Methods for Laser Burning Data Preprocessing: Parameterization of Pulses

Methods for Laser Burning Data Preprocessing: Parameterization of

Pulses

Ing. Jana HájkováDSS 8. 4. 2009

Methods for Laser Burning Data Preprocessing: Parameterization of Pulses 2/25

laser burning project• project description

– aim: to create an adjustable system for laser burning– simulation – part of the system

• cooperation– NTC – laser burning simulation, data explorer, measurement of

burned samples– Lintech – laser samples burning– KMA – automatic pulse detection algorithm development

• possible ways of research– data preprocessing (samples parameterization, pulse

detection)– burning simulation– simulation verification, samples comparison


laser burning principle 1/2• laser – electromagnetic radiation• after the radiation strikes the material:

– reflected, absorbed, transmitted– excitation of free electrons (metals) – vibrating in the structure of the material (insulators)– material heating – melting, boiling, vaporization,

plasma


laser burning principle 2/2• previous description –laser affects the material

surface continuously• more laser pulses burning

• parameters affecting the burning results:– used material and laser– material roughness– number and position of burned laser pulses– laser motion– laser angle of incidence according to the material surface


literature – laser burning• Anisimov, S. I., 1968: Vaporization of metal absorbing laser

radiation. Soviet Physics JETP, Vol. 27, pp. 182.

• Bäuerle, D., 2000: Laser Processing and Chemistry. Berlin Springer Verlag. ISBN: 978-3540668916.

• Bulgakova, N. M., Stoian, R., Rosenfeld, A., Hertel, I. V., Campbell E. E. B., 2007: Fast Electronic Transport and Coulomb Explosion in Materials Irradiated with Ultrashort Laser Pulses. Laser Ablation and its Applications, Springer Berlin / Heidelberg. ISBN: 978-0-387-30452-6.

• Dahotre, N. B., Harimkar, S. P. 2008. Laser Fabrication and Machining of Materials, Springer, New York, USA.

• Steen, W. M., 1991: Laser Material Processing. Springer-Verlag, New York Berlin Heidelberg. ISBN: 0-387-19670-6.


laser burning simulation• reasons for the simulation

– real burning of experiments with usage of optimal laser parameters• pulse burning:

– into one place– along a curve– in area

• simulation model design– based on real data


input data• samples burned and measured by the confocal

microscope• surface high map

• data set– each sample burned several times– pulses number sequence

(influence of the result on the number of laser pulses)


parametric description of a sample• burning affected area

• similar samples exploration– similarity of the pit– irregularity of the transition ring


pulse approximation 1/3• approximation of the pit cross-section with a

parabole

• from the top view – elliptical shape of the pulse

• elliptical paraboloid

02

20

2

20* z

byy

axx

hz


pulse approximation 2/3• transition ring cross-section approximation –

parabole

• revolving a parabola along the elliptic trajectory – the top half of the parabolic elliptic torus

velmaterialLeringHeightaxringWidth

ringHeighty R

22*

2

velmaterialLeringHeightpkz 21*

22

TdringWidth

ringHeightk


pulse approximation 3/3


roughness generation• real samples – measured in high detail –

roughness of the material

• heat affected area – irregularities– roughness on the pit bottom– irregular waves around the outer border of the transition ring– local defects on the transition ring


Pelin noise function 1/3• wide range of application in the computer graphics

(textures generating, …)• usable for areal noise generation• combination of noise and interpolation functions• 1D

– random points generation– Hermit interpolation H(t) = t 2*(3-2t) – final Perlin noise:

sum of several functions with different frequencies and amplitudes

– octaves (frequency of each function is twice as the frequency of the previous one )


Pelin noise function 2/3• 2D

– generation of random matrix– 2D interpolation– different number of summed actaves (2, 4, 6)

– same number of octaves, each following summed with ½ and ¼ amplitude than the previous one


