Correlation analysis in laser welding plasma

Optics Communications 251 (2005) 139–148

www.elsevier.com/locate/optcom

Correlation analysis in laser welding plasma

T. Sibillano, A. Ancona, V. Berardi *, P.M. Lugara

INFM-LIT3-Laboratorio Regionale di Ricerca, Formazione, Sviluppo e Trasferimento alle Imprese di Tecnologie Laser Innovative, Italy

Dipartimento Interuniversitario di Fisica-Universita e Politecnico di Bari via Orabona 4, I-70126 Bari, Italy

Received 11 October 2004; received in revised form 23 January 2005; accepted 21 February 2005

Abstract

During the process of high power laser welding the interaction between laser beam and workpiece produces a plasma

bunch, named plume which is ejected from the surface of the welded material. The efficiency of the welding process and

the quality of the welded seams are influenced by the plasma plume which plays a fundamental role in coupling the

incident radiation to the material. The aim of this work is to present an optical non-invasive spectroscopic approach

for the investigation of plasma plume dynamics. In particular, we present data on optical spectroscopy of the plume

produced during laser welding of aluminum–magnesium alloys of industrial interest. The dynamics of the plume during

laser welding is investigated by performing a correlation analysis of the optical spectra (covariance mapping technique).

� 2005 Elsevier B.V. All rights reserved.

PACS: 81.20Vj; 52.50Jm; 52.38.Mf

Keywords: Laser welding; Optical spectroscopy; Correlation analysis; Aluminum alloys

1. Introduction

Because of its advantages over other joining

techniques, laser welding is one of the most impor-

tant manufacturing processes particularly in theautomotive industry. Nevertheless laser-material

interaction is very complex and the correlation

0030-4018/$ - see front matter � 2005 Elsevier B.V. All rights reserv

doi:10.1016/j.optcom.2005.02.076

* Corresponding author. Tel.: +39 080 5442363; fax: +39 080

5442434.

E-mail address: [email protected] (V. Berardi).

between the various parameters characterizing

the process is still not fully understood.

Setting up a reliable procedure to obtain welds

of acceptable quality is often a challenging prob-

lem. Therefore there is a growing interest in devel-oping procedures for real-time monitoring of the

weld quality by investigating the plasma plume

produced in the laser-material interaction. It is

well known that chemical composition, spatial

and thermal characteristics of the plasma plume

are related to the welding conditions and keyhole

ed.

mailto:[email protected]

140 T. Sibillano et al. / Optics Communications 251 (2005) 139–148

shape. And in turn almost every change of a

welding process parameter has a certain, though

in some cases ambiguous, influence on the plasma

plume dynamics. Thus, studying the plume

dynamics turns out to be very useful to improveand monitor the weld quality. In fact plasma

dynamics can affect the energy transport from

the laser beam to the work-piece and modify the

amount and distribution of energy on the target

surface [1–3]. Several efforts have been devoted

to develop reliable, non-intrusive sensors [4,5].

Most of commercially available process monitor-

ing devices are simple threshold-type sensing sys-tems, detecting audible sound or infrared and

ultraviolet emission [6]. Using fuzzy logic or neural

networks recognition algorithms, early works on

photodiode based sensors identified several weld

states with peculiar optical and/or acoustic ampli-

tude patterns [7,8]. A different promising approach

consists of analysing the visible radiation emitted

by the plasma plume, and several works, basedon in-process plasma spectroscopy at high acquisi-

tion rate, found a correlation between some of the

spectral features, e.g., intensities of selected lines

acquired either integrating or spatially resolving

the plasma plume emission, and the formation

dynamics of weld defects like blow-holes [9–11].

In our previous works, we performed several

studies on the spectroscopy of plasma plume eitherby evaluating in real time the welding plasma elec-

tron temperature [12] or by analysing the oscilla-

tion frequencies of the integrated optical

radiation as a function of operating conditions

[13].

In this paper, we present preliminary results on

a new kind of spectroscopic approach for the

investigation of plasma plume characteristics bymeans of the covariance mapping technique

(CMT), previously successfully applied to the

study of time-of-flight (TOF) mass spectra ob-

tained from laser surface interaction in YBCO

samples [14,15]. We applied this technique to

optical spectra in order to understand the rela-

tionship between spectroscopic plasma parame-

ters and welds quality. In particular, CMTallows to identify correlation and anti-correlation

between lines belonging to different chemical spe-

cies, thus giving information on the dynamics of

the welding plasma under different operating

conditions.

2. Experimental

The welding tests were carried out by using a

CO2 laser (Rofin Sinar mod. DC025) with maxi-

mum output power of 2.5 kW in continuous wave

regime. The beam diameter is 25 mm and the

divergence is 0.5 mrad. The laser beam is then fo-

cused on the work-piece by a 200 mm focal length

water-cooled parabolic mirror. The laser source iscoupled to full-automated robotic cell (Ravasi LC

1000).

