Quality Assessment Method Based on a Spectrometer in Laser Beam Welding Process

Yu, Jiyoung; Lee, Huijun; Kim, Dong-Yoon; Kang, Minjung; Hwang, Insung

doi:10.3390/met10060839

Cited by 12 publications

(9 citation statements)

References 26 publications

(26 reference statements)

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“…In another research [ 34 ], a deep neural network (DNN) was employed for quality assessment of the LBW. A spectrometer used for collecting data and and the measured data were converted to RGB values.…”

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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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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“…Recently, research on welding condition monitoring using artificial intelligence and camera vision devices has gained increasing attention because of the increasing demand for manufacturing intelligence, cost reduction, efficiency, and quality. This research can be classified into three areas of application: weld defect prediction [10][11][12], weld bead shape prediction [13,14], and weld seam tracking [15][16][17]. For instance, Zhang et al [10] developed a convolutional neural network (CNN) algorithm based on a multi-sensor system, including an auxiliary illumination (AI) visual sensor system, UVV band visual sensor system, spectrometer, and two photodiodes to detect three different welding defects during highpower disk laser welding.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Zhang et al [10] developed a convolutional neural network (CNN) algorithm based on a multi-sensor system, including an auxiliary illumination (AI) visual sensor system, UVV band visual sensor system, spectrometer, and two photodiodes to detect three different welding defects during highpower disk laser welding. Yu et al [11] proposed a deep neural network (DNN)-based quality assessment method based on a spectrometer in the laser beam welding (LBW) process. Shin et al [12] proposed a DNN-based nondestructive testing method for the detection and prediction of porosity defects in real time based on welding voltage signals.…”

Section: Introductionmentioning

confidence: 99%

“…Chu et al presented time-frequency analysis as an effective method to determine welding quality and performed frequency analysis to distinguish between normal and defective welding process signals [27]. Based on the advantages of DNNs, such as their capabilities of nonlinear combination, learning between nonlinear variables, and understanding the potential structure of data, excellent results have been achieved in welding monitoring [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

Jin

Rhee

2021

Metals

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In the flux-cored arc welding process, which is most widely used in shipbuilding, a constant external weld bead shape is an important factor in determining proper weld quality; however, the size of the weld gap is generally not constant, owing to errors generated during the shell forming process; moreover, a constant external bead shape for the welding joint is difficult to obtain when the weld gap changes. Therefore, this paper presents a method for monitoring the weld gap and controlling the weld deposition rate based on a deep neural network (DNN) for the automation of the hull block welding process. Welding experiments were performed with a welding robot synchronized with the welding machine, and the welding quality was classified according to the experimental results. Welding current and voltage signals, as the robot passed through the weld seam, were measured using a trigger device and analyzed in the time domain and frequency domain, respectively. From the analyzed data, 24 feature variables were extracted and used as input for the proposed DNN model. Consequently, the offline and online performance verification results for new experimental data using the proposed DNN model were 93% and 85%, respectively.

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“…Seam tracking, defect detection, weld forming quality monitoring of laser welding process can be realized by collect various signals of the welding process by using inductor, capacitor, sound waves, photoelectric, visual and other kinds of sensors and processed them by computer [50]. These signals can be fed to the computer at the same time to adjust the welding parameters, finally realizing the high quality of laser welding process automation [51][52][53]. In the process of laser welding, it is difficult to accurately and quantitatively characterize important information, such as laser shielding rate caused by plasma and nano metal particle jet flow, metal evaporation behavior and flow and heat transfer behavior of molten pool, while these information is very important and necessary for controlling of weld formation and quality prediction.…”

Section: Introductionmentioning

confidence: 99%

Innovations in Monitoring, Control and Design of Laser and Laser-Arc Hybrid Welding Processes

Cheng

Ning

et al. 2021

Metals

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With the rapid development of high power laser, laser welding has been widely used in many fields including manufacturing, metallurgy, automobile, biomedicine, electronics, aerospace etc. Because of its outstanding advantages, such as high energy density, small weld size, easy automation. Combining the two heat sources of laser and arc for welding can achieve excellent results due to the synergistic effect. Laser welding is a complicated physical and chemical metallurgical process, involving the laser beam and molten pool, keyholes and materials melting, evaporation and multiple physical process. Process monitoring and quality control are important content of research and development in the field of laser welding, which is the premise to obtain fine weld with high quality. Numerical simulation technology can describe many complex physical phenomena in welding process, which is very important to predict weld forming and quality and clarify the underline mechanism. In this paper, the research progress of process monitoring, quality control and autonomous intelligent design of laser and laser-arc hybrid welding based on numerical simulation were reviewed, and the research hotspots and development trends of laser welding in the future are predicted.

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Quality Assessment Method Based on a Spectrometer in Laser Beam Welding Process

Cited by 12 publications

References 26 publications

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

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

Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

Innovations in Monitoring, Control and Design of Laser and Laser-Arc Hybrid Welding Processes

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