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
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Affiliation as printed
Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany
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Affiliation as printed
Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany
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Affiliation as printed
Manufacturing Technology Institute (MTI), RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany
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Thomas Bergs Aachen Fraunhofer Institute for Production Technology Manufacturing Technology Institute (MTI)
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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