A neural network to analyze plasma fluctuations with the aim to determine the degree of full penetration in laser welding

Beyer, E.; Maischner, D.; Kratzsch, Ch.

doi:10.2351/1.5058831

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…It is clear that the plasma density is a function of metal vapour density. This metal vapour density was determined by Beyer et al (1987) as…”

Section: Generation Of Electrons and Ions During Laser Keyhole Weldingmentioning

confidence: 99%

“…A cyclic heating and cooling of the plasma thus occurs which is seen as rapid fluctuation of the PCS signal. In addition to such fluctuation, a further source of plasma instability is hydrodynamic oscillation in the meltpool as described by Beyer et al (1987).…”

Section: Generation Of Electrons and Ions During Laser Keyhole Weldingmentioning

confidence: 99%

“…These techniques include acoustic methods (e.g. Saifi 1976, Jon 1985, Weerasinghe and Steen 1985, Weeter and Albright 1987, Hamann et al 1989, Li and Steen 1992, various optical method including plasma light sensing (Dixon and Lewis 1983, Beck and Jurca 1988, Chen et al 1993, CCD vision systems (Juptner et al 1986, Olsen 1988, a probe laser (Beyer et al 1987, Gazweiler et al 1988, Schafer et al 1990, and thermal sensing methods for welding and hardening (e.g. Webber 1987, Brookfield andSharkey 1992).…”

Section: Introductionmentioning

confidence: 99%

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Plasma charge sensor for in-process, non-contact monitoring of the laser welding process

Brookfield

Steen

1996

Meas. Sci. Technol.

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Laser keyhole welding is an important modern manufacturing technology. During such welding a plasma cloud is generated and the behaviour of this plasma is closely coupled to the behaviour of the weld. The plasma behaviour has been observed during welding through measurement of the space charge voltage induced on an electrically insulated welding nozzle, the plasma charge sensor (PCS). It is shown both theoretically and experimentally that the induced voltage is a measure of plasma temperature and thus of the welding performance. In particular, it is shown under laboratory conditions that the PCS signal can measure weld penetration and detect a wide range of weld defects.Results have also been obtained from the PCS installed on an industrial laser can welder. An expert system has been used to analyse the PCS signal automatically and classify weld defects. It is reported that a greater than 90% success rate has been achieved in detecting and classifying defects in high-speed industrial laser can welding.The PCS is shown to be a reliable and robust non-contact method of on-line weld monitoring well suited to installation on existing laser welding stations.

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“…It is clear that the plasma density is a function of metal vapour density. This metal vapour density was determined by Beyer et al (1987) as…”

Section: Generation Of Electrons and Ions During Laser Keyhole Weldingmentioning

confidence: 99%

Section: Generation Of Electrons and Ions During Laser Keyhole Weldingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Plasma charge sensor for in-process, non-contact monitoring of the laser welding process

Brookfield

Steen

1996

Meas. Sci. Technol.

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“…A variety of approaches has been adopted in order to develop non-intrusive, real-time sensors for weld quality [3][4][5][6]. Acoustic sensors are attractive because of their simplicity and low cost, but such devices are susceptible to interference in production environments.…”

Section: Introductionmentioning

confidence: 99%

A statistical approach to acoustic monitoring of laser welding

Duley

1996

J. Phys. D: Appl. Phys.

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Acoustic emission at frequencies between 20 Hz and 20 kHz during laser welding of steel contains important diagnostic information related to weld morphology, depth of penetration and the heat-affected zone. We have recorded acoustic spectra under a variety of welding conditions with mild and galvanized steel sheet and find that a well-defined spectrum is obtained under conditions that lead to an optimization of these weld characteristics. A statistical approach is developed which yields a predictive measure of weld quality, based on a comparison between real-time acoustic spectra, and that obtained under optimized conditions. This technique is applied to an analysis of bead on plate, lap and butt laser welds in mild and galvanized steel.

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Statistical Classification of Spectral Data for Laser Weld Quality Monitoring

Ali

Farson

2002

Journal of Manufacturing Science and Engineering

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Signals from several sensors were employed for real-time laser weld quality monitoring. Sheet-metal butt-joint laser welds of three quality classes (full penetration, partial penetration, gapped) were produced in experimental trials. Optical, air-born acoustic and plasma charge signals acquired during welding were subsequently Fourier-transformed and the spectra were analyzed individually to determine relationships to laser weld quality. The frequency bands most highly correlated to weld quality were identified by stepwise linear discriminant analysis (LDA) of the spectra. Testing of the quality discriminators formulated by LDA of the spectral data showed that the acoustic signal was most reliably correlated with weld quality. Fusing the data from all three sensors prior to LDA analysis produced a discriminator that had about the same reliability as one based on acoustic data alone.

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A neural network to analyze plasma fluctuations with the aim to determine the degree of full penetration in laser welding

Cited by 8 publications

References 2 publications

Plasma charge sensor for in-process, non-contact monitoring of the laser welding process

Plasma charge sensor for in-process, non-contact monitoring of the laser welding process

A statistical approach to acoustic monitoring of laser welding

Statistical Classification of Spectral Data for Laser Weld Quality Monitoring

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