X-Ray and Optical Videography for 3D Measurement of Capillary and Melt Pool Geometry in Laser Welding

Boley, Meiko; Abt, Felix; Weber, Rudolf; Graf, Thomas

doi:10.1016/j.phpro.2013.03.105

Cited by 36 publications

(15 citation statements)

References 2 publications

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“…Due to the increase in depth of the keyhole, it becomes unstable, which leads to its partial collapse during the process, trapping the overheated vapor in the melted surrounding. This results in porosity after resolidification of the melt pool [9], [32], [33].…”

Section: Results and Discussion A Dataset Categorization And Labmentioning

confidence: 99%

“…Obviously, such method is destructive and time consuming and so it is not suitable for industrial mass production. Non-destructive alternatives also include post mortem analysis using active methods and the main ones are: X-ray tomography [9], ultrasonic testing and magnetic induced analysis (MIA) [10]. The use of X-ray tomography is strongly limited due to the large scale and expensive installations.…”

Section: Introductionmentioning

confidence: 99%

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Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

et al. 2019

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Laser welding is a rapidly developing technology that is of utmost importance in a number of industrial processes. The physics of the process has been investigated over the past 50 years and is mostly well understood. Nevertheless, online laser-quality monitoring remains an open issue until today due to its dynamic complexity. This paper is a supplement to existing approaches in the field of in situ and real-time laser-quality monitoring that presents a novel combination of state-of-the-art sensors and machine learning for data processing. The investigations were carried out using laser welding of titanium workpieces. The quality was estimated a posteriori by the visual inspection of cross-sections of the welded joints. Four quality categories were defined to cover the two main laser welding regimes: conduction and keyhole. The signals from the laser back reflection and optical and acoustic emissions were recorded during the laser welding process and were decomposed with the M-band wavelets. The relative energies of narrow frequency bands were taken as descriptive features. The correlation of the extracted features with the laser welding quality was carried out using the Laplacian graph support vector machine classifier. Also, an adaptive kernel for the classifier was developed to improve the analysis of the distributions of the complex features and was constructed from Gaussian mixtures. The presented laser welding setup and the developed adaptive kernel algorithm were able to classify the quality for every 2 µm of the welded joint with an accuracy ranged between 85.9% and 99.9%. Finally, the results of the developed adaptive kernel were compared with stateof-the-art machine learning methods.

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Section: Results and Discussion A Dataset Categorization And Labmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

et al. 2019

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“…One step further, the x-ray imaging inspection technique was adopted by the authors in [128] in order to optimize the laser parameters with the evaluation of the identified defects. Finally, the paper presented in [129] described a method for the reconstruction of the 3D shape of the melt pool and the capillary of a laser keyhole welding process. Three different diagnostic methods, including x-ray, optical videography, and metallographic cross sections, were combined to acquire the three-dimensional data of the solidus-liquidus surface.…”

Section: X-ray Radiographymentioning

confidence: 99%

Quality assessment in laser welding: a critical review

Stavridis

Papacharalampopoulos

Stavropoulos

2017

Int J Adv Manuf Technol

171

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Quality assessment methods and techniques for laser welding have been developed both in-and post-process. This paper summarizes and presents relevant studies being classified according to the technology implemented (vision, camera, acoustic emissions, ultrasonic testing (UT), eddy current technique (ECT)) for the quality inspection. Furthermore, the current review aims to map the existing modeling approaches used to correlating measured weld characteristics and defects with the process parameters. Research gaps and implications of the quality assessment in laser welding are also described, and a future outlook of the research in the particular field is provided.

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“…While first applied in the 1980s for electron beam 3 and laser welding 4 took it more than twenty years of improvement in imaging hardware to enable a detailed view inside the keyhole 5 and to model the three dimensional keyhole geometry depending on these images. 6 Furthermore, the use of tracer particles allowed the measurement of melt pool dynamics depending on the feed rate in continuous-wave laser welding. 7 However, the x-ray based techniques currently lack the high temporal and spatial resolution that can be achieved with modern high-speed cameras.…”

Section: State Of the Artmentioning

confidence: 99%

Experimental approach for quantification of fluid dynamics in laser metal welding

Tenner

Berg

Brock

et al. 2015

Journal of Laser Applications

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Laser metal welding is widely applied in industry. Nevertheless, a complete process understanding which leads to a full control of the process is not available yet. To contribute a solution for this task, we use two high-speed cameras with recording rates of up to 250 kHz and measure the velocity and direction of the fluid flow inside the keyhole with very high precision. Furthermore, we use tracer particles to measure the fluid dynamics in the keyhole. To get a deep view into the keyhole without the use of x-rays, we use a setup which allows us to look inside the keyhole during overlap welding of zinc-coated steel sheets with a spatial resolution of 25 μm per pixel and a temporal resolution of 5.3 μs per frame. Moreover, we quantify the influence of this approach on the process dynamics. Our findings show how the fluid dynamics inside the keyhole are linked to the laser power, the feed rate, and a gap between two zinc-coated steel sheets.

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X-Ray and Optical Videography for 3D Measurement of Capillary and Melt Pool Geometry in Laser Welding

Cited by 36 publications

References 2 publications

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

Quality assessment in laser welding: a critical review

Experimental approach for quantification of fluid dynamics in laser metal welding

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