Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach

Wasmer, Kilian; Le-Quang, Tri; Meylan, Bastian; Vakili-Farahani, Farzad; Olbinado, Margie P.; Rack, Alexander; Shevchik, Sergey

doi:10.1016/j.procir.2018.08.054

Cited by 54 publications

(24 citation statements)

References 8 publications

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“…Similarly to the LBR, the quality-significant events cannot be clearly recognized in the AE signal. Similar observations and conclusions were reported in our previous works 29,31 .…”

Section: Lbr and Ae Signatures For Laser Welding The Corresponding Lsupporting

confidence: 93%

“…The domain of wavelet spectrograms is also a 2D time-frequency domain, to which existing CNN can be applied directly. At the same time, in practical applications, the spectrograms preserve the special features of the original signals, and evidence of this was successfully demonstrated in earlier investigations 22,30,31,34,35 .…”

Section: Resultsmentioning

confidence: 68%

“…The built-in germanium (Ge) photodiode originally had a spectral sensitivity in the range of 800-1,800 nm. A narrow band-pass filter (FB1070-10, Thorlabs Inc., USA) with a center wavelength of 1070 ± 2 nm and a Full Width Half Max (FWHM) of 10 ± 2 nm was installed to the photodiode to provide a selective transmission of the LBR radiation from the process zone 22,[29][30][31] . The photodiode signal was acquired with an oscilloscope Teledyne LeCroy HDO6104 at sampling rates within the range of 50-2,000 kHz.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

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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...

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“…Similarly to the LBR, the quality-significant events cannot be clearly recognized in the AE signal. Similar observations and conclusions were reported in our previous works 29,31 .…”

Section: Lbr and Ae Signatures For Laser Welding The Corresponding Lsupporting

confidence: 93%

Section: Resultsmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

“…Le-Quang et al [33] used high speed X-ray imaging to show the real challenge to monitor the keyhole dynamic, in particular the transition stable-unstable keyhole but even more important the creation and disappearance of pores during the process. Wasmer et al [34] applied a specialized gradient boost to differentiate AE from stable/unstable keyholes and spatter. All aforementioned approaches demonstrated the feasibility of developing ML based universal tools for in situ and real-time quality monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…All aforementioned approaches demonstrated the feasibility of developing ML based universal tools for in situ and real-time quality monitoring. A special interest of these works is to detect single defects, such as pores and cracks [25]- [34].…”

Section: Introductionmentioning

confidence: 99%

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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Machine learning monitoring for laser osteotomy

Shevchik

Kenhagho

Le-Quang

et al. 2021

Journal of Biophotonics

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This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser‐induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre‐trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting‐edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84–99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real‐life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal.

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Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach

Cited by 54 publications

References 8 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

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

Machine learning monitoring for laser osteotomy

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