tool condition monitoring - tcm.pdf · École polytechnique montréal, politechnika warszawska...

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Prof. Krzysztof Jemielniak

Tool Condition Monitoring

École Polytechnique Montréal, Politechnika Warszawska

Outline

• TCM systems objectives & structure

• Process variables used in TCM

• Strategies of commercial TCM systems

• New strategy of tool wear monitoring

• Catastrophic tool breakage detection

• Concluding remarks

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Tool life estimation

2) small batch production,

direct operator attendance

Indirect methods

3) Large scale productionTime, number of parts

freq

uen

cy

t

ool w

ear

time, No of parts

tool life

4) Automatic, unmanned production

Indirect methods

1) Laboratory

direct methods


Methods used for tool wear estimation

Direct methods:(measurement of wear criteria)

Optical methods

Touch probes

Indirect methods:(changes in process variables caused by tool wear)

Acoustic Emission

Cutting forces and forcerelated measures (torque, motor current, true power)

Vibration and noise

3


Definition of TCM

Tool (and Process) Condition Monitoringrefers to data gathered by sensors that is used to determine the status and performance about:

• Machine tool

• Cutting tool

• The machining process


Tasks of TCM systems

Tool condition monitoring:

tool wear estimation (detection of the tool life end )

detection of catastrophic tool failure (CTF)

Detection of extensive vibration

Detection of collisions

Others (e.g. diagnostics of chip shape, detection of BUE, burrs, missing tool of workpiece, etc).

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Objectives of TCM

It allows you to:• Protect machines • Determine cutting tool status • Improve efficiency • Decrease the cycle time • Improve quality • Protect tooling and setups • Increase reliability • Optimize the process • Reduce rework • Lower scrap


Tool Condition Monitoring System Configuration

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Structure of Tool & Process Condition Monitoring System

process variables

Cutting zone

sign

als

sensorsData Acquisition & Signal processing:filters, statistics

FFT, RMS,...

sign

alre

pres

enta

tion STRATEGY

Proces model,

knowledge

diagnosis,command

ACTION !


Process variables used in commercial Tool & Process Condition Monitoring Systems

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Influence of Tool Wear on Cutting Forces in Turning


Tool condition monitoring in drilling operations, based on signal amplitude

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Axial forces and torque for sharp and dull drills

new tools

new tools

feed

for

ce F

f(N

)


Influence of CTF on power spectrum in milling

time (s)

cutt

ing

forc

e (*

0.2

kN

)

tool breakage

frequency (Hz)

frequency of edge passing

frequency of edge passing

revs

revs

nor

mal

i zed

pow

ern

o rm

ali z

ed p

ower

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Cutting Forces During CTF (Chipping) in Turning

chipping

chippings


Cutting Forces During CTF (Breakage) in Turning

chipping

breakage

breakage

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Tool wear and AERMS parameters

workpiece 45,

tool: SNUN 120408 NT35;

vc= 240 m/min, ap= 2.4 mm, f = 0.3 mm/obr;

tool wear (mm)burst rate (1/s)

burst width (%)

threshold (V)

threshold (V)


Development of AE until drill breaks

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Tool Life Monitoring of Small HSS Drills Using Vibration Signal


Strategy of TCM Systems

processvariables

Cutting zone

sign

als

sensorsData Acquisition & Signal processing:filters, statistics

FFT, RMS,...

sign

alre

pres

enta

tion

STRATEGY

Proces model,

knowledge

diagnosis,command

ACTION !

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Nordman


Prometec

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Artis

wear alarm (area exceeds wear limit).

area signal learned=100%,

larger area signal (e.g. through worn tools)

learnt area (bar diagram),

pre-alarm limitwear limit

pre-alarm (area exceeds wear limit)


Montronix T100

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Brankamp


Experiences from Brankamp Pilot Installation in Raufoss

• Installed on mechanically controlled 6-spindle automatic lathe

• 4 piezo-electric sensors:– Drilling tool on position 1– Drilling tool on position 2– Bar cut-off tool in position 6– Bar feed stopper

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Experiences from Brankamp Pilot Installation in Raufoss

R A U F O S S M A T E R I A L T E K N O L O G Iside 11

Experiences

+ No multiple tool breakage since installation+ Improved quality output, especially on

randomly occurring faults- Difficult to find correct monitoring limits: Trial

and error -approach requires skilled operators and continuity in process and operators

- Complex system, requires high level of training: problems at sick leave, vacation etc.


