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Identification of Mechanistic Force Model Coefficients in Milling Using Sensor Fusion of Main Spindle and Feed Drive Signals

Procedia CIRP, vol. 134, pp. 343–348

Abstract

Mechanistic force models describe the relation between process forces and geometrical engagement of the tool and workpiece in cutting processes. The model parameters are dependent on the tool’s wear state, which results in constantly changing force model parameters during cutting. To account for this, previous studies developed a methodology to identify the force model parameters online using an ensemble Kalman filter (EnKF). The methodology relies on piezo-electric dynamometer-based force measurements, which have high accuracy but are also costly and not easily industrially applicable. Instead, this study uses current and voltage signals of the electrical motors. As these signals have less accuracy, both main spindle and feed drive axes are utilized and fused in an EnKF. The methodology is capable of identifying a mechanistic force model online with high accuracy and shows potential for force monitoring and control without dynamometer-based force measurement.

Authors 4

  1. RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

  2. RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

  3. RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

  4. Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

    Affiliation as printed

    Fraunhofer Institute for Production Technology (IPT), Steinbachstraße 17, 52074 Aachen, Germany

    Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

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