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Integration of virtual and on-line machining process control and monitoring using CNC drive measurements

University of British Columbia

The recent trend in manufacturing is to develop intelligent and self-adjusting machining systems to improve productivity without overloading the machine tool. This thesis presents a novel digital machining system: the use of virtual machining simulation to feed predicted process data to on-line monitoring and control system to improve its robustness. The process states (i.e. cutting forces, vibration, torque) are also extracted from CNC drive measurements to auto-tune the virtual model and control the process on-line. An on-line communication link between the CNC and external computer is developed where the virtual process model and on-line algorithms run in parallel with information exchange. Prior to the cutting operation, the machining process is simulated using a virtual machining system to calculate cutter-workpiece engagement and process states along tool-path. During the cutting operation, process forces are identified from feed drive motor current command measurements by compensating the corresponding friction, inertia of each drive and disturbance of structural dynamics through Kalman filters. The kinematics of the machine tool is solved to transform the individual compensated motor torque to the cutting forces acted on the tool without having to use external force sensors. The speed and load dependent structural dynamics of the spindle assembly are updated in a Kalman filter model by monitoring the vibrations at the spindle. Simulated machining states are accessed by the on-line machining process monitoring and control system as a virtual feedforward information to avoid false tool failure detection and transient force overshoots during adaptive control. The chatter vibrations are detected from the Fourier Spectrum of the spindle motor current measurements by compensating the structural dynamics of the drive train. The proposed algorithms are integrated to an on-line process monitoring and control system, and demonstrated on a five-axis CNC machining center. The thesis presents the first comprehensive virtual process model assisted machining process monitoring and control system in the literature to form the foundations of a comprehensive digital twin for machining systems. The prediction of process states from mainly CNC inherent data makes the system more industry friendly. The system has been designed to be reconfigurable to add new monitoring and control algorithms.

Text

2019-04-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/69387

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/