Vincenzo Piuri, Sicon/02, Houston, TX, USA 18-21 November 2002 LASER WELDING FOR AUTOMOTIVE...

Vincenzo Piuri, Sicon/02, Houston, TX, USA 18-21 November 2002

LASER WELDING FOR AUTOMOTIVE COMPONENTS

This research has been carried out in collaboration with

Fiat Research Centre, Turin, Italy


The Application

• The gear is built by joining two separated rings (a light syncronization gear and the principal gear)

• Welding is carried out with a CO2 laser

• Every product is tested using ultrasonic waves after welding for quality control

B

A

Y X

Z


The Application• We wish on-line monitoring for

– welding quality assessment

– welding process monitoring (control)

• Welding problems are related to:

– Penetration depth

– Misalignment of coupling in mounted samples

– Porosity

– Power decrement up to 10%

– Power lack up to 10 ms


The Application Requirements

• The error categories can be grouped in three classes: – Power Loss

– Mounting

– Porosity

• Requirements: – High monitoring performance

– Low computational load


Signal Pre-Processing

• Simple Processing– Amplitude Demodulation

– Low Pass Filtering

• Fast Processing– 15K samples


Feature Extraction

• Reference construction– a Cubic line has been

considered to interpolate the relevant interval of the weld watcher signal

• Processing– 1805697 Flops


Power Errors and Features

T

F• Power Loss Errors

– Short Duration of Welding Process

– Laser Power Fluctuation

• Features– T: Duration of Effective Laser Power

– F: Maximum Power Fluctuation

T

GOOD NO GOOD


Mount Errors and Features

• Mount Errors– Modulation in Weld

Watcher Signal

• Features– Parameters of the Cubic

line

– H-L: Cubic line Features

GOOD NO GOOD

H

L


Porosity Errors and Features

GOOD NO GOOD

A

D

A

D

• Porosity Errors– Variations wrt to the reference signal

• Features– A: Amplitude of the discrepancy

– D: Time duration of the discrepancy


Remarks

• Few samples are available to configure the solution

• Not all samples can be classified by the operator

• The distribution of samples for the different error typologies is unknown

Good No Good Not Classified TotalDepth Error 31 9 29 69Power Error 29 40 0 69Mount Error 47 8 14 69Porosity Error 275 10 60 345


The Proposed Algorithm

NO GOOD

Welding SignalAcquisition

LowPass Filtering

Polynomial Fitting

NO GOOD

-

GOODNO GOOD

GOOD

Start

Laser PenetrationFeatures Extraction

Laser PenetrationClassification

GOOD

MountingFeature Extraction Power

Classification

Porosity Feature Extraction

PorosityClassification

NO GOOD

MountingClassification

Power SignalAcquisition

LowPass Filtering

Laser PowerFeatures Extraction

GOOD

GOOD


Experimental ResultsClassifier Training Cross Validation Error of

best classifierAccuracy Interval Notes

KNN 40 samples(100%)

39 samples(~100%)

0 % ~ 0 –10 % K= 1Depth

FF-NN 28 samples(70%)

12 samples(30%)

0 % ~ 0 – 10 % Neurons= 2(Best over 100)


68 samples(~100%)

0 % ~ 0 – 8 % K= 1Power


21 samples(30%)



54 samples(~100%)

1.8 % ~ 0 – 10 % K= 1Mount


16 samples(30%)



214 samples(~100%)

0.35 % ~ 0 – 4 % K= 1Porosity


86 samples(30%)



SPARKS ANALYSIS FOR LASER CUTTING


TRUMPF, Ditzingen (Stuttgart), Germany


The Application

• Laser cutting of steel/stainless steel is a complex process

• It is expected that monitoring of the sparks dynamic associated with the cutting process can provide hints about– The internal nature of the cutting process

– Indications for subsequent process monitoring and control


The Application Requirements

• There are three cutting error categories:– Good

– No Good • Discontinuous cut

• Pearls of metal

– Ambiguous

• Requirements: – High Accuracy

– Low computational load


Composite System Partitioning

FEATURE EXTRACTION

SC

CLASSIFIER

PEARL

CONTROL

NoGood

Good

Ambiguous

Jet /no Jet

, ,

Cut speed, gas used,...

