WISCONSINREACH Lab

Mike ZinnAssociate Professor, Department of Mechanical Engineering

High-Performance Process Control through Realtime Feedback and

EstimationAdvanced Manufacturing Die Casting

Initiative Workshop

High Performance Control

OTS Hardware/Software

Realtime Control and Communication

Rapid Prototyping Systems1 23

4

T1T3T2

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T4

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Process Physics and Modeling

Relevant control dynamics

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, …)

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

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

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

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

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

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

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

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

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

WISCONSINREACH Lab

Thank You