countering thermal behaviour of machine tools tre… · (fea approach) thermal modal analysis...
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Countering Thermal behavior of Machine Tools
Prof. N. Ramesh Babu
V Balaraman Institute Chair ProfessorHead, Department of Mechanical Engineering
Indian Institute of Technology Madras
Secretary & HeadAdvanced Manufacturing Technology
Development CentreIITM Research park
Why to counter thermal behaviour?
Machining accuracy over time[Taniguchi]
Various Levels of precision[Taniguchi]
Ultra-high precision (0.05 – 0.0005 µm)High energy beam machining
High precision (1 – 0.05 µm)Free abrasive machining
Precision (5 – 1 µm)Grinding, CNC based precision turning and
milling machines
Normal (100 – 5 µm)Lathes, milling machines, Hydraulic and NC
machines
Enhancing the repeatability and accuracy of machine tools led to Ultra-high precision machine tools
Ob
ject
ive
2Ref: N Taniguchi, Current Status in, and Future Trends of, Ultraprecision Machining and Ultrafine Materials Processing, CIRP annals (32/2), 1983
Prof. N. Ramesh Babu, IIT-Madras
Different Types of Errors in Machine Tool
Errors in a
machine tool
Dynamic
Static
KinematicDeadweight
ThermalGeometric
Quasi-static
Vibration
relatedThermal
gradients
Geometric, Kinematic and Thermal
gradient Errors contribute to more than
90% of inaccuracies in machine tools
Time and ambient
dependent
Time and temperature dependent
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Prof. N. Ramesh Babu, IIT-Madras 4
Sources of Machine Tool Thermal Errors [Slocum]
Ref: Alexander Slocum, Errors in Precision Machines, 2000
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How to counter thermal errors?Thermal error
Avoidance Reduction
Compensation
Indirect compensation
Direct compensation
Temp raise control
Parts of Machine Tool (Grinding Machine)
Avoidance and Temperature Raise Control are achieved by changing/altering the machine tool – this has been demonstrated in one of the projects at IITM - NGPG
Measurement of drift and temperature at axes / drive units
Sensors like capacitive, inductive etc. used to make instrumented drive spindles and drive axes
Real time measurement of temperature
Compensation strategy based on mathematical models like ANN, fuzzy logic driven by temperature data
Compensation of TCP requires development of kinematic model of machine tool
Development of geometric-thermal model
Updating the model with measured data at varying ambient conditions
Compensation strategy based on developed model and temperature measurement at strategic locations on machine tool
Structure
Guideways and Drives
Spindle
Next Generation Precision Grinder (NGPG)
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Next Generation Precision Grinder (NGPG)
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Countering thermal errors in NGPG
• Thermal mapping of the machine tool is done using athermal imaging camera.
• The thermal maps of wheel head spindle zone, wheelhead motor and carriage axes drive and ballscrewsystem are studied to understand the influence oftemperature rise in the functioning of theseelements.
• The machine tool was maintained in a controlledambient temperature of about 22oC (±1 oC)
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Thermal imaging camera
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Thermal drift of wheel spindle
Existing grinder Machine with motorized spindle
The thermal drift of wheelhead spindle due to the rise in bearing temperature is measured using a non-contact capacitance sensor positioned against a rotating spherical target
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Thermal growth along grinding axis The drift in positioning of grinding infeed axis (X-
axis) was measured using laser interferometer.
The positional drift in the grinding zone (X 120mm) and in home position (X 500 mm) was measured at periodic interval for a total duration of 8 hours.
Test conditions - Cold start (no warm-up cycle) ; Room temperature: 23°C +/- 1°C
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Thermal stability of wheel spindle
Drift(µm)
Existing Grinder
NGPG
X 19 6
Z 26 18
The thermal stabilization of spindle takes about 45 – 60 minutes in NGPG, while in the existing
grinder, stabilization was observed after 90 – 100 minutes.
Direct drive motor with cooling arrangement for the spindle system influences the quicker
stabilization of wheel spindle thermal deviations.
Existing grinder NGPG
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Thermal stability of linear axis
0
2
4
6
8
0 30 60 90 120 150 180
X a
xis
Gro
wth
(m
icro
n)
Time(mins)
8
6
4
2
0
0 60 120 180 240 300 360 420 480
X a
xis
gro
wth
(m
icro
n)
Time (min)
Significant growth of carriage axis (X axis) influenced the size deviation on ground part
Growth of X-axis in Existing Grinder did not stabilize over a period of 480 min, while the growth of axis isquite small in NGPG
Pre-tensioning of the ballscrew system is found to be effective in controlling the overall growth of the axis
Existing grinder NGPG
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Output on finished component
Time period
Achievable
tolerance
Existing
GrinderNGPG
Cold trial IT 7 IT 5
After 2 Hours IT 6 IT 3
After 3 Hours IT 5 IT 4
After 4 Hours IT 5 IT 3
After 5 Hours IT 4 IT 3
Grinding trials conducted to study the
dimensional deviation with both machines
After cold trials, IT3 tolerance grade (gauge
tolerance) was achieved on NGPG, while in
existing grinder, the stabilization could not be
realized.
