Evaluation and Identifying the Ductile Fracture Area of X70 Steel from DWTT Broken Specimens

Skalny, Pavel

doi:10.1016/j.prostr.2016.06.463

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The impact test represented by The Charpy V-Notch (CVN) (ASTM International) [1]is used to determine the fracture value of the toughness of a considerable material in the frame that it is an easy and more economical method to perform experiments. The high cost of large-scale experimental campaigns has driven the development of more economical laboratory-scale tests such as the well-known Charpy V-Notch (CVN) impact test, Drop Weight Tear Test (DWTT) [2]and more recently the Dynamic Tear Test (DT3) [3]. For modern pipelines with a wall thickness reaching over 20mm, the CVN has a relatively small standard specimen size with a cross section of 10mm × 10mm [4].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Ouladbrahim

Belaidi

Khatir

et al. 2021

Frattura Ed Integrità Strutturale

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In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel.

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Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Ouladbrahim

Belaidi

Khatir

et al. 2021

Frattura Ed Integrità Strutturale

View full text Add to dashboard Cite

show abstract

“…In [7], three-dimensional (3D) scanner was used to acquire the three-dimensional fracture surface and a statistical method was applied to estimate BFR and DFR for the DWTT specimen. In [8], along with an expensive 3D scanner, a single charge-coupled device (CCD) camera was used to acquire fracture surface images. The K-means clustering algorithm was then applied to estimate the multivariate characteristics of the fracture surface.…”

Section: Introductionmentioning

confidence: 99%

Automatic Brittle Fracture Ratio Estimation Using Convolutional Neural Network Regression Based on Classmap Regulation

et al. 2021

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A convolutional neural network (CNN) based regression is proposed for estimating the brittle fracture ratio (BFR) in a fracture image of a drop weight tear test (DWTT) specimen. Different with the previous complex semantic segmentation based estimator, the method extracts the feature vector through global average pooling of feature map and calculates the BFR directly through the fully connected layer. By removing decoder network, the number of weights, training time, and required GPU memory dramatically reduced. To train the proposed CNN, a new loss function, which is the sum of L1-norm between class activation map and ground truth inspection image and L1-norm of BFR error, is also designed. To validate the present method, fracture images of 1532, 79, and 158 DWTT specimens obtained from real industrial site were used for training, validation, and test, respectively. The accuracy of the proposed method was evaluated based on the number of test samples with an error of 5% or less divided by the total number of test samples, which is the measure used in real industrial application. Despite having dramatically reduced the number of weights and inference time by 85.8% and 64.8%, respectively, the proposed method has a higher accuracy (96.2%) compared to that of the existing segmentation based BFR estimation method (94.9%).

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“…In [7], statistical methods and fractal concepts were used to evaluate the fracture surface of DWTT specimens. Multivariate characteristics of normal vectors in conjunction with the K-means clustering method were used in [8]. The inputs for the aforementioned algorithms were obtained by a 3D scanner.…”

Section: Introductionmentioning

confidence: 99%

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

et al. 2019

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This study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through dropweight tear test (DWTT) increases to evaluate steel's property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.

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Evaluation and Identifying the Ductile Fracture Area of X70 Steel from DWTT Broken Specimens

Cited by 4 publications

References 14 publications

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Automatic Brittle Fracture Ratio Estimation Using Convolutional Neural Network Regression Based on Classmap Regulation

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

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