Composite System

No Jet


Features Extraction

beta sxbeta sx

gammagamma

beta dxbeta dx

AlphaAlpha

gammagamma

: inclination angle : opening angle of the main jet : opening angle of the whole jet


Finding the Jet Starting Point

100 200 300 400 500

100

200

300

400

0 100 2000

2

4

6x 10

650 100 150

100

200

300

400

500

600

100 200 300 400 500

100

200

300

400

100 200 300 400 500

100

200

300

400

0 100 2001

2

3

4

5x 10

750 100 150

100

200

300

400

500

600

100 200 300 400 500

100

200

300

400

Radon transform

Profile extraction

Direction of the main jet


Estimating the Angles

THE AND ANGLESTHE AND ANGLES

• Median filtering• Threshold binarization• Cumulate intensity in rows• Find left/right edges of the spark• Separated left/right linear regression passing trough the vertex


Pearl Identification

FeedForward Neural Network

(2 hidden units,1 output good/no good unit)

NEURAL

NETWORK


The Final SystemSLAPS-EU

Activity Status Report - 10 March 2000

Camera

Sensors

Sampler

Movie

Information fromthe field

N images + Information from the sensors

Classification Supervisor

Classification Data file

Index evaluationImage

Evaluation JET-P.

JET?

JET presence

Presence ofPearls of burr

NO

Image Classifier

YES Vertex evaluationalpha

beta

gamma

(x,y)

alpha

alpha

beta

gamma

Classificationof a singleimage

Metal typeMetal thicknessCut speedFocus positionGas typeGas pressureLaser powerNozzleCut distance

Final ClassifierN Classifications & JET-presence

JET presence

Final CutClassification

Classification system

A

B

B1

C

B2

B3


Performance

NotePerformance

Using human estimates over 121 imagesthe behavior of the angles-processing module fits suitably the sparks

Error < 3°processing

, ,

Using validation images30/30Pearls

84/84Classification good/no good/ambiguous


LASER SPOT WELDING FOR ELECTRONIC COMPONENTS


Philips CFT - Centre for Industrial Technology

Philips Centre for Industrial Technology


Electron Gun for Cathode Ray Tube

1 - Generation of free electrons by cathode2 - Beam shaping using ‘electric field lenses’3 - Acceleration of electrons

Hdeflection


Manual Classification

Top viewspot weld

Bottom viewspot weld OK

Bottom viewspot weld bad

Acceptable gap

Too large gap


CCD Camera

On-AxisLaser Reflection

Laser OutputMonitor

TemperatureSensor (2x)

Off-AxisReflection

Plume(not visible)

FiberInserts hereLaser power input

Fibre

On-axisreflection

Camera

Laser powermonitoring

X-Y scannermirrors

Off-axisreflection

SoundThermalemission

Plumeemission

Work piece


Feature Extraction

Spot welding of Grid 1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

-1.648 -0.848 -0.048 0.752 1.552 2.352 3.152 3.952 4.752 5.552 6.352 7.152

Time [ms]

Volts

Laser input power

On-axis reflection

Off-axis reflection

Plume emission

Temperature

cooling slope

turning point

time of firston-axis minimum

Process starting time

Meanpower level


Automatic Classifiers

Pros Cons

NeuralNetworks

High accuracy Requires propertraining (and re-training)

Difficult to refuseclassification

Estimation ofclassificationaccuracy only

K-NearestNeighborClassifier

No trainingrequired

Does not necessarilylead to classifications


Results from theNearest Neighbour Classifier

1NN 2NN

StripStrip (3)

Bracket

Bracket(15)

CuramikCuramik(18)

MAI (32)

MAI (18)

98.15 1.8597.67 2.33

75.92 24.08

75.44 24.56

86.04 13.96

78.65 21.35

77.37 22.63

84.86 15.14

95.74 3.24 1.0295.63 3.20 1.17

61.53 25.43 13.04

62.25 25.04 12.71

73.06 19.64 7.3075.01 17.14 7.85

64.22 23.73 12.05

63.70 24.75 11.55

O.K. O.K. N.O.K.N.O.K. ??

Vincenzo Piuri, Sicon/02, Houston, TX, USA 18-21 November 2002 LASER WELDING FOR AUTOMOTIVE...

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Transcript of Vincenzo Piuri, Sicon/02, Houston, TX, USA 18-21 November 2002 LASER WELDING FOR AUTOMOTIVE...