptive signal filter. For the second method the MPC is extended to a two-layer MPC: the first layer calculates an optimal reference trajectory; the second layer controls the machine tool.


Model-based predictive control Reference generation Milling Force control Filter 



The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries”.


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Copyright information

© German Academic Society for Production Engineering (WGP) 2017

Authors and Affiliations

  1. 1.Laboratory for Machine Tools and Production Engineering (WZL)RWTH Aachen UniversityAachenGermany
  2. 2.Institute of Automatic Control (IRT)RWTH Aachen UniversityAachenGermany

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