Strain rate effect on mechanical properties of 0.24% carbon steel using artificial neural network

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

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Study of the Strain Rate Effect on Cold-Reduced Carbon Steel and Aluminium Alloy with Numerical Simulations

Materials Science Forum ◽

10.4028/www.scientific.net/msf.532-533.973 ◽

2006 ◽

Vol 532-533 ◽

pp. 973-976

Author(s):

Lin Wang ◽

Tai Chiu Lee ◽

Luen Chow Chan

Keyword(s):

Carbon Steel ◽

Strain Rate ◽

Strain Path ◽

Plastic Material ◽

Strain Rate Effect ◽

Rate Effect ◽

Forming Process ◽

Risk Area ◽

Sensitive Material ◽

Simulation Results

In this paper, the effect of strain rate has been considered in the simulation of forming process with a simple form combined into the material law. Quite a few researchers have proposed various hardening laws and strain rate functions to describe the material tensile curve. In this study, the strain rate model Cowper-Symonds is used with anisotropic elasto-plastic material law in the simulation process. The strain path evolution of certain elements, when the strain rate is considered and not, is compared. Two sheet materials, Cold-reduced Carbon Steel (SPCC) JIS G3141 and Aluminum alloy 6112 are used in this study. Two yield criteria, Hill 48 and Hill 90, are applied respectively to improve the accuracy of simulation result. They show different performance when strain rate effect is considered. Strain path of the elements in the fracture risk area of SPCC (JIS G3141) varies much when the strain rate material law is used. There is only little difference of the strain distribution of Al 6112 when the strain rate effect is included and excluded in the material law. The simulation results of material SPCC under two conditions indicate that the strain rate should be considered if the material is the rate-sensitive material, which provides more accurate simulation results.

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Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

Volume 12: Mechanics of Solids, Structures, and Fluids ◽

10.1115/imece2020-23515 ◽

2020 ◽

Author(s):

Yangping Li ◽

Yangyi Liu ◽

Sihua Luo ◽

Zi Wang ◽

Ke Wang ◽

...

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Cooling Rate ◽

Volume Fraction ◽

Sem Images ◽

Mechanical Hardness ◽

Nickel Based Superalloy ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

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Strain rate effect on mechanical properties of 3D needle-punched C/C composites at different temperatures

Composites Part B Engineering ◽

10.1016/j.compositesb.2018.10.044 ◽

2019 ◽

Vol 160 ◽

pp. 140-146 ◽

Cited By ~ 2

Author(s):

Xiaochao Jin ◽

Cheng Hou ◽

Chunling Li ◽

Xiaobin Wang ◽

Xueling Fan

Keyword(s):

Mechanical Properties ◽

Strain Rate ◽

Strain Rate Effect ◽

Rate Effect ◽

Different Temperatures

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A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

Materials Science Forum ◽

10.4028/www.scientific.net/msf.488-489.793 ◽

2005 ◽

Vol 488-489 ◽

pp. 793-796 ◽

Cited By ~ 1

Author(s):

Hai Ding Liu ◽

Ai Tao Tang ◽

Fu Sheng Pan ◽

Ru Lin Zuo ◽

Ling Yun Wang

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Magnesium Alloys ◽

Tensile Yield Strength ◽

Data Set ◽

The Neural Network ◽

Artificial Neural ◽

Prediction Of Mechanical Properties ◽

Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

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