We investigated the AA6082 aluminum alloy

belonging to Al–Si–Mg group (composition in

mass percent: Al: 96; Si: 0.70–1.30; Mg: 0.6–1.2;

Mn: 0.40–1.00; Cu: 0.10; Zn: 0.20; Fe: 0.50; Ti:

0.10; Cr: 0.25; other: 0.15). To prevent possible

back reflection of the incident power on laser op-tics, the samples were positioned on a tilting table,

with an inclination angle of 10� with respect to the

beam axis.

The plasma optical emission was collected by a

quartz collimator of 6 mm focal length. The col-

lected light was transmitted to a PC interfaced

miniature spectrometer by an 8 lm core-diameter

optical fibre. The spectral range investigated was550–800 nm, with an optical resolution of

0.3 nm. A schematic of the experimental apparatus

is shown in Fig. 1.

The experimental condition relative to the ac-

quired spectra are: 2.5 and 2.0 kW incident

power, welding speed of 30 and 60 mm/s, beam

focus position 2 mm above surface of workpiece,

nitrogen shielding gas at a flow rate of 20 and60 Nl/min and nozzle stand-off distance of

6 mm. Optical spectra were acquired at a rate

of 30 frames per second, with an integration time

of 5 ms each. In this condition each spectrum cor-

responds to a weld length of 1 mm when operat-

ing at 30 mm/s and of 2 mm at 60 mm/s. For the

purpose of the covariance analysis, we computed

a number of N = 10 consecutive spectra eachtime, thus giving a spatial resolution on the weld-

ing bead of 1 or 2 cm depending on the travel

speed.

Fig. 1. Schematic of the experimental apparatus.

T. Sibillano et al. / Optics Communications 251 (2005) 139–148 141

3. Covariance mapping technique

The principle of covariance mapping technique

(CMT) is based on the assumption that the spec-

trum under investigation can be considered as a

sampling function for the signal coming from the

detector. Under this hypothesis, if xk(ki) is the sig-nal recorded at wavelength ki of the kth spectrum

the covariance matrix of the series of N spectra is

given by:

Cij ¼1

N

XNk¼1

xkðkiÞxkðkjÞ �1

N

XNk¼1

xkðkiÞ" #

� 1

N

XNk¼1

xkðkjÞ" #

: ð1Þ

The covariance matrix is clearly symmetric upon

exchange of i and j and in our analysis, we willuse the normalized form of the matrix, defined by:

nij ¼Cij

ðCii � CjjÞ1=2: ð2Þ

In this way, the values of nij lie in the range be-

tween �1 and 1, and the diagonal elementsnii = 1 (obviously, each chemical species is fully

correlated with itself). nij = 1 indicates correlated

pairs of species, whereas nij = �1 indicates anti-

correlated pairs of species; of course nij = 0, stands

for uncorrelated chemical species. In our analysis,

only the value of nij corresponding to a confidence

level above 95% have been plotted. This value

depends only on the number of spectra, N = 10,

used in the covariance matrix evaluation.It is worth noting that the number of spectra

necessary to obtain the desired confidence level is

not affected by the weakness of the lines. The main

feature of CMT resides in its reliability in extract-

ing the information in case of very weak lines, pro-

vided that the change in intensity can be

statistically correlated to the variation of the inten-

sity of some other line. In fact very strong spectrallines, whose intensity changes randomly (or re-

mains constant) during the weld run do not con-

tribute to the covariance signal, whereas very

weak lines whose intensity variation can be con-

nected to the intensity change of other lines do

contribute to the correlation signal because the

first term of Eq. (1) has a different value with re-

spect to the second term. The CMT is thus partic-ularly powerful to evidence the contribution of

weak spectral signals which carry information

but are characterized by a small value of the

signal/noise ratio.

4. Results and discussion

4.1. General remarks

Several spectral lines which can be attributed to

the main components of the aluminum alloy under

investigation are clearly visible in the two typical

600 650 700 750 8000

500

1000

1500

2000

Fe(I

) 74

7.64

Al(

II)

624.

34

Fe(I

) 60

2.40

Al(

III)

572

.28

Al(

III)

569

.66

Gas flow rate =60 Nl/minGas flow rate =20 Nl/min

Inte

nsity

(a.

u.)

Wavelength (nm)

Al(

I) 6

69.8

7

Al(

II)

683.

71A

l(II

) 69

2.03

Al(

II)

704.

21

Al(

II)

706.

21

Mg(

I) 7

65.6

6

O(I

) 78

9.25

O(I

) 79

2.35

Al(

II)

559.

33

Fig. 2. Optical spectra for two different values of gas flow rate. The roman number in brackets indicates the ionisation state of the

chemical species: (I) neutral species; (II) single ion; (III) double ion.

