Detection of Weld Defects by High Speed Imaging of the Vapor Plume

Brock, Christian; Tenner, Felix; Klämpfl, Florian; Hohenstein, R.; Schmidt, Michael

doi:10.1016/j.phpro.2013.03.113

Cited by 14 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…blowout, porosity). Both factors are known to be critical for the mechanical properties of the weld joint and are widely used for characterization in industrial validation tests 3,52 . Consequently, the following categories were considered in the present work: conduction welding, stable keyhole, unstable keyhole, blowout and pores.…”

Section: Resultsmentioning

confidence: 99%

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Shevchik

Le-Quang

Meylan

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. this work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on reallife data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system. The introduction of laser technology in metal welding of metals is dated back to the late 1960s 1,2 when it immediately showed advantages as compared to traditional arc welding 3. The attractions of this technique are in the non-contact processing, the absence of tool wear, high aspect ratio of the melt pool, better material fusion, possibility to process refractory materials, low running costs and high processing speed 3,4. Today, laser welding is a key technology in many fields e.g. automotive 5 and aerospace 3,6 industries, naval and heavy machinery production 7 , medicine and micromechanics 3. Unfortunately, the potential of this technology is not fully exploited, particularly in applications that require the guarantee of high weld quality. The reason is the non-linear nature of light-matter interactions, which complicates the reproducibility of the weld quality in mass production 8-10. The complex dynamics of the process, especially in keyhole welding regime, and its instabilities can cause various defects at the joint 3,10-12. A defect type of particular interest is porosity, which is a hidden threat for the mechanical properties of the workpieces 3,9-11. Obviously, an adequate, robust and low cost quality monitoring system is of great desire. The major challenge in developing such technique is in the difficulties to inspect directly the sub-surface behavior of the process zone in real-life conditions 13. Multiple approaches have been proposed, which are mostly based on mathematical modeling aiming to reconstruct the under surface dynamics using inspections of the surface via measurements of temperature 11,12,14,15 , optical 16,17 and/or acoustic 18,19 emissions (AE). However, those approaches face three main problems. Firstly, modeling often suffers inaccuracies originating from the deviations of the model assumptions from the real parameters' values. More complicated assumptions can be used to imp...

show abstract

Section: Resultsmentioning

confidence: 99%

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Shevchik

Le-Quang

Meylan

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Further, two high-speed cameras were synchronously utilized to investigate the 3D vapor plume fluctuations in overlap-joint of LBW. The results indicate that the position of the vapor plume contains the abundant information on the welding process [87]. To better observe the dynamic melt pool and vapor plume during laser oscillation welding, Li et al [91] employed a off-axial visual sensing system.…”

Section: Off-axial Visual Sensingmentioning

confidence: 99%

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Zhang

et al. 2022

Journal of Manufacturing Processes

View full text Add to dashboard Cite

“…The plume instability is one of the most important reasons for keyhole fluctuation mechanism, leading to welding defects, such as spatters and pores [5]. A correlation between the vapour plume centre position and the depth of penetration was established by Brock et al [6]. It has been observed that the vapour plume inclines in the direction of the melt pool as the penetration depth increases.…”

Section: Introductionmentioning

confidence: 97%

Fundamental understanding of the interaction of continuous wave laser with aluminium

Coroado

Meco

Williams

et al. 2017

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In welding, the depth of penetration, weld profile and the corresponding thermal cycle are the three basic outcomes that a user wishes to control flexibly. In laser welding applications, controlled application of power and energy density is the key to achieve predictable control of these characteristics. Creation of an analytical model is an important step towards understanding the underpinning science of laser metal interaction in controlling the depth, bead geometry and thereby temperature profile of a weld. The "power factor model", which correlates the power applied per unit length to the laser metal interaction time, has been originally developed and validated for mild steel, guides a user on the selection laser system parameters, to achieve specific weld profile. This study is performed to extend the power factor-interaction time model to aluminium alloys by understanding the underpinning laser aluminium interaction parameters in terms of power density, interaction time, specific point energy and their correlation with the weld bead profiles. Although the power factor and interaction time showed a rectangular hyperbolic relationship, as observed in low carbon steel, for a specific weld depth and profile, the absolute magnitude and the characteristic profile of the curve is different due to the intrinsic differences in physical and thermal properties of aluminium as compared to steel. It was shown that identical depth of penetration but different weld metal profile can be obtained for a specific beam diameter for a range of power and travel speed by keeping the energy input per unit length constant.

show abstract

Detection of Weld Defects by High Speed Imaging of the Vapor Plume

Cited by 14 publications

References 10 publications

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Fundamental understanding of the interaction of continuous wave laser with aluminium

Contact Info

Product

Resources

About