Pelin noise function 3/3• amplitudes vector

– [0.25, 0.25, 1, 1, 0.5, 0.5], [0.5, 0.5, 1, 1, 3, 3], [2, 0, 0, 0, 0.5, 0.5]

• surface generation – 3D view– Perlin noise function– ^2


Perlin noise function application• pit bottom roughness

• local defect generation

– mask usage


waves modulation 1/2• waves representation – polyline

– whole or its part– segmentation (number or representing points)– difference of the points position from the ellipse– width and maximal height of the wave

• several waves application on the smooth surface


waves modulation 2/2• plain wave × full wave

– for the whole polyline– into the center – linear decrementation of the surface height

• full wave application on the smooth surface


sample surface generation results• 10, 50, 100 laser

pulses burned into steel

real samplegenerated sample


literature – sample parameterization• Ebert, S. E., Musgrave, F. K., Peachey, D., Perlin, K., Worley, S.,

2003: Texturing & Modelling, A Procedural Approach, Third edition. Elsevier Science. ISBN: 1-55760-848-6.

• Perlin, K. 1985. An Image Synthetizer, Proceedings of the 12th annual conference on Computer graphics and interactive techniques, ISBN: 0‑89791-166-0. ACM New York, USA, pp. 287-296.

• Perlin, K. 2002. Improving noise, In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, ISBN: 0730‑0301. ACM New York, USA, pp. 681-682.

• Polack, T., 2003: Focus on 3D Terrain Programming. Premier Press, USA. ISBN: 1-59200-028-2.

• Žára, J., Beneš B., Felkl P. 2005. Moderní počítačová grafika, ISBN: 80‑251-0454-0. Computer Press, Brno, Czech Republic.


similar samples comparison

50 pulses [μm]

A 144.50 129.50 6.46 2.17 116.00 100.00

B 142.50 131.50 6.24 2.22 234.50 125.00

C 138.00 130.50 5.91 4.17 114.00 104.00

D 131.00 115.50 5.35 3.19 121.50 109.00

E 124.00 123.00 7.06 3.19 120.50 111.00

average 136.00 126.00 6.20 2.99 141.30 109.80

MRE 5% 4% 7% 21% 26% 6%

100 pulses [μm] a b pit depth ring height horizontal ring

widthvertical ring width

A 155,00 170,50 8,71 2,72 112,00 85,00

B 153,50 174,00 9,89 1,96 122,00 75,00

C 160,00 178,00 8,83 1,87 106,50 93,00

D 192,50 152,00 8,42 1,82 67,50 144,00

E 177,00 177,50 9,42 3,45 89,50 106,00

average 167,60 170,40 9,05 2,36 99,50 100,60

MRE 8% 4% 5% 24% 17% 19%


trend of parameters in dependence on the number of burned laser pulses

a b h ring height

horiz. ring width

vert. ring width

10 85.38 82.50 3.45 2.30 198.25 171.00

20 79.00 93.40 3.36 2.41 235.10 175.60

30 88.30 86.10 3.27 2.06 244.40 209.60

40 100.50 99.83 3.86 2.50 177.83 149.67

50 130.40 119.00 5.43 3.20 116.00 134.00

60 126.20 135.20 6.28 2.75 132.60 103.00

70 148.13 144.13 8.59 2.81 147.25 92.25

80 159.00 149.40 8.50 1.79 119.50 87.40

90 162.70 163.80 7.49 2.23 95.90 85.00

100 166.80 165.00 9.13 2.37 99.20 88.80


0

1

2

3

4

5

6

7

8

9

10

10 20 30 40 50 60 70 80 90 100

pit depth

ring height

0

20

40

60

80

100

120

140

160

180

10 20 30 40 50 60 70 80 90 100

a

b

0

50

100

150

200

250

300

10 20 30 40 50 60 70 80 90 100

ring horizontal width

ring vertical width


future research• sample parameterization automation• methods for automatic pulse detection• burning simulation• methods for samples comparison• system verification


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Methods for Laser Burning Data Preprocessing: Parameterization of Pulses

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