Fig. 3. (a) Positive correlation covariance map. (b) Negative

anti-correlation covariance map.


optical spectra shown in Fig. 2. We detected the

emission coming from neutral and ionised chemi-

cal species, and in particular it was evident the for-

mation of both neutral, single and double ionised

Al, namely Al(I), Al(II) and Al(III) peaks. It is

also remarkable the presence of small peaks attrib-uted to oxygen on the right side of the spectrum.

Following the details given in a previous para-

graph, we performed the covariance analysis on

the acquired optical spectra focusing our attention

on Al–O and Mg–Al correlation. We also

extended the Mg–Al correlation analysis as a func-

tion of the thermal input.

Two sample covariance maps showing the cor-

relation and anti-correlation are shown in Fig. 3.

The correlation (anti-correlation) degree is

shown as grey scale. To enhance the resolution

of the maps and obtain specific information on

the welding process, we restricted our analysis to

specific regions of the spectrum, as shown in

Fig. 4.

4.2. Al–O correlation

The covariance maps calculated for a defect free

welding process (P = 2.5 kW, v = 30 mm/s, gas

flow rate = 60 Nl/min) and a flaw weld resulting

from a lower shielding gas flow rate (P = 2.5 kW,

v = 30 mm/s, gas flow rate = 20 Nl/min) are shown

in Figs. 5(a) and (b), respectively.

As can be seen in the inset of Fig. 5(b), a thick

layer of oxides has been detected on the upper sur-

face of the defective joint, due to the lack of shield-

ing gas.

Fig. 4. Optical spectrum for covariance analysis. The shaded regions are used for correlation analysis.

Fig. 5. Covariance maps for Al–O correlation. (a) Defect free

welding. (b) Defective welding. The insertion on right side of (b)

is a photograph of the defective joint.


It is evident, label [1] in Fig. 5, that Al(I), Al(II)

and O(I) lines correlation coefficient is very high inthe case of flaw welds (Fig. 5(b)), whereas the cor-

relation coefficient value is very small in case of

defect free process (Fig. 5(a)). The situation de-

picted in Fig. 5 for a defective weld, where a very

strong correlation has been observed betweenAl(I), Al(II) and O(I) lines, is repeated in all the

welding tests performed under several combina-

tions for the values of the relevant process param-

eters when at least one of them is different from the

value giving defect free welds. This indicates that

the welding zone, in case of optimal experimental

condition, is effectively shielded from the oxygen

contamination from the surrounding atmosphere.The inefficient shielding, evidenced by the correla-

tion between Al and O lines is also confirmed by

the visual inspection of the welded joint and

X-ray photoemission spectroscopy analysis which

showed the presence of a high amount of Al2O3

compounds and other complex oxides in all

the defective welds. To check this assumption

we investigated the presence of eventual anti-correlation signals in case of spectra taken in the

regime of defect free welds.

In Fig. 6, we show the spectral region investi-

gated (top), together with the corresponding

covariance map (bottom). It is evident that a

remarkable anti-correlation is observed between

O(I) and Al(I), Al(II) lines in case of optimal pro-

cess parameters, label [2] on Fig. 6. A possibleexplanation is the formation of complex alumi-

num oxides, starting from Al ions coming from

the welding alloy, that subsequently dissociate

inside the plasma plume. This anti-correlation

Fig. 6. (Top) Optical spectrum for covariance analysis. The shaded regions are used for correlation analysis; (bottom) corresponding

Al–O anti-correlation map.


disappears and changes into correlation in case of

lower gas flow rate, indicating that the oxides

do not longer dissociate. This analysis is thus

consistent with the observed formation of

oxides, caused by the inefficient shielding of the

inert gas.

4.3. Al–Mg correlation

In the case of optimal operating conditions we

also checked the possible correlation between Al

and Mg emission lines. The analysis is shown in

Fig. 7, where a clear correlation is observed be-

tween the above mentioned species. We can thus

say that there is an increase (decrease) in Mg

emission which corresponds to an increase (de-

crease) in the Al emission. It can be argued that

part of Mg, as well as of Al, even in optimal

experimental condition enriches the plasma at

the expenses of the molten region. The loss of

alloying elements is of course not desirable to

prevent the mechanical features of the meltedzone to be different from the base metal. Unfor-

tunately, by this technique we can only give a

qualitative figure of such loss; a quantitative

analysis can be done only by using sophisticated

metallographic analysis which is beyond the

scope of the present communication.

Fig. 7. (Top) Optical spectrum for covariance analysis. The shaded regions are used for correlation analysis; (bottom) corresponding

Al–Mg correlation map.

Fig. 8. Optical spectrum for Al(II)–Mg(I)covariance analysis.



