Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Moon, In Yong; Jeong, Hwan Jeong; Lee, Ho Won; Kim, Se Jong; Oh, Youngseok; Jung, Jaimyun; Oh, Sehyeok; Kang, Seong-Hoon

doi:10.3390/met12020223

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…The typical BP ANN results for metal dynamic constitutive models are illustrated in the Figure 18, where the input layer typically includes temperature (T), strain rate ( . ε), and strain (ε), and the output layer represents the flow stress (σ) [129]. the training database and cannot replace the theoretical significance and research value traditional constitutive models [127].…”

Section: Artificial Neural Network Constitutive Modelmentioning

confidence: 99%

“…The output of its upper layer nodes is the input of the low layer nodes, including feedforward and backpropagation algorithms [128]. The typical ANN results for metal dynamic constitutive models are illustrated in the Figure 18, wh the input layer typically includes temperature (T), strain rate (𝜀𝜀), and strain (ε), and output layer represents the flow stress (σ) [129]. In 1995, Rao et al [130] applied the four-layer BP neural network to the flow str prediction of the thermal deformation process for the first time.…”

Section: Artificial Neural Network Constitutive Modelmentioning

confidence: 99%

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A Review of Dynamic Mechanical Behavior and the Constitutive Models of Aluminum Matrix Composites

Li,

Luo,

Chao

et al. 2024

Materials

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Aluminum matrix composites (AMMCs) have demonstrated substantial potential in the realm of armor protection due to their favorable properties, including low density, high specific stiffness, and high specific strength. These composites are widely employed as structural components and frequently encounter high strain rate loading conditions, including explosions and penetrations during service. And it is crucial to note that under dynamic conditions, these composites exhibit distinct mechanical properties and failure mechanisms compared to static conditions. Therefore, a thorough investigation into the dynamic mechanical behavior of aluminum matrix composites and precise constitutive equations are imperative to advance their application in armor protection. This review aims to explore the mechanical properties, strengthening the mechanism and deformation damage mechanism of AMMCs under high strain rate. To facilitate a comprehensive understanding, various constitutive equations are explored, including phenomenological constitutive equations, those with physical significance, and those based on artificial neural networks. This article provides a critical review of the reported work in this field, aiming to analyze the main challenges and future development directions of aluminum matrix composites in the field of protection.

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Section: Artificial Neural Network Constitutive Modelmentioning

confidence: 99%

Section: Artificial Neural Network Constitutive Modelmentioning

confidence: 99%

A Review of Dynamic Mechanical Behavior and the Constitutive Models of Aluminum Matrix Composites

Li,

Luo,

Chao

et al. 2024

Materials

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“…Nevertheless, the accuracy of the models is highly dependent on the size and structure of the ANN. Moon et al [ 31 ] investigated the prediction accuracy of an ANN with different sizes, and they also revealed that the hidden layer size and node number have independent influences on the accuracy. Sani et al [ 32 ] predicted the constitutive equation of magnesium (Mg–Al–Ca) alloy by an ANN with one hidden layer and seven neurons per layer.…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

et al. 2023

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Artificial neural networks (ANNs) have been an important approach for predicting the value of flow stress, which is dependent on temperature, strain, and strain rate. However, there is still a lack of sufficient knowledge regarding what structure of ANN should be used for predicting metal flow stress. In this paper, we train an ANN for predicting flow stress of In718 alloys at high temperatures using our experimental data, and the structure of the ANN is optimized by comparing the performance of four ANNs in predicting the flow stress of In718 alloy. It is found that, as the size of the ANN increases, the ability of the ANN to retrieve the flow stress results from a training dataset is significantly enhanced; however, the ability to predict the flow stress results absent from the training does not monotonically increase with the size of the ANN. It is concluded that the ANN with one hidden layer and four nodes possesses optimized performance for predicting the flow stress of In718 alloys in this study. The reason why there exists an optimized ANN size is discussed. When the ANN size is less than the optimized size, the prediction, especially the strain dependency, falls into underfitting and fails to predict the curve. When the ANN size is less than the optimized size, the predicted flow stress curves with the temperature, strain, and strain rate will contain non-physical fluctuations, thus reducing their prediction accuracy of extrapolation. For metals similar to the In718 alloy, ANNs with very few nodes in the hidden layer are preferred rather than the large ANNs with tens or hundreds of nodes in the hidden layers.

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“…Commonly used constitutive models for prediction of flow stress are Arrhenius-type model [11][12][13][14][15], Johnson-Cook [16,17], Fields-Backofen [18][19][20][21], and Zerilli-Armstrong [22,23] models. In addition, models based on artificial neural network [24][25][26] and physical-based models [27][28][29] are also widely used. A brief review of most models is given in [30].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear multivariate constitutive equations for modeling hot deformation behavior

Ahmedabadi

2023

Mater. Res. Express

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Nonlinear constitutive equations are proposed to model variations in flow stress as a function of strain rate and temperature during hot deformation. Modified constitutive are applied to seventy data sets about hot deformation of alloys. Two modifications to conventional constitutive models are introduced, viz. (1) nonlinear and (2) multivariate models with the fitting of flow stress simultaneously with two variables. The predictive accuracy of constitutive equations was evaluated using three statistical parameters and compared with aconventional Arrhenius-like model. It is shown that nonlinear constitutive equations have improved predictive accuracy for variations in flow stress during hot deformation. The advantages of multivariate models include less computation and material parameters that are constants independent of temperature or strain rate. In another type of multivariate model, flow stress is expressed as linear and nonlinear polynomial functions of the Zener-Holloman parameter. This approach gives a single value of the activation energy of hot deformation.The results have indicated that a generalized second-order multivariate constitutive equation can be used to better predict flow stress, as a function strain rate and temperature, during hot deformation.

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Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Cited by 13 publications

References 27 publications

A Review of Dynamic Mechanical Behavior and the Constitutive Models of Aluminum Matrix Composites

A Review of Dynamic Mechanical Behavior and the Constitutive Models of Aluminum Matrix Composites

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

Nonlinear multivariate constitutive equations for modeling hot deformation behavior

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