Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

Maleki, Erfan; Ünal, Okan; Sahebari, Seyed Mahmoud Seyedi; Kashyzadeh, Kazem Reza; Danilov, Igor

doi:10.3390/jmse10020128

Cited by 16 publications

(6 citation statements)

References 74 publications

(84 reference statements)

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“…Correlation between multiple IFs and fatigue life. [90] www.advancedsciencenews.com www.aem-journal.com SVM, [56,[143][144][145] GA-ANN, [146] GA-BPNN, [146,147] RF, [89,137,148,149] DNN, [112,[150][151][152] convolution neural network (CNN), [153][154][155] long short-term memory (LSTM), [156][157][158] radial basis function neural network (RBFNN), [53,88,159,160] etc.) were developed to realize the fatigue performance prediction of welded joints.…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

“…[ 23,131–134 ] After the establishment of the database and the analysis of the IFs, different data‐driven algorithms were used to predict the fatigue performance. Specifically, several effectively approaches (Bayesian model, [ 135–137 ] ANN, [ 67,129,138–141 ] particle swarm optimization (PSO)‐BPNN, [ 75,142 ] Ant colony optimization‐BPNN, [ 39,55 ] SVM, [ 56,143–145 ] GA‐ANN, [ 146 ] GA‐BPNN, [ 146,147 ] RF, [ 89,137,148,149 ] DNN, [ 112,150–152 ] convolution neural network (CNN), [ 153–155 ] long short‐term memory (LSTM), [ 156–158 ] radial basis function neural network (RBFNN), [ 53,88,159,160 ] etc.) were developed to realize the fatigue performance prediction of welded joints.…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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Section: Progress In Prediction Approachesmentioning

confidence: 99%

Section: Progress In Prediction Approachesmentioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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“…Schütt et al used a prediction model for quantum-chemical to enable spatially and chemically resolved insights into quantum-mechanical observables of molecular systems . In material science, deep convolutional neural networks (DCNNs) are commonly used for object detection and image classification, allowing for identifying various structures and defects. ,− However, despite their utility, the lack of interpretability in DCNNs has hindered their broader adoption in material research. There are several methods available for visualizing the middle layers of DCNNs to make them interpretable, such as sensitivity analysis, , layer-wise relevance propagation, − and pixel-space gradient visualization techniques like guided backpropagation and deconvolution .…”

Section: Introductionmentioning

confidence: 99%

Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding

Shi

Feng

et al. 2023

ACS Appl. Mater. Interfaces

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Constructing porous structures in electromagnetic interference (EMI) shielding materials is a common strategy to decrease the secondary pollution caused by the reflection of electromagnetic waves (EMWs). However, the lack of direct analysis methods makes it difficult to fully understand the effect of porous structures on EMI, hindering EMI composites’ development. Furthermore, while deep learning techniques, such as deep convolutional neural networks (DCNNs), have significantly impacted material science, their lack of interpretability limits their applications to property predictions and defect detection tasks. Until recently, advanced visualization techniques provided an approach to reveal the relevant information behind DCNNs’ decisions. Inspired by it, a visual approach for porous EMI nanocomposite mechanism studies is proposed. This work combines DCNN visualization with experiments to investigate EMI porous nanocomposites. First, a rapid and straightforward salt-leaked cold-pressing powder sintering method is employed to prepare high-EMI CNTs/PVDF composites with various porosities and filler loadings. Notably, the solid sample with 30 wt % loading maintains an ultrahigh shielding effectiveness of 105 dB. The influence of porosity on the shielding mechanism is discussed macroscopically based on the prepared samples. To determine the shielding mechanism, a modified deep residual network (ResNet) is trained on a dataset of scanning electron microscopy (SEM) images of the samples. The Eigen-CAM visualization of the modified ResNet intuitively shows that the amount and depth of the pores impact the shielding mechanisms and that shallow pore structures contribute less to EMW absorption. This work is instructive for material mechanism studies. Besides, the visualization has the potential as a porous-like structure marking tool.

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“…For example, fatigue research based on ML has made significant progress in both fatigue strength prediction [18][19][20][21][22][23][24] and fatigue crack-driving force prediction [25] . Several researchers have recently developed ML-based models for fatigue life prediction [26][27][28][29][30][31][32] . Zhang et al proposed a neuro-fuzzy-based ML method for predicting the high-cycle fatigue life of laser powder bed fusion stainless steel 316 L under different processing conditions, postprocessing treatments, and cyclic stresses [26] .…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Zhan et al used several ML algorithms to establish models for predicting the fatigue life of 316 L with different additive manufacturing process parameters and fatigue loadings [27] . Furthermore, based on deep learning of long short-term memory networks, Yang et al established a more general life-prediction method for the multiaxial fatigue of materials [30] . In a study by Maleki et al, a deep neural network was utilized for fatigue behavior prediction and analysis of a coated AISI 1045 mild carbon steel and AISI 316L stainless steel, which demonstrated a promising approach for deep learning in fatigue behavior modeling [31,32] .…”

Section: Introductionmentioning

confidence: 99%

High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network

Wei¹,

Wang²,

Jia³

et al. 2022

J Mater Inf

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The evaluation and prediction of fatigue properties are crucial for metallic materials. Although the determination of S-N curves represents the most important methods for evaluating such properties, its fatigue testing is costly and time-consuming. Furthermore, fatigue testing involves different test conditions, thereby complicating the evaluation of the fatigue properties. This study develops a transfer convolutional neural network (TR-CNN) framework, in which the prediction of the reversed torsion S-N curves of steels is transferred from rotating bending S-N curves. In the TR-CNN framework, the source CNN models for rotating-bending curve prediction are first trained based on the composition and process conditions. Subsequently, based on the source models, the reversed torsion S-N curves are estimated by training the TR-CNN models based on only a small dataset. After proving the rationality of the framework, its universality with respect to different amounts of data is further investigated. The reversed torsion curves under small-sample conditions (22 samples) are predicted accurately by the TR-CNN. Additionally, the TR-CNN models remain accurate under varying amounts of data (22-112 samples), showing excellent generality for different amounts of fatigue data. The predictive capability of the TR-CNN models is improved by introducing tensile properties into the source models. The proposed TR-CNN framework can significantly reduce the cost of evaluating fatigue properties, and the prediction of S-N curves can be optimized by combining the transfer framework and low-cost properties related to fatigue.

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Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

Cited by 16 publications

References 74 publications

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding

High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network

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