Non-destructive Real Time Monitoring of the Laser Welding Process

Šebestová, Hana; Chmelíčková, Hana; Nožka, L.; Moudrý, J.

doi:10.1007/s11665-012-0193-4

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…The particular inprocess monitoring method was implemented into a proportional-integral (PI) controller, which adjusted the laser power, aiming at a constant penetration depth. The correlation between electron plasma temperature and weld penetration was investigated by the researchers in [69]. The frequent implementation of spectroscopes is also proven in [70].…”

Section: Optical Signal Techniquesmentioning

confidence: 99%

Quality assessment in laser welding: a critical review

Stavridis

Papacharalampopoulos

Stavropoulos

2017

Int J Adv Manuf Technol

182

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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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Section: Optical Signal Techniquesmentioning

confidence: 99%

Quality assessment in laser welding: a critical review

Stavridis

Papacharalampopoulos

Stavropoulos

2017

Int J Adv Manuf Technol

182

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“…24 The research findings of Sebestova and others also prove that there is hardly any correlation between electron temperature of chromium (or iron) and weld penetration during galvanised sheet overlap welding. 73 It is well known that metallurgical defects like cracks in the aluminium alloy plate are resulted from the amount of silicon and magnesium in the base material. Such cracks can be prevented by some amount of Si (for example, 7% or more).…”

Section: Visual Sensormentioning

confidence: 99%

Review of laser welding monitoring

You

Gao

Katayama

2014

Science and Technology of Welding and Joining

192

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Laser welding, as a highly efficient processing technology, has been widely applied to manufacturing industry. This paper makes an overview on real time monitoring of laser welding. It begins with a detailed introduction to six typical sensors (photodiode, visual, spectrometer, acoustical sensor, pyrometer, plasma charge sensor) in laser welding detection. Then it makes a review on multi-sensor fusion technology in both laser welding monitoring and adaptive control. Last, subjects for future research concerning welding monitoring and control have been proposed. The paper concludes that the real-time monitoring of laser welding can provide a great amount of valid information about welding status to help effectively identify weld defects and realize adaptive control.

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“…In addition to the spectrometers for real-time and non-destructive monitoring, photo-diodes also have been utilized for monitoring of LBW [ 21 , 22 , 23 ]. However, a combination of photo-diodes is necessary to cover the whole relevant spectral range.…”

Section: Introductionmentioning

confidence: 99%

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

Jadidi

Sikström

et al. 2022

Sensors

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Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.

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Non-destructive Real Time Monitoring of the Laser Welding Process

Cited by 21 publications

References 13 publications

Quality assessment in laser welding: a critical review

Quality assessment in laser welding: a critical review

Review of laser welding monitoring

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

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