A classification method of glass defect based on multiresolution and information fusion

Liu, Huai-guang; Chen, Youping; Xiang-qian, Peng; Xie, Jingming

doi:10.1007/s00170-011-3248-z

Cited by 25 publications

(7 citation statements)

References 14 publications

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“…Evidence theory known as Dempster-Shafer theory (DST) is one of the effective methods of uncertain information processing [8,9].…”

Section: Evidence Theorymentioning

confidence: 99%

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Knowledge base development for assembly planning using evidence theory.

Kutschenreiter-Praszkiewicz¹,

Matuszny²

2022

Journal of Machine Engineering

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This paper presents an approach to assembly planning in the early phase of product development. The product specification, workstation, environment, equipment and tools are not fully known in the early stage of product development. When comparing product variants at this stage there is a lack of data that affects the efficiency of the manufacturing process. It is therefore necessary to apply methods useful in processing incomplete and uncertain data. The main indicator which helps in comparing different product variants is manufacturing time standard. This papier is focused on assembly tool selection which is one of important data influenced assembly time. Based on the proposed algorithm and case study, a tool selection method using a decision tree induced from a training set with reduced uncertainty is presented.

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“…Evidence theory known as Dempster-Shafer theory (DST) is one of the effective methods of uncertain information processing [8,9].…”

Section: Evidence Theorymentioning

confidence: 99%

“…Liu et al use DST for glass defect identification [9]. In this approach, DST was combined with image classification methods using artificial neural networks and fuzzy k-nearest neighbour classifier.…”

Section: Evidence Theorymentioning

confidence: 99%

Knowledge base development for assembly planning using evidence theory.

Kutschenreiter-Praszkiewicz¹,

Matuszny²

2022

Journal of Machine Engineering

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“…The threshold is chosen to maximize the variance among the classes of the image histogram [47]. Otsu's algorithm is the basis of several inspection systems in the literature: In [20], it was used for surface inspection of transparent parts, in [13] and [25] for inspection during float glass fabrication.…”

Section: Algorithmsmentioning

confidence: 99%

“…Numerous industrial processes have adopted machine vision-based inspection systems [20][21][22][23][24][25][26][27], in addition to the related application domain such as the glass tube processing industry. In particular, the production of vials, bottles, and glasses [13,21,[28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Fast Blob and Air Line Defects Detection for High Speed Glass Tube Production Lines

et al. 2021

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During the production of pharmaceutical glass tubes, a machine-vision based inspection system can be utilized to perform the high-quality check required by the process. The necessity to improve detection accuracy, and increase production speed determines the need for fast solutions for defects detection. Solutions proposed in literature cannot be efficiently exploited due to specific factors that characterize the production process. In this work, we have derived an algorithm that does not change the detection quality compared to state-of-the-art proposals, but does determine a drastic reduction in the processing time. The algorithm utilizes an adaptive threshold based on the Sigma Rule to detect blobs, and applies a threshold to the variation of luminous intensity along a row to detect air lines. These solutions limit the detection effects due to the tube’s curvature, and rotation and vibration of the tube, which characterize glass tube production. The algorithm has been compared with state-of-the-art solutions. The results demonstrate that, with the algorithm proposed, the processing time of the detection phase is reduced by 86%, with an increase in throughput of 268%, achieving greater accuracy in detection. Performance is further improved by adopting Region of Interest reduction techniques. Moreover, we have developed a tuning procedure to determine the algorithm’s parameters in the production batch change. We assessed the performance of the algorithm in a real environment using the “certification” functionality of the machine. Furthermore, we observed that out of 1000 discarded tubes, nine should not have been discarded and a further seven should have been discarded.

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“…In the training phase, the correct class for each record is known, and the output nodes can therefore be assigned "class number" values-"1" for the node corresponding to the correct class, and "0" for the wrong class [15,16]. It is thus possible to compare the network's calculated output values with these "correct" (target) values, and calculate an error term for each node, which is the "Delta" rule.…”

Section: Preliminaries: Neural Networkmentioning

confidence: 99%

A Neural Network-Based Interval Pattern Matcher

Xue

Zhang

et al. 2015

Information

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One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

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A classification method of glass defect based on multiresolution and information fusion

Cited by 25 publications

References 14 publications

Knowledge base development for assembly planning using evidence theory.

Knowledge base development for assembly planning using evidence theory.

Fast Blob and Air Line Defects Detection for High Speed Glass Tube Production Lines

A Neural Network-Based Interval Pattern Matcher

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