Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network

Gupta, Amit Kumar; Singh, Swadesh Kumar; Reddy, Swathi; Hariharan, Gokul

doi:10.1016/j.matdes.2011.09.060

Cited by 81 publications

(31 citation statements)

References 22 publications

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“…First, both from the start and finish of instabilities/serrations it is concluded that quasi-static plastically deformed 304 steel undergoes DSA in the temperature interval from 200 to 700ºC, which is comparable to that of 316 steel [10][11][12]15,16 . The reader should remember that the DSA interval, between 50 to 300ºC for common carbon steel 5 , is significantly below the one here found for 304 steel, which coincides with applied high temperature conditions for stainless steels.…”

Section: Resultsmentioning

confidence: 99%

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Relevance of Dynamic Strain Aging under Quasi-Static Tension on AISI 304 Stainless Steel

et al. 2017

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The interval of existence of dynamic strain aging (DSA) in AISI type 304 austenitic stainless steel subjected to quasi-static tension tests, under strain rates from 3.5 x 10 -2 to 3.5 x 10 -4 s -1 , in the temperature range from 25 to 800ºC was investigated. It was found that DSA occurs in the range extending from around 200 to 700ºC. Plastic instabilities associated with serrations, Portevin-Chatelier effect, allowed activation energies to be calculated and a possible mechanism of dislocation interaction with interstitial carbon atoms to be proposed. Significant increase in the ultimate strength, uniform elongation and work hardening demonstrate that DSA is a relevant phenomenon, which improves the high temperature mechanical properties of 304 steel.

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Section: Resultsmentioning

confidence: 99%

“…However, it was not possible to determine activation energies associated with atomistic mechanisms. Recent works on 316 steel [15][16][17][18] related both anomalous variation in the work hardening parameters with strain rate and temperature as well as plastic instabilities and serrations with DSA.…”

Section: Introductionmentioning

confidence: 99%

Relevance of Dynamic Strain Aging under Quasi-Static Tension on AISI 304 Stainless Steel

et al. 2017

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“…The typical artificial neural network contains three layers, which are input layer, hidden layer and output layer. The input layer receives outside signals and then the output layer generates output signals, while the hidden layer provides the complex network architecture to mimic the non-linear relationship between input signals and output signals [20]. Basically, a feed forward network, which was trained by the back propagation algorithm, was used to establish the back-propagation (BP) neural network.…”

Section: Bp-ann Modelmentioning

confidence: 99%

“…BP-ANN has been widely used to process complex non-linear relationships among several variables [6,[20][21][22][23]26]. It is a quite efficient computing tool to learn and predict the hot deformation behavior between inputs and outputs by simulating the neural networks structure of the biological neurons.…”

Section: Bp-ann Modelmentioning

confidence: 99%

Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model

2016

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Abstract:In order to predict hot deformation behavior of superalloy nimonic 80A, a back-propagational artificial neural network (BP-ANN) and strain-dependent Arrhenius-type model were established based on the experimental data from isothermal compression tests on a Gleeble-3500 thermo-mechanical simulator at temperatures ranging of 1050-1250˝C, strain rates ranging of 0.01-10.0 s´1. A comparison on a BP-ANN model and modified Arrhenius-type constitutive equation has been implemented in terms of statistical parameters, involving mean value of relative (µ), standard deviation (w), correlation coefficient (R) and average absolute relative error (AARE). The µ-value and w-value of the improved Arrhenius-type model are 3.0012% and 2.0533%, respectively, while their values of the BP-ANN model are 0.0714% and 0.2564%, respectively. Meanwhile, the R-value and ARRE-value for the improved Arrhenius-type model are 0.9899 and 3.06%, while their values for the BP-ANN model are 0.9998 and 1.20%. The results indicate that the BP-ANN model can accurately track the experimental data and show a good generalization capability to predict complex flow behavior. Then, a 3D continuous interaction space for temperature, strain rate, strain and stress was constructed based on the expanded data predicted by a well-trained BP-ANN model. The developed 3D continuous space for hot working parameters articulates the intrinsic relationships of superalloy nimonic 80A.

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“…Therefore, the input datasets and output datasets measured in different units should be normalized into the dimensionless units that in a small range to train the BP-ANN model, and then the convergence speed and accuracy of the BP-ANN model would be improved 13 . The input variables (temperature, strain and strain rate) and output variable (flow stress) were normalized by the improved relation Equation 1 13,40 . In order to narrow the range of the normalized values, a serious of empirical coefficients were adopted in Equation 1 13,40 .…”

Section: Construction Process Of Bp-ann Model For As-cast Az80 Magnesmentioning

confidence: 99%

Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

et al. 2015

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Hot compressions of as-cast AZ80 magnesium alloy in a wide temperature range of 523-673 K and strain rate range of 0.01-10 s -1 with a height reduction of 60% were conducted by a Gleeble-1500 thermo-mechanical test simulator. The hot flow behaviors show highly non-linear intrinsic relationships with temperature, strain and strain rate. In order to model the complicated flow behaviors, error back-propagation algorithm, a representative method to minimize the target error, was selected to train the artificial neural network. A comparative study was made on the predictabilities of the improved Arrhenius-type and BP-ANN model by using two standard statistical parameters including correlation coefficient (R) and average absolute relative error (AARE). Comparison results show that the well-trained BP-ANN has higher prediction accuracy. Three highlight applications were presented. Firstly, the strain-stress data volume was expanded by BP-ANN predictions above experimental conditions. Secondly, the expanded data were applied in the simulations of isothermal compressions, and high simulation accuracy for the load-stroke curve was achieved. Thirdly, a three-dimensional (3D) interaction space of stress, strain, temperature and strain rate was constructed based on the intensive data, which supplies the stress data to arbitrary temperature, strain rate, and strain.

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Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network

Cited by 81 publications

References 22 publications

Relevance of Dynamic Strain Aging under Quasi-Static Tension on AISI 304 Stainless Steel

Relevance of Dynamic Strain Aging under Quasi-Static Tension on AISI 304 Stainless Steel

Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model

Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

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