Application of Pattern Recognition for a Welding Process

Haffner, Oto; Kučera, Erik; Kozák, Štefan; Stark, Erich

doi:10.15439/2017f115

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…In addition, this work was motivated by Haffner et al. [7, 10] who proposed a system for automatic weld evaluation. Their work achieved good results, but most of the detected contours were not smooth and sometimes missed the external edges of the welding bead.…”

Section: Introductionmentioning

confidence: 99%

“…The images used for the experiments were obtained from different environments and some of them have no clear distinction between the foreground and background regions to allow the smooth segmentation process. In addition, this work was motivated by Haffner et al [7,10] who proposed a system for automatic weld evaluation. Their work achieved good results, but most of the detected contours were not smooth and sometimes missed the external edges of the welding bead.…”

Section: Introductionmentioning

confidence: 99%

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Morphological geodesic active contour algorithm for the segmentation of the histogram‐equalized welding bead image edges

Mlyahilu

Lee

et al. 2022

IET Image Processing

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Assessment and evaluation are the essential processes of industrially manufactured products for the determination of the quality and quantity of products. They give justifications in a practical way about whether the machine is perfect or imperfect, which can lead to a better or poorer production. In this study, the authors propose an algorithm that uses morphological geodesic active contour and image processing techniques to perform segmentation and assess the performance of a robot used to manufacture welding beads. The algorithm has four parameters which are pre-processed images, balloon force, smoothing parameter, and number of iterations. To pre-process the images, the algorithm uses an inverse Gaussian gradient operator for edge detection and applies the histogram equalization method to level the distribution. To detect the external contour of the bead, the level set is initialized as the region of interest whereby a balloon force can inflate or deflate towards the edges. To smoothen the contour, a smoothing parameter is applied to convert the jagged lines into a curve over a reasonable number of iterations. Based on the experimental results, the authors' algorithm used a fixed balloon force of −2, a smoothing parameter value of 4, and 40 iterations to segment images obtained from three different environments. The computation time for the segmentation and evaluation of one image was 0.70, 0.61, and 0.67 s for datasets with high brightness, low brightness, and normal brightness, respectively. Additionally, the authors' proposed algorithm achieved an outstanding performance of 0.9954, 0.9843, 0.9892, and 0.9435 in terms of recall, precision, F-measure, and IOU, respectively. To justify the performance of the authors' proposed algorithm, the authors compared it with the existing algorithms and found that it worked better than all the others for segmentation, although it lagged behind the entropy-based algorithm in terms of speed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Morphological geodesic active contour algorithm for the segmentation of the histogram‐equalized welding bead image edges

Mlyahilu

Lee

et al. 2022

IET Image Processing

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“…The authors of [15][16][17][18] deal with the development of a system for automatic weld evaluation using new information technologies based on cloud computing and single-board computer in the context of Industry 4.0. The proposed approach is based on using a visual system for weld recognition, and a neural network cloud computing for real-time weld evaluation, both implemented on a single-board low-cost computer.…”

Section: Introductionmentioning

confidence: 99%

Using Entropy for Welds Segmentation and Evaluation

Haffner

Kučera

Drahoš

et al. 2019

Entropy

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In this paper, a methodology based on weld segmentation using entropy and evaluation by conventional and convolution neural networks to evaluate quality of welds is developed. Compared to conventional neural networks, there is no use of image preprocessing (weld segmentation based on entropy) or data representation for the convolution neural networks in our experiments. The experiments are performed on 6422 weld image samples and the performance results of both types of neural network are compared to the conventional methods. In all experiments, neural networks implemented and trained using the proposed approach delivered excellent results with a success rate of nearly 100%. The best results were achieved using convolution neural networks which provided excellent results and with almost no pre-processing of image data required.

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