Deep learning for plasticity and thermo-viscoplasticity

Abueidda, Diab W.; Korić, Seid; Sobh, Nahil; Şehitoğlu, Hüseyin

doi:10.1016/j.ijplas.2020.102852

Cited by 143 publications

(58 citation statements)

References 78 publications

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“…Since its introduction in the 1960s, ANNs continued to provide a powerful framework for modeling nonlinear systems, and they were used in a wide variety of engineering applications, including automatic control [ 24 ], solar energy systems [ 25 ], traffic and transportation [ 26 ], image processing [ 27 ], optimization of structures [ 28 ], materials science and engineering [ 29 , 30 , 31 , 32 ], manufacturing [ 33 ], fracture mechanics, and fault detection [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. In fracture mechanics, ANNs were mostly used in applications concerned with crack propagation, fatigue life, and failure mode prediction [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

2020

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Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.

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Section: Introductionmentioning

confidence: 99%

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

2020

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“…Firstly, we propose an improved faster R-CNN model, which can detect multi-scale defects better by adding spatial pyramid pooling (SPP) [21][22][23][24][25], and enhanced feature pyramid networks (FPN) [26] modules. To increase the detection accuracy of crazing defects, we modify the aspect ratio of the anchor.…”

Section: Amentioning

confidence: 99%

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Wang

Xia

et al. 2021

Metals

121

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Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

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“…In [14], region convolutional neural networks are used for automatic detection of steel surface defects in product quality control. In [15], authors show the application of neural networks to a cyclic elastoplastic material as well as to a more complex thermo-viscoplastic steel solidification model. In [16][17][18], ANNs address the problem of extracting the Jominy hardness profiles of steels directly from the chemical composition.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance

Hanza

Marohnić

Iljkić

et al. 2021

Metals

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Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs were the main alloying elements (C, Si, Mn, Cr, Mo, Ni), the austenitizing temperature, the austenitizing time, and the cooling time to 500 °C, while in the second configuration alloying elements were substituted by the specific Jominy distance. Comparing the results of total hardness prediction, it can be seen that the ANN using the specific Jominy distance as input variable (runseen = 0.873, RMSEunseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (runseen = 0.940, RMSEunseen = 46, MAPE = 10.7%). The research results indicate that the prediction of total hardness of steel can be successfully performed only based on four input variables: the austenitizing temperature, the austenitizing time, the cooling time to 500 °C, and the specific Jominy distance.

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Deep learning for plasticity and thermo-viscoplasticity

Cited by 143 publications

References 78 publications

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance

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