Coaxial monitoring of the fibre laser lap welding of Zn-coated steel sheets using an auxiliary illuminant

Zhang, Yi; Zhang, Chenglei; Tan, Lipeng; Li, Shichun

doi:10.1016/j.optlastec.2013.03.001

Cited by 44 publications

(14 citation statements)

References 9 publications

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“…The energy conservation equation is described as (10) where the source term q represents the energy changes. The energy source is mainly on the liquid/gas interface, and it includes laser energy absorbed q laser and energy transported due to melting, evaporation and thermal emission as (11) where m e is the mass loss due to evaporation, and L is…”

Section: Heat Transfermentioning

confidence: 99%

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Numerical Simulation of Transport Phenomena for Laser Full Penetration Welding

Zhao

2017

Journal of Welding and Joining

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In laser full penetration welding process, full penetration hole(FPH) is formed as a result of force balance between the vapor pressure and the surface tension of the surrounding molten metal. In this work, a threedimensional numerical model based on a conserved-mass level-set method is developed to simulate the transport phenomena during laser full penetration welding process, including full penetration keyhole dynamics. Ray trancing model is applied to simulate multi-reflection phenomena in the keyhole wall. The ghost fluid method and continuum method are used to deal with liquid/vapor interface and solid/liquid interface. The effects of processing parameters including laser power and scanning speed on the resultant full penetration hole diameter, laser energy distribution and energy absorption efficiency are studied. The model is validated against experimental results. The diameter of full penetration hole calculated by the simulation model agrees well with the coaxial images captured during laser welding of thin stainless steel plates. Numerical simulation results show that increase of laser power and decrease of welding speed can enlarge the full penetration hole, which decreases laser energy efficiency.

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Section: Heat Transfermentioning

confidence: 99%

“…Blug et al (2011) [7][8][9] investigated coaxial images of laser full penetration welding, extracted full penetration from coaxial images and developed a close loop control system to improve laser full penetration welding quality. Similarly, Zhang et al (2013) 10) studied the full penetration hole dynamics using an auxiliary illuminant.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Transport Phenomena for Laser Full Penetration Welding

Zhao

2017

Journal of Welding and Joining

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“…The keyhole is typically surrounded by molten material, the weld pool. Size and shape of weld pool are important geometrical parameters that correlate with weld shape and quality [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards

Knaak

Eßen

Kröger

et al. 2021

Sensors

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In modern production environments, advanced and intelligent process monitoring strategies are required to enable an unambiguous diagnosis of the process situation and thus of the final component quality. In addition, the ability to recognize the current state of product quality in real-time is an important prerequisite for autonomous and self-improving manufacturing systems. To address these needs, this study investigates a novel ensemble deep learning architecture based on convolutional neural networks (CNN), gated recurrent units (GRU) combined with high-performance classification algorithms such as k-nearest neighbors (kNN) and support vector machines (SVM). The architecture uses spatio-temporal features extracted from infrared image sequences to locate critical welding defects including lack of fusion (false friends), sagging, lack of penetration, and geometric deviations of the weld seam. In order to evaluate the proposed architecture, this study investigates a comprehensive scheme based on classical machine learning methods using manual feature extraction and state-of-the-art deep learning algorithms. Optimal hyperparameters for each algorithm are determined by an extensive grid search. Additional work is conducted to investigate the significance of various geometrical, statistical and spatio-temporal features extracted from the keyhole and weld pool regions. The proposed method is finally validated on previously unknown welding trials, achieving the highest detection rates and the most robust weld defect recognition among all classification methods investigated in this work. Ultimately, the ensemble deep neural network is implemented and optimized to operate on low-power embedded computing devices with low latency (1.1 ms), demonstrating sufficient performance for real-time applications.

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“…Yamada et al [5] used X-ray images of internal materials with strong synchrotron radiation to observe the weld pool and defects of laser welding, and studied the relationship between the shape of weld pool and the welding quality. Zhang et al [6] used the coaxial monitoring system to extract the edge of molten pool. Wang et al [7] and Huang et al [8] measured the distribution of welding temperature by infrared thermography.…”

Section: Introductionmentioning

confidence: 99%

Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing

Wang

Zhang

et al. 2020

IEEE Access

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Wire Arc Additive Manufacturing (WAAM) has developed rapidly in recent years and has been widely used in industry. Cold Metal Transfer (CMT), a kind of Gas Metal Arc Welding (GMAW), is widely used in the modeling of thin parts. In the monitoring of CMT process, it is necessary to monitor the weld reinforcement of the deposited layer. It can provide effective alerting for welding and help to improve welding quality. In this paper, the weld reinforcement and the position of molten pool image on the deposited layer are determined by the molten pool visual acquisition system. According to the data of the same direction additive manufacturing experiment, the future molten pool image is predicted by the improved prediction network (PredNet). Meanwhile through a regression network (SERes), the predicted results are regressed to the accurate weld reinforcement information of the deposited layer in advance. The experimental results show that the monitoring system designed in this paper can predict the change of molten pool shape 140ms in advance and the average precision of the weld reinforcement regression is better than 0.3mm. This paper provides the necessary basis for the shape online monitoring and controlling in WAAM process.INDEX TERMS additive manufacturing, long-term prediction, weld reinforcement regression, molten pool image, deep learning.

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Coaxial monitoring of the fibre laser lap welding of Zn-coated steel sheets using an auxiliary illuminant

Cited by 44 publications

References 9 publications

Numerical Simulation of Transport Phenomena for Laser Full Penetration Welding

Numerical Simulation of Transport Phenomena for Laser Full Penetration Welding

A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards

Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing

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