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Robotic machining with embedded feedback
Integrated Metrology for Precision Manufacturing Conference22 - 23 January 2019, Knowledge Transfer CentreAdvanced Manufacturing Research Centre, Sheffield, UK
Presented by Patrick Keogh
Professor of Machine Systems
Department of Mechanical Engineering
University of Bath
Bath BA2 7AY
UK
MetMap 2019AMRC Sheffield, 22-23 January 2019
Presentation Overview
Research undertaken in the EPSRC Light Controlled Factory (LCF) and Future Advanced Metrology Hub projects
Robotic machining with embedded feedback
Achievements and future challenges
MetMap 2019, 22-23 January 2019
LCF/Hub Metrology Academic Links
Future Advanced Metrology Hub
£10M
University of Huddersfield
University of
Sheffield
Light Controlled Factory £2.4M
University of Bath
University of Loughborough
University College London
£0.5M £0.5M
£1.2M
£1.2M £1.2M
MetMap 2019, 22-23 January 2019
Metrology Options for Robotic Machining –considered for the LCF
MetMap 2019, 22-23 January 2019
Lasertrackernetwork- Bath
Fringe projection for object recognition
- Loughborough
- Professor Jonathan Huntley
Photogrammetric network- UCL- Professor Stuart Robson
LCF Demonstrator with System Integration for Robotic Machining
MetMap 2019, 22-23 January 2019
+ Active control of robotic machining
Robotic Machining Challenges
Machining with industrial robots:
Flexible operation - Low cost – Large working volume – Small installation footprint
However, under internal control only, robots have:
Poor absolute machining accuracy (up to 1 mm)
Relatively low stiffness
Low and high frequency regions for machining control
Low bandwidth
Joint backlash and other tribological uncertainties
Vibration modes that depend on pose
1 Hz 10 Hz 100 Hz 1000 Hz
Dynamics of laser tracker torobot feedback
Dynamics of inertialactuators
“Position” control “Vibration” control
MetMap 2019, 22-23 January 2019
LCF Demonstrator with System Integration for Robotic Machining
MetMap 2019, 22-23 January 2019
Laser Tracker
Robot
Photogrammetry
Carrier Plate
Fringe Projection
Common Coded Targets
RobotCalibration
Floor split bearings
CMMCalibration
Coordinate Transformation
Coordinate Frame
Real-time Multi-scale Control
RoboticSystem
Dynamics
RobotControl
Laser Tracker
+Machine ToolPath Demand
+-
AccelerometerTransducers
++
InducedMachine Tool
Vibration
InertialActuation
+++- ‘Clean’ Machine
Tool Path Profile
Low Bandwidth
High Bandwidth
• Assess by surface finish• Measured machining forces
MetMap 2019, 22-23 January 2019
Fringe projection and photogrammetry used to determine the accurate machine tool path demand to match up with a scanned artefact
Real-time Laser Tracker Position Compensation
Direct measurement of end effector position with laser tracker
250 Hz sampling control loop (for low bandwidth control)
Custom real-time control software
Windows 7 PC
KR-C4 ControllerKR-120 Robot
Faro
Ion
Tra
cker
Tracker Commands
Tracker measurements ~800Hz
Robot data 250Hz
Correction data 250Hz
Robot Servo Cable
Tracker 100 Mbps Ethernet
KUKA RSI Gigabit Ethernet
MetMap 2019, 22-23 January 2019
Renishaw Ballbar Tests
Ballbar linear uncertainty of 0.7 µm 0.3%
1 DOF dynamic measurements
Independent confirmation of performance
Tests at 300, 600 mm radii, different ‘feed’ rates
Joint reversal
MetMap 2019, 22-23 January 2019
Ballbar Test Results
With laser tracker control:
Reduction of RMS error from 74 µm to 16 µm
Small effect of feed rate 0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
No FB No FB No FB With FB With FB With FB
RM
S Er
ror
(mm
)
RMS Error comparison of different feed rates
1000 mm/min 500 mm/min 250 mm/min
MetMap 2019, 22-23 January 2019
Industrial Case Study 1 – Profile Machining
Industrial project assessing robotic machining with real-time laser tracker feedback
10 components machined
5 without feedback
5 with feedback
Profile errors greatly reduced with feedbackMetMap 2019, 22-23 January 2019
Industrial Case Study 2 - Block Drilling Trials
Position 1 Position 2
Position 3 Position 4
MetMap 2019, 22-23 January 2019
CMM Hole Probing - Exaggerated Hole Position (2x) and Radial Error (20x)
No Feedback With Laser Tracker Feedback
MetMap 2019, 22-23 January 2019
+ more consistent absolute hole position
Vibration Control of Robotic Machining
Robots are complex dynamic systems
Nonlinear parameters e.g. backlash
Vibration modes depend on pose
Excitation frequency and magnitude vary dramatically
Spindle speed
Cutting depth
Material
Active control is required
Ongoing as part of the Advanced Metrology Hub
MetMap 2019, 22-23 January 2019
Laser tracking for machine tool path control (A)
3 DoF xyz accelerometer measurement of dynamic spindle vibration (B)
3 inertial xyz actuators (C) to control machining forces up to 200 N
Load cell to measure dynamic machining forces (D)
AB
C-z
C-x
C-y
D
MetMap 2019, 22-23 January 2019
Vibration Control of Robotic Machining
Frequency response identification
AVC Eccentric Mass Results1
58
0 R
PM
26
.4H
z R
eso
nan
ce
90
00
RP
M1
49
Hz R
eso
nan
ce
MetMap 2019, 22-23 January 2019
LCF Demonstrator – without vibration control
Conclusions ➡ Future Challenges
Integration and metrology systems with robotic machining demonstrated
Revolute joint tribologicalinfluences are controllable to some extent though not exactly
High frequency vibration control of machining is achievable with additional actuation, e.g. inertial
➡ Automation of processes to
follow-on. Thermal variations require compensation for large volumes
➡ Nonlinear modelling
uncertainty, variability. Flexure joints may have potential to eliminate these
➡ Precision control depends on
robot pose, hence challenges for the complete robot operating volume