Measurement of Weld Bead Defect for Shock Absorber Using Laser Vision Sensor

Lee, H.; Sung, K.; Park, H.; Rhee, Sehun

doi:10.4028/www.scientific.net/kem.270-273.2332

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…Ye 등 은 2D 레이저 비전 센서를 활용하여 용접부 비드를 스 캐닝하였고, 비드 높이와 비드 폭을 검출하는 연구를 수행하였다 17) . Lee 등은 레이저 비전 센서를 활용하여 실시간으로 용접부 결함을 검출하는 연구를 수행하였으 며, 현장 적용을 고려하여 GUI 환경 프로그램을 개발 하였다 18) . 그리고 맞대기 조인트에서 아크 용접부에 대 한 레이저 비전을 적용하여 비드 외관 프로파일을 획득 하였으며 비드 폭을 측정하였다 19,20) .…”

Section: Gmaw의 경우 타 공정들과 마찬가지로 품질 확보를unclassified

Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW

Kim¹,

Hwang²,

Kim³

et al. 2023

J Weld Join

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Weld shape and size generally determine the quality of gas metal arc welding. Auto parts manufacturers prescribe the size and shape of the weld because they can indicate the mechanical properties of the weld. It is impossible to evaluate the quality of all welds through destruction inspection. Therefore, research on welding quality inspection using laser vision sensors as a non-destructive inspection method is underway. Although the external profile of the weld can be measured using a laser vision sensor, studies to predict the weld strength are insufficient. In this study, an artificial neural network (ANN) model was developed to predict the welding strength of the lap-fillet weld of an aluminum alloy. Input date for weld size was obtained in two ways. In the first method, a bead profile was acquired using a laser vision sensor, whereas the size of the weld was obtained through the acquired bead profile. In the second method, the size of the weld was obtained directly from cross-section analysis. The output data on the strength of the weld was obtained through a tensile shear test. Two models for predicting the tensile shear strength based on ANN were developed. By predicting the tensile strength of both models, the average error rate was within 10%, but the prediction accuracy using the laser vision sensor was better than that of the cross-sectional method.

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Section: Gmaw의 경우 타 공정들과 마찬가지로 품질 확보를unclassified

Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW

Kim¹,

Hwang²,

Kim³

et al. 2023

J Weld Join

View full text Add to dashboard Cite

show abstract

“…As problems have emerged about manual intervention during the manufacturing process, the introduction of automation systems has assisted workers considerably in production lines. Lee et al [29] developed a laser-vision-based inspection system for welding defects on automobiles' shock absorbers. Because of noise in industrial environments, however, it is difficult to apply the laser vision sensor in production lines.…”

Section: Related Workmentioning

confidence: 99%

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

Wang

Liu

et al. 2023

Sustainability

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Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits.

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Laser-vision-based quality inspection system for small-bead laser welding

Nguyen

Lee

2014

Int. J. Precis. Eng. Manuf.

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Measurement of Weld Bead Defect for Shock Absorber Using Laser Vision Sensor

Cited by 5 publications

References 1 publication

Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW

Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

Laser-vision-based quality inspection system for small-bead laser welding

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