Existing TCM systems

• TCM systems are based on phenomena correlated with tool wear (cutting force, AE, vibration)• measured signal feature (SF) depends on other process

parameters• relationship tool wear vs. measured feature is very complex • it has a statistical nature

• Reliable tool wear evaluation based on one signal feature is impossible• most commercial and experimental systems apply "one

process – one SF" strategies• basic assumption: the feature is a monotonic function of

the tool wear

• This can be the reason of still not satisfactory performance of the systems applied in industry

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Solution?

• Combination of different signal features • statistical methods, • auto-regressive modeling, • pattern recognition, expert systems • neural network - most often used

• Usually single neural network• several SFs are fed into the network inputs,• tool wear estimation is the network output.

• In some works, hierarchical tool wear monitoring strategies were proposed

• advantages of such approach will be presented here


Questions

Does operator have to find and enter limit values, points of measurements etc?

Why the operator have to know anything about the signals values?

300-250∆T = ----------- =....

400-250

He can calculate used up part of tool life! 0,33

NO!

???

What can he do with this knowledge?

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Tool Condition Indicator

What is interesting for machine tool operator?

used up part of the tool life:

∆T = t / Twhere t – cutting time as performed so far

T – tool life

In laboratories: tool wear measures (VBB, KT)

crater on rake face

wear land of flank facenotch

wear

notch wear

not used in factory floor


Basics of TCM Learning

SF1

SF

The first tool life

1st cycle.: No of cuts, min and max of the signals

n= 1

After each cycle: store the measure

value SFOp

SF2SF3

2 3 ...

. . .

After the end of 1st tool life transform array SFOp into SF∆T using:

n∆T= ---

N

SFN

N

Sequential tool life

SF1

SF

After each part: store SFOpand evaluate ∆T using SF∆T

SF2SF3

. . .

n= 1 2 3 ...

After the end of tool life transform array SFOp into SF∆T,

learn (gather experience)

SFN

N

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Transformation SFOp into SF∆T

1. Smoothing SFop to eliminate random changes of SF: SFOp SFOpFlt



2. Assignment ∆T to each operation number (∆T =n/N) and transforming SFOpFltinto 20 elements array: SFOpFlt SF∆Tcurrent

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3. Taking into account previous experience:

SF∆T = 0.25 SF∆Tcurrent + 0.75 SF∆Tprev


SF∆T

∆T

Evaluation of Used Up Part of Tool Life

∆T

SF

n

Search out in array SF∆T value closest to SF obtained during machining last part

SF∆T

∆T

SF

n

The search starts from previous result: if recent value is lower, ∆T does not changes

∆T

Search only 6 elements of the array (30%T) reduces influence of accidental high values

SF∆T

∆T

∆T

SF

n

30%

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SF∆T

∆T

Evaluation of Used Up Part of Tool Life

SF

∆T

30%

Evaluation cannot be lower then 0.7 ∆T resulting from previous experience

SF∆T

∆T

∆T=70*n/NB

SF

n

Limited search of the SF∆T array enables, at least to some extent, to utilize the signal features which are not monotonic versus used up portion of the tool life


Machine Tool & Sensors

Turning Center Venus 450

AE sensor

Cutting force sensor

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Workpiece & Tool

Workpiece: steel C 45 bars, 160 mm machined down to 85 mm

ap=1,5 and ap = 2 mm

f = 0.1 mm/rev

vc = 150 m/min


Results of Experiments

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Results of Experiments

• 88 operations

• 8 worn out tools

• 1st tool used for system learning

• 7 tools used for estimation of tool wear monitoring accuracy


Results – Signal Feature Value vs. Operation Number

The average value of the feed force versus number of operation in three selected tool lives

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Results – Transformation SFOp into SF∆T

After the 1st tool life:

1. transformation of SFOp into SFOpFlt

2. transformation of SFOpFlt into SF∆T

After the 2nd tool life:

