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Automated identification of cutting force coefficients and tool dynamics on CNC machines

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Title: Automated identification of cutting force coefficients and tool dynamics on CNC machines
Author: Dunwoody, Keith
Degree Master of Applied Science - MASc
Program Mechanical Engineering
Copyright Date: 2010
Publicly Available in cIRcle 2010-04-01
Abstract: The complexity and variation of parts are continuously increasing due to technologically oriented consumers. The objective of present manufacturing industry is to increase the quality while decreasing the machining costs. This thesis presents a smart machining strategy which allows the automated prediction of chatter-free cutting conditions using sensors integrated to Computer Numerical Controlled (CNC) machine tools. The prediction of vibration free spindle speeds and depth of cuts require to have material's cutting force coefficient and frequency response function (FRF) of the machine at its tool tip. The cutting force coefficients are estimated from the cutting force measurements collected through dynamometers in laboratory environment. The thesis presents an alternative identification of tangential cutting force coefficient from average spindle power signals which are readily available on machine tools. When tangential, radial and axial cutting force coefficients are needed, the forces need to be collected by piezoelectric sensors embedded to mechanical structures. The structural dynamics of sensor housings distort the force measurements at high spindle speeds. A Kalman filter is designed to compensate the structural modes of the sensor assembly when the spindle speed and its harmonics resonate one of the modes the measuring system. The FRF of the system is measured by a computer controlled impact modal test unit which is integrated to CNC. The impact head is instrumented with a piezo force sensor, and the vibrations are measured with a capacitive displacement sensor. The spring loaded impact head is released by a DC solenoid controlled by the computer. The impact force and resulting tool vibrations are recorded in real time, and the FRF is estimated automatically. The measured FRF and cutting force coefficient estimated from the spindle power are later used to predict the chatter free depth of cuts and spindle speeds. The machine integrated, smart machining system allows the operator to automatically select the chatter-free cutting conditions, leading to improved quality and productivity.
URI: http://hdl.handle.net/2429/23240
Scholarly Level: Graduate

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