countering thermal behaviour of machine tools tre… · (fea approach) thermal modal analysis...

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

3

Prof. N. Ramesh Babu, IIT-Madras 4

Sources of Machine Tool Thermal Errors [Slocum]

Ref: Alexander Slocum, Errors in Precision Machines, 2000


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)

6

Next Generation Precision Grinder (NGPG)

7

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)


Thermal imaging camera


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


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


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


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


Output on finished component

Time period

Achievable

tolerance

Existing

GrinderNGPG

Cold trial IT 7 IT 5

After 2 Hours IT 6 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

15

• 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

16

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


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


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


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.


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


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

-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 before compensation

Diameter Variation

75% reduction in diameter deviation


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


Approach for Compensation using Machine Learning

Pre-Processing

Regression

Experimentation Data Collection Modelling

SVM

ANN

Ensembles

Machine Learning

Techniques


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


Summary

Machine Learning Techniques

Thermal Chamber

Deviation Measurement

Temperature Measurement –at specific points

Temperature Measurement –over an area

Clustering

Data Smoothering

countering thermal behaviour of machine tools tre… · (fea approach) thermal modal analysis...

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