Model predictive force control in milling based on an ensemble Kalman filter

Max Schwenzer, Sebastian Stemmler, Muzaffer Ay, Adrian Karl Rüppel (), Thomas Bergs and Dirk Abel
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Max Schwenzer: RWTH Aachen University
Sebastian Stemmler: RWTH Aachen University
Muzaffer Ay: RWTH Aachen University
Adrian Karl Rüppel: RWTH Aachen University
Thomas Bergs: RWTH Aachen University
Dirk Abel: RWTH Aachen University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 7, No 2, 1907-1919

Abstract: Abstract Process force determines productivity, quality, and safety in milling. Current approaches of process design often focus on a priori optimization. In order to enable online optimization, the establishment of active force controllers is required. Due to fast-changing engagement conditions of the tool in conjunction with the slower machine dynamics, classic control is not suited. A promising approach is the application of model predictive control (MPC) for force control, which is proposed in this contribution. The model predictive force controller (MPFC) explicitly takes into account a model to predict the immediate future. It consists of a model of the machine tool and a separate model of the process. The process model describes the relation between feed velocity of the tool, force, and geometric properties of the tool, such as the radial deviation, and of the tool/workpiece engagement. The feedback loop of the controller is closed by an online identification of the process model to account changes in the material properties or of the tool wear state. For this identification an ensemble Kalman filter (EnKF) is applied. The MPFC solves an optimization problem on the future behavior in each sampling step to determine the optimal controller output enabling high dynamic control. The proposed control system is validated experimentally and compared with a conventionally designed process with constant feed. It can be shown that the manufacturing time is reduced by 50%. The system enables a paradigm shift in the design of milling processes operating the manufacturing process at its technological limit.

Keywords: Model predictive control; Ensemble Kalman filter; Milling; Force control; Manufacturing control; Model identification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-022-01931-2

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