Weld bead recognition using laser vision with model-based classification

Ye, Guoliang; Guo, Jianwen; Sun, Zhong; Li, Chuan; Su, Zhong

doi:10.1016/j.rcim.2018.01.006

Cited by 47 publications

(18 citation statements)

References 22 publications

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“…The classification results are assessed by Cohen's kappa index from the confusion matrix and the receiving operating characteristic (ROC) curve area. The kappa statistic (κ) (15) measures the degree of agreement of categorized data [53]. It is widely used in remote sensing and ML applications to assess the classification results.…”

Section: Machine Learning Classificationmentioning

confidence: 99%

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Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Rodríguez‐Gonzálvez

Rodríguez‐Martín

2019

IEEE Access

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Weld bead detection is essential for automated welding inspection processes. The non-invasive passive techniques, such as photogrammetry, are quickly evolving to provide a 3D point cloud with submillimeter precision and spatial resolution. However, its application in weld visual inspection has not been extensively studied. The derived 3D point clouds, despite the lack of topological information, store significant information for the weld-plaque segmentation. Although the weld bead detection is being carried out over images or based on laser profiles, its characterization by means of 3D geometrical features has not been assessed. Moreover, it is possible to combine machine learning approaches and the 3D features in order to realize the full potential of the weld bead segmentation of 3D submillimeter point clouds. In this paper, the novelty is focused on the study of 3D features on real cases to identify the most relevant ones for weld bead detection on the basis of the information gain. For this novel contribution, the influence of neighborhood size for covariance matrix computation, decision tree algorithms, and split criteria are analyzed to assess the optimal results. The classification accuracy is evaluated by the degree of agreement of the classified data by the kappa index and the area under the receiver operating characteristic (ROC) curve. The experimental results show that the proposed novel methodology performs better than 0.85 for the kappa index and better than 0.95 for ROC area.

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Section: Machine Learning Classificationmentioning

confidence: 99%

“…As an alternative, the active methods, which are based on the use of an external illumination source, usually a laser beam or structured light, produce range maps of the weld joint [14]. However, the generated weld bead profiles are still inadequate for a precise bead geometric extraction [15].…”

Section: Introductionmentioning

confidence: 99%

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Rodríguez‐Gonzálvez

Rodríguez‐Martín

2019

IEEE Access

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“…Han et al [ 19 ] and Zhou et al [ 20 ] used the RANSAC algorithm for fitting linear functions to a laser stripe over a welded surface; which allowed them to measure weld bead dimensions. Ye et al [ 21 ] investigated the use of a model-based classification method to automatically segment the bead from the welding surface, regardless of the distance and the angle of the scanner to the welding surface. An approach based on pixel intensity analysis was proposed by Singh et al [ 22 ] for studying weld beads in P-91 steel plates.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Cruz

Rivas

Quiza

et al. 2020

Sensors

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One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

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“…This system adopted a sliding vector method for a fast and reliable approach to detect feature points on the laser stripe profile of welds. Ye et al [22] studied a model-based classification method that used a polynomial model in conjunction with the expectation-maximization (EM) algorithm for weld bead recognition on the 3D bead profile measured by an LVS. Recently, the deep learning algorithm has been intensively applied for the monitoring of the welding process and the prediction of the weld quality owing to its high-performance predictive power.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

Lee

Hwang

Kim

et al. 2020

Sensors

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Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.

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Weld bead recognition using laser vision with model-based classification

Cited by 47 publications

References 22 publications

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

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