REACH Lab WISCONSIN...Feedback Control Spindle dynamics FSW Process Sensing Wireless comm. Feedback...
Transcript of REACH Lab WISCONSIN...Feedback Control Spindle dynamics FSW Process Sensing Wireless comm. Feedback...
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WISCONSINREACH Lab
Mike ZinnAssociate Professor, Department of Mechanical Engineering
High-Performance Process Control through Realtime Feedback and
EstimationAdvanced Manufacturing Die Casting
Initiative Workshop
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High Performance Control
OTS Hardware/Software
Realtime Control and Communication
Rapid Prototyping Systems1 23
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T1T3T2
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Process Physics and Modeling
Relevant control dynamics
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Case Study: Friction Stir Welding
FSW tool
Leading edgeRetreating sideTrailing edge
Reaction forcesTranslation
Advancingside
Pin
Rotation
Shoulder
Friction Stir Welding
Tooling:
Machines:
Solid state joining process Complex thermo-
mechanical process Many factors contribute to
weld quality (tool design, process parameters, …)
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Case Study: Friction Stir Welding
Process Variations Process Limitations
Complex process window (input parameters) Process variations / disturbances Process window – measured outputs
Feedback control can greatly reduce process sensitivity
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Temperature controlThermal Process Modeling
hall-effect sensor(rotation sensor)
FSW tool(with thermocouples)
Bluetooth(wirelessmodule)
signalconditioningelectronics
tollholdermm
Measurement / Estimation
Feedback Control
Spindle dynamics FSW Process Sensing Wireless comm.
Feedback control can greatly reduce process sensitivity Heat input critical to
weld quality – control through temperature Enabled through
modeling, real-time sensing and control
Physics based models
+ Experimental
data
Heat input Heat
distribution
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Temperature control
Process Control
Process Uncertainty / Disturbance
Feedback control can greatly reduce process sensitivity Heat input critical to
weld quality – control through temperature Enabled through
modeling, real-time sensing and control
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Robotic Friction Stir Welding
*rT s
*zF s
*tool s
*toolZ s
zF s
intT s
Feedback Control
Process Modeling
Control and communication
latencyDrive
dynamics
Coupled thermo-mechanical dynamics essential for control
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Robotic Friction Stir WeldingProcess Control
Decoupled thermo-mechanical control Feedback control can greatly reduce process sensitivity Control decoupling and feedback result in robust control
Temperature Plunge Force
Spindle Speed Plunge Depth
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Defect Estimation (and Control)Quality Control
(subsurface defects)Void Size
Estimation
Time [sec]
Measured Force
Void Distribution
Subsurface voids significantly reduce strength Difficult and expensive to inspect Recognize correlation between
measured forces and void volume
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Defect Estimation (and Control)
Frequency domain
(Complex Morlet Wavelet)
Measured Process Forces
Fx
Fy
time
Freq
uenc
y [H
z]
Magnitude
Magnitude
Measured Voids(CT scanned weld sample)
Volume extraction
Supervised Machine Learning
Discontinuity Model (NN)
Process physics suggest frequency dependency
tool
3x tool
time
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Defect Estimation (and Control)
Discontinuity Model
Measured
2 % error
Process physics model + signal processing Accurate void-
volume predictions
Void formation correlated with force, temperature of process Future: use of temperature / force control
to reduce / eliminate void formation
Frequency domain
(Complex Morlet Wavelet)
FxFr
eque
ncy
[Hz]
Magnitude
Magnitude
tool
3x tool
Fy
timetime
Process physics suggest frequency dependency
Measured Process Forces
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High Performance Control
OTS Hardware and Software
Realtime Control and Communication
Rapid Prototyping Systems1 23
4
T1T3T2
q1q2
T4
q4q5q3
Process Physics and Modeling
Process variation addressed through feedbackProcess monitoring addressed through estimationModern automation tools allow for wider adoption
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WISCONSINREACH Lab
Thank You