X-bar chart shows the stabilization of process
deviation in NGPG, while the process is highly
unstable with respect to existing grinder.
NGPG could achieve dimensional stability on
ground components without IPG and the
process is quite stable for longer period.
Industry Partners
Ace Designers,
Bengaluru
Jyoti CNC, Rajkot Micromatic Grinding
Technologies, BengaluruChennai Metco,
ChennaiInterface Design
Associates, Mumbai
MTAB, Chennai
Our workspace
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• Located in IITM Research Park – Block B
• Over 9000 sq. ft.
• Creating dedicated facilities
• High performance computational work
• Research facility with test beds
• Prototype creation
Core Capabilities of AMTDC
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Core Capabilities of
AMTDC
Kinematic Design and
Analysis
Volumetric Error
Modelling and Analysis
Static and Dynamic Analysis
Auxiliary systems for
machine tools
System view of machine
tool
IoT & Data Analytics
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Thermal Error Compensation at AMTDC
• Thermal Errors usually observed in Indian machines without compensation are in the range of 30µm to 50µm
• Thermal error in Benchmark Machine (Okuma Japan) - 5µm - 8µm including process scatter
• Some machines cannot be structurally modified due to structural stability and commercial reasons
• The objective was to reduce the diameter deviation to 5µm using compensation without modifying the machine structure
Snapshot from Okuma’s Thermo Friendly Concept Machines
Ref: https://www.okuma.co.jp/english/usersvoice/pdfs/vol2.pdf
Vantage 800LM – CNC lathe Machine from Ace Designers
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Schematic of direct compensation
Measurement of dimensional error on job turned @ varying ambient
conditions (Thermal chamber)
Kinematic model Vantage lathe
Mathematical model To find the residual error
coefficients
Compensation strategy for TCP control
Geometric and kinematic error measurement
@ STP
Model updating
ControllerAxes position control
Temperature measurement @ key locations of lathe
XZ
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Schematic of indirect compensation (FEA Approach)
Thermal modal analysisCompensation strategy
for TCP control
Temperature measurement@ Varying ambient cond.
ControllerAxes position control
Temperature measurement
Thermocouple / RTD sensor
FE based thermo-mechanical model
Spindle drift measurement @ varying ambient cond.
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Schematic of indirect compensation (Machine Learning Approach)
4. Mathematical modelLinear Regression, ANN, Fuzzy
logic
3. Diametrical Deviation measurement
@ varying ambient cond.
5. Compensation strategy
2. Temperature measurement@ Varying ambient cond.
6. ControllerAxes position control
Thermocouple / RTD sensor
1. Determination of Location
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Result of Compensation Algorithm Developed
35µm to 50µm 20µm 12µm …… 5µm
Phase I - ModelsAug 2018
Phase II - ModelsNov 2018
Machine outputMar 2017
with further fine tuning
Timeline
-20
-10
0
10
20
30
40
50
0 100 200 300 400 500 600
Dia
met
er D
evia
tio
n µ
m
Time in mins
Diameter variation after compensation
Diameter Variation
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-10
0
10
20
30
40
50
0 100 200 300 400 500
Dia
met
er V
aria
tio
mn
(µ
m)
Time in mins
Diameter Variation before compensation
Diameter Variation
75% reduction in diameter deviation
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Diameter deviation from cutting trials
TCP Drift & X-axis growth without coolant
Spindle running alone
Axis movement alone
Varying ambient alone
Total ErrorPositioning and Kinematic Error
Machining Error Thermal Error
Geometric Induced error
(Static error & Machine dependent)
(Dynamic error & Material dependent)
(Dynamic error & Machine dependent)
= + +
-20
-10
0
10
20
30
40
50
0 100 200 300 400 500
Dia
met
er V
aria
tio
mn
(µ
m)
Time in mins
Diameter Variation
Spindle drift measurement setup Axis Growth measurement setup Thermal Chamber setup
Experimental Measurement of Thermal Error
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Approach for Compensation using Machine Learning
Pre-Processing
Regression
Experimentation Data Collection Modelling
SVM
ANN
Ensembles
Machine Learning
Techniques
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Approach for using FEAFEA model developed in ANSYS
Planning for experimentation
(fixing the experimental trials to
be done)
Aids for Prediction Modelling
(Intermittent Stoppages and Lunch
break behaviour)
Extreme variation in Ambient Condition
(conditions-experiments cannot be
done)
Validated model can be used for further
generation of data (TCP/Cutting Trial) instead of
experimental data
Prof. N. Ramesh Babu, IIT-Madras 25
Summary
Machine Learning Techniques
Thermal Chamber
Deviation Measurement
Temperature Measurement –at specific points
Temperature Measurement –over an area
Clustering
Data Smoothering
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