An inspection approach for casting defects detection using image segmentation

Riaz, Farhan; Kamal, Khurram; Zafar, Tayyab; Qayyum, Rizwan

doi:10.1109/icmsc.2017.7959451

Cited by 22 publications

(11 citation statements)

References 7 publications

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“…To further study the feasibility and generalization ability of the proposed method, we also propose to apply the proposed DSA method to detect corner case data in real-world application. Here, the metal casting product image data is taken for case study, which is applied for industrial quality inspection in real world [34]. It has totally 6,633 training data images, and 715 testing images.…”

Section: E Real-world Data Analysismentioning

confidence: 99%

Corner case data description and detection

Ouyang

Marco

Isobe

et al. 2021

Preprint

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As the major factors affecting the safety of deep learning models, corner cases and related detection are crucial in AI quality assurance for constructing safety-and securitycritical systems. The generic corner case researches involve two interesting topics. One is to enhance DL models' robustness to corner case data via the adjustment on parameters/structure. The other is to generate new corner cases for model retraining and improvement. However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training. Therefore, this paper proposes a simple and novel approach aiming at corner case data detection via a specific metric. This metric is developed on surprise adequacy (SA) which has advantages on capture data behaviors. Furthermore, targeting at characteristics of corner case data, three modifications on distanced-based SA are developed for classification applications in this paper. Consequently, through the experiment analysis on MNIST data and industrial data, the feasibility and usefulness of the proposed method on corner case data detection are verified.

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Section: E Real-world Data Analysismentioning

confidence: 99%

Corner case data description and detection

Ouyang

Marco

Isobe

et al. 2021

Preprint

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“…To classify potential defects, we built threshold classifiers for each feature and then trained a strong ensemble classifier consisting of the threshold classifiers. In order to obtain the feature that yields the best detection performance, the receiver operation characteristic [1] (ROC) curve is analyzed and the AdaBoost algorithm [21] is applied to produce the weight for each threshold classifier.…”

Section: Defect Identification Based On Multithreshold Adaboostmentioning

confidence: 99%

“…Various unintended internal defects, including blowholes, fractures, inclusions, or slag formation, can occur during the metal casting process [1,2]. Some internal defects can negatively affect the strength of the product.…”

Section: Introductionmentioning

confidence: 99%

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

Zhao

Liang

et al. 2019

Mathematical Problems in Engineering

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Small blurred blowholes are difficult to detect, especially those in castings having a complex structure. In this paper, we propose a blowhole detection method based on bidirectional enhancement and omnidirectional analysis for X-ray inspection of castings. After obtaining the X-ray images of a casting from the X-ray inspection equipment, there are two main processes to detect the blowholes. First, in the extraction process for the potential defect region, we propose a method based on bidirectional enhancement that highlights the blurred defects and models the nondefective structure. Second, in the precise defect identification process, we propose a method to distinguish real defects by 3D model fitting-based omnidirectional analysis. The experimental results indicate that the proposed method has a higher performance in blowhole detection than comparable methods.

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“…Their results indicated that the mean true positive rate of WRF prediction was 93%, which was higher than both the 68% of the decision tree and the 65% of the SVM. To detect casting surface defects such as pores, pinholes, and cracks, Riaz et al [12] passed images through a Gaussian lter to smooth them and then detected and classi ed defects in the images using k-means.…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies have rarely focused on the detection of impurities or remnants of shell molds on the surface of investment castings after sandblasting; most studies investigated the detection of pores, pinholes, and cracks associated with the casting process [6,11,12,15]. To protect the eyesight of workers and reduce their exposure to environments with high levels of noise and poor air quality, in this study we employed AOI for defect detection.…”

Section: Introductionmentioning

confidence: 99%

Inspection of sandblasting defect in investment castings by deep convolutional neural network

Kuo

Huang

et al. 2022

Int J Adv Manuf Technol

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Investment castings often have surface impurities and pieces of shell molds can remain on the surface after sandblasting. Identi cation of defects involves time-consuming manual inspections in working environments of high noise and poor air quality. To reduce labor costs and increase the health and safety of employees, we applied automated optical inspection (AOI) combined with a deep learning framework based on convolutional neural networks (CNNs) to the detection of sandblasting defects. We applied the following four classic CNN models for training and predictive classi cation: AlexNet, VGG-16, GoogLeNet, and ResNet-34. In terms of predictive classi cation, AlexNet, VGG-16, and GoogLeNet v1 could accurately determine whether there were defects. Among the four models, AlexNet was the most accurate, with prediction accuracy of 99.53% for qualifying products and 100% for defective products. We demonstrate a direct detection technique based on the AOI and CNN structure with a fast and exible computational interface.

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An inspection approach for casting defects detection using image segmentation

Cited by 22 publications

References 7 publications

Corner case data description and detection

Corner case data description and detection

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

Inspection of sandblasting defect in investment castings by deep convolutional neural network

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