1. transformation of SFOp into SFOpFlt

2. transformation of SFOpFlt and SF∆Tprevinto SF∆T


Results – Evaluation of Used Up Part of Tool Life

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Signal Features

maximum another

average 2smedian

2s

varianceaverage

Altogether 98 signal features obtained from 4 signals – Ff, Fp, Fc and AERMS


Feature Selection

2nd degree polynomial approximation

Fc,MaxFfMed2s

FfMed2sFc,Max

Root Mean Square Error (RMSE), threshold = 0.05

0,046113 0,189998

RMSE=n

TTn

iii∑

=

∆−∆1

2)(

i – number of operationn – number of operations in tool wear

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Feature Selection

0,082725Variance(Fc (j))FcVar

0,077072Variance(AE(l))AEVar2s

0,070857RMS(Ff(j))FfRMS

0,062425Median(AE(l))AEMed2s

0,062076Mod(Ff(l))FfMod2s

0,061438Max(Ff(l))Ffmax2s

0,058544Min(Ff(j))Ffmin

0,055442Sum(Ff(j))/1000Ffsred

0,053809Max(Ff(k))-Suma(Ff(l))/1000Ffmaxpocz-sred

0,047488Median(Ff(j))FfMed

0,047267Sum(Ff(l))/1000Ffsred2s

0,046814RMS(Ff(l))FfRMS2s

0,046113Median(Ff(l))FfMed2s

0,044602Min(AE(l))AEmin2s

0,044247Median(AE(j))AEMed

0,030035Moment 3 degree(Ff(j))FfMom3

0,025272Moment 4 degree (Ff(j))FfMom4

0,021929Standard DeviationFf(j))FfStDev

0,020465Variance (Ff(j))FfVar

RMSE formulaFeature

RMSE<0,05


FfRMS2s

FfSred2s FfMed

Elimination of Similar Features

RMSE between features

0,009750Criterion: RMSE < 0.1

0,003096

0,002382

FfMed2s

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min. of AERMS from the 2nd sec.variance of Ff

Feature Integration

FfMed2s

FfVar

AEMed

?

Neural networks

median of Ff from the 2nd sec. median of AERMS

AEMin2s


Feature Integration

Artificial Neural Network Feed Forward Back Propagation (FFBP)

• 10 neurons in hidden layer (tested 4 to 20 neurons)

• Learning rate 0,14 (tested for 0,02 to 0,3 with step 0,02)

• Momentum rate 0,65 (tested for 0,05 to 0,9 with step 0,05)

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Feature Integration by Neural Networks -Results

FfMed2s FfVar

Untypical course of SF

dominates the network’s

output and can not be

compensated by typical ones

∆TNN


Feature Integration by Neural Networks –reasons of failure

Number of learning cases is not big enough

The system should be trained (learnt) using the data from the first tool life only Number of inputs is

dependent on number of learning cases

Number of features must be limited

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Hierarchical Algorithm

1. Estimation of ∆T from all signal features separately

2. Integration of estimations– neural network

– averaging without results outside 3σ



1. Estimation of ∆T based on single feature

FfMed2s

FfVar

AEMed

AEMin2s

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NN

2. Integration of estimations

Average


Tool Wear Estimation – Conclusions

1. Assumption about monotonic, increasing character of SF(∆T) function, excludes many useful features. Taking full advantage of all SFs correlated with the tool condition can significantly improve TCM systems reliability.

2. The difficulties related to reversing of non-motonic SF(∆T) functions can be overcome by transforming them into array and limited range search of SF value closest to the measured one.

3. The signal feature integration minimizes the diagnosis uncertainty, reducing the randomness in one SF and providing more reliable tool condition estimation.

4. Decomposition of the multi SF tool wear estimation into hierarchical algorithms has several advantages over the single step approach:

• many more SFs can be used,

• non-monotonic SF(∆T) functions can be used

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Cutting Force Change Pattern Accompanying Two Types of CTF


CTF Detection Based on Limit Value

assumed

actual

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CTF Detection Based on Pattern Recognition


Strategy of CTF Detection Developed in TH Aachen

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Strategy of CTF Detection Developed in WUT


Evaluation of the WUT CTF Detector Performance – Example 1

Tool breakage followed by the chipping of the cutting edge

C45, SNUN S30S

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Evaluation of the WUT CTF Detector Performance – Example 2

Cutting force and CTF detector reactions during stepwise change of the depth of

cut and the cutting edge chipping

ZL25M, SPUN H10S


Dynamic Limit Strategy for CTF Detection in Milling

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Conclusions – problems in TCM

• TCM systems are based on phenomena correlated with tool wear (cutting force, AE, vibration)• basic assumption: the feature is a monotonic function of

the tool wear• relationship tool wear vs. measured feature is very complex • it has a statistical nature

• Reliable tool wear evaluation based on one signal feature is impossible• most commercial and experimental systems apply "one

process – one SF" strategies• this can be the reason of still not satisfactory performance

of the systems applied in industry

• Detection of catastrophic tool failure based on constant limit strategies (most often used) can detect only a major tool breakage, usually far too late


Conclusions – possible solutions

• Reliable tool wear monitoring can be achieved by combination of different signal features • neural network - most often used

• number of features must be limited, thus NN seem to be not effective enough

• Decomposition of the multi SF tool wear estimation into hierarchical algorithms has several advantages over the single step approach: • many more SFs can be used, • non-monotonic SF(DT) functions can be used

• Detection of catastrophic tool failure is possible only using dynamic limit strategies

tool condition monitoring - tcm.pdf · École polytechnique montréal, politechnika warszawska...

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