4.4. Thermal input analysis

Finally, we extended the analysis of the correla-

tion between Al and Mg as a function of heat in-

put by restricting to the correlation betweenAl(II) and Mg(I), see Fig. 8. We found that the

Fig. 9. Covariance maps as a function of heat input. (a) Shows an

(b) and (c) are positive correlation (CORRELATION) maps. Experim

(b) incident power 2.5 kW, speed 30 mm/s; (c) incident power 2.5 kW

correlation is almost absent for a low heat input

(Fig. 9(a)), increasing for increasing values of the

heat input.

Obviously the vaporisation rate of the alloying

elements increases as a function of the heat input.Due to the high vapor pressure and low boiling

negative correlation (ANTI-CORRELATION) map whereas

ental parameters are: (a) incident power 2.0 kW, speed 30 mm/s;

, speed 10 mm/s.


point, magnesium can be easily lost during laser

welding. Thus, for lower heat inputs, Mg and Al

species are anti-correlated because of the pro-

nounced vaporisation of magnesium with respect

to aluminum. As the heat input increases, thevaporisation rate increases and aluminum species

are also substantially emitted in the plasma, result-

ing in a correlation signal between Mg and Al.

The spectra used for covariance maps were fur-

ther analysed to calculate the plasma electron tem-

perature. In particular, we selected the Al(II) lines,

shown in the shaded region of Fig. 8, and for each

spectrum acquired during a single weld run weevaluated the plasma electron temperature by

using the Boltzmann plot method [12]. An increase

of the plasma electron temperature�s mean value

(from 9000 to 17000 K) as a function of growing

heat input was found, moreover a remarkable in-

crease of the temperature signal�s standard devia-

tion, ranging from 370 to 1340 K, was observed.

In a previous paper [12], we demonstrated thathigher values of the standard deviation of the

plasma temperature are significantly correlated to

perturbations of the melt pool which in turn re-

sults in flaw welds. This is a further experimental

evidence which corroborates our hypothesis that

an excessive heat input causes an increase of the

vaporisation rate of volatile elements, thus affect-

ing the stability of the melt pool and the qualityof the weld.

Thus, by means of this technique we are able to

have a qualitative control on the loss of volatile

alloying elements, such as the magnesium species,

that causes the degradation of the mechanical

properties of the welds. A fact worth noting is that

using CMT even relatively weak spectral lines, like

O(I) at 636.63 nm and Mg(I) at 765.66 nm, give arelevant information on the plasma plume dynam-

ics. Such lines would have been in fact neglected if

we performed a simple analysis of the emission

spectra [14,15].

5. Conclusions

In summary, we can say that our preliminary

analysis shows that the covariance mapping tech-

nique can be reliably used to study the dynamical

properties of the plasma produced in laser welding

processes. In particular, studying the AA6082 al-

loy both in case of sound welds and in case of

defective welding processes, we evidenced that:

(1) Al(I), Al(II) and O(I) lines are strongly corre-

lated for each combination of the process

parameters, except for the optimal experi-

mental condition where no correlation has

been found, thus indicating that the welding

zone has been effectively shielded from the

oxygen contamination from the surrounding

atmosphere.(2) Anti-correlation has been observed between

O(I) and Al(II) lines in case of optimal pro-

cess parameters. A possible explanation is

the formation of complex aluminum oxides,

starting from Al ions coming from the weld-

ing metal that dissociate inside the plasma

plume. This anti-correlation disappears and

changes into correlation in case of lowergas flow rate, indicating that the oxides do

not dissociate anymore; indeed the formation

of Al2O3 and other complex oxides is caused

by the inefficient shielding of the inert gas.

(3) In case of optimal operating condition a clear

correlation is observed between Al and Mg

emission lines, indicating the possible pres-

ence of significant loss of magnesium in themelted zone, even for deepest penetration.

(4) Mg emission lines have been observed in the

spectra acquired under all operating condi-

tions but they are significantly correlated to

other species only for heat inputs above a

certain threshold.

(5) The evaluation of the plasma electron tem-

perature obtained during a single weld runby using the Boltzmann plot method [12], cor-

roborated the results obtained by CMT. An

increase of the plasma electron temperature�smean value (from 9000 to 17000 K) as a func-

tion of growing heat input was observed,

together with a remarkable increase of the

temperature signal�s standard deviation rang-

ing from 370 to 1340 K. These data, as shownin a previous paper [12], indicate the presence

of perturbations of the melt pool which in

turn results in flaw welds. This is a further


experimental evidence that an excessive heat

input causes an increase of the vaporisation

rate of volatile elements, thus affecting the

stability of the melt pool and the quality of

the weld.

Acknowledgments

We gratefully acknowledge Dr. Nicola Cioffi of

CIS-Centro Interdipartimentale Spettroscopie,

Universita di Bari, for the XPS analysis of the

welded joints. We also acknowledge M. Roblesfor useful suggestions.

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Correlation analysis in laser welding plasma

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