Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN

Maleki, Erfan; Ünal, Okan

doi:10.1007/s00366-020-00964-6

Cited by 37 publications

(12 citation statements)

References 59 publications

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“…There have been many studies describing the corrosion resistance, mechanical and functional properties of this steel [ 7 , 8 ]. Currently, many studies have focused on possible ways to improve the properties of AISI 304 steel [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Laser Dissimilar Welding of AISI 430F and AISI 304 Stainless Steels

et al. 2020

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A dissimilar autogenous laser welded joint of AISI 430F (X12CrMoS17) martensitic stainless steel and AISI 304 (X5CrNi18-10) austenitic stainless steel was manufactured. The welded joint was examined by non-destructive visual testing and destructive testing by macro- and microscopic examination and hardness measurements. With reference to the ISO 13919-1 standard the welded joint was characterized by C level, due to the gas pores detected. Microscopic observations of AISI 430F steel revealed a mixture of ferrite and carbides with many type II sulfide inclusions. Detailed analysis showed that they were Cr-rich manganese sulfides. AISI 304 steel was characterized by the expected austenitic microstructure with banded δ-ferrite. Martensitic microstructure with fine, globular sulfide inclusions was observed in the weld metal. The hardness in the heat-affected zone was increased in the martensitic steel in relation to the base metal and decreased in the austenitic steel. The hardness range in the weld metal, caused by chemical inhomogeneity, was 184–416 HV0.3.

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Section: Introductionmentioning

confidence: 99%

Laser Dissimilar Welding of AISI 430F and AISI 304 Stainless Steels

et al. 2020

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“…The application of artificial neural networks and their advantages in numerical modeling has been discussed by many researchers [38,39]. As regards, the purpose of the study was to predict the maximum of ground surface settlement.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil

Ghiasi

Koushki

2020

SN Appl. Sci.

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The main purpose of this research is to predict the ground surface settlement in tunneling of a single circular tunnel with simultaneous changes in the mechanical properties of soil and geometrical properties of the tunnel section. In this research, numerical and parametric analysis of circular tunneling in frictional-cohesive saturated soil has been investigated using 2D finite element method by ABAQUS. In other words, the behavior of ground surface, considering to change the different values of depth-to-diameter ratio (H/D), soil cohesion, internal friction angle, permeability coefficient, and the influence of these variables on settlement of surface in each model, has been separately evaluated. Then, a multilayer perceptron (MLP) artificial neural network is designed to predict the ground surface settlement. MLP is a type of feedforward artificial neural network utilizing backpropagation technique for training phase, and the Levenberg-Marquardt method is used to reduce the errors and the distance between the network outputs and finite element method results. There are some independent variables in the input layer and a dependent variable in the output layer. The middle layer consists of seven neurons. Finally, the high potential of the artificial neural network with a correlation coefficient of 0.98 is shown in the prediction of ground surface settlement.

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“…On the other hand, artificial intelligence (AI) based methods such as neural networks (NN) are remarkably applied in different aspects of science and engineering [26][27][28][29], as well as their applications in fatigue behavior prediction and analysis [30][31][32][33][34][35] and modelling of SP process [28,30,[36][37][38]. In general, a neural network has three major layers of input, hidden and output [39].…”

Section: Introductionmentioning

confidence: 99%

Analysing the Fatigue Behaviour and Residual Stress Relaxation of Gradient Nano-Structured 316L Steel Subjected to the Shot Peening via Deep Learning Approach

et al. 2021

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In this study, the effect of kinetic energy of the shot peening process on microstructure, mechanical properties, residual stress, fatigue behavior and residual stress relaxation under fatigue loading of AISI 316L stainless steel were investigated to figure out the mechanisms of fatigue crack initiation and failure. Varieties of experiments were applied to obtain the results including microstructural observations, measurements of hardness, roughness, induced residual stress and residual stress relaxation as well as axial fatigue test. Then deep learning approach through neural networks was used for modelling of mechanical properties and fatigue behavior of shot peened material. Comprehensive parametric analyses were performed to survey the effects of different key parameters. Afterward, according to the results of neural network analysis, further experiments were performed to optimize and experimentally validate the desirable parameters. Based on the obtained results the favorable range of shot peening coverage regarding improved mechanical properties and fatigue behavior was identified as no more than 1750% considering Almen intensity of 21 A (0.001 inch). Graphic abstract

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Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN

Cited by 37 publications

References 59 publications

Laser Dissimilar Welding of AISI 430F and AISI 304 Stainless Steels

Laser Dissimilar Welding of AISI 430F and AISI 304 Stainless Steels

Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil

Analysing the Fatigue Behaviour and Residual Stress Relaxation of Gradient Nano-Structured 316L Steel Subjected to the Shot Peening via Deep Learning Approach

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