Construction of hot deformation processing maps for 9Cr-1Mo steel through conventional and ANN approach

Kumar, Sumit; Karmakar, Anish; Nath, S. K.

doi:10.1016/j.mtcomm.2020.101903

Cited by 25 publications

(18 citation statements)

References 33 publications

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“…Liu et al have shown a high accuracy ANN-based model for the determination of the 42CrMo steel hot flow behaviour compared to the Arrhenius-type model [15]. Similar results were obtained by other scholars for API 5CT-L80 [16], AISI-1045 [17], 9Cr-1Mo [18], 10Cr [19], 40Mn [20] and 33Cr23Ni8Mn3N [21] steels. K. Arun Babu et al have constructed a dynamic recrystallisation model in super austenitic stainless steel [22] using an ANN-based approach.…”

Section: Introductionsupporting

confidence: 59%

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Churyumov

Kazakova

Churyumova

2022

Metals

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Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s−1 and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation.

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Section: Introductionsupporting

confidence: 59%

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Churyumov

Kazakova

Churyumova

2022

Metals

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“…This is because the higher the polynomial order, the higher the degree of freedom of fitting. However, it will also lead to the complexity of the solution process [ 28 ]. In this work, a six-order polynomial function (as presented by Equation (12)) was applied to fit

, and

values, and the calculation results are shown in Table 2 :

where

denotes sixth-order polynomial coefficients.…”

Section: Methodsmentioning

confidence: 99%

Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model

Chen²,

Bhandari³

et al. 2022

Materials

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To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003–0.03 s−1 and temperature range 633–773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R2), average absolute relative error (AARE), and relative error (δ) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R2, lower AARE, and more concentrative δ value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.

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“…Various equations are proposed that allow the estimation of the number of neurons in the hidden layer. These equations often combine the number of neurons in the hidden layer with the number of neurons in the input and output layers and optionally with the number of patterns in the training data set [35][36][37]. In practice, the number of neurons in the hidden layer or layers is most often determined experimentally.…”

Section: Data Set and Neural Network Topologymentioning

confidence: 99%

“…The comparison of the error value for the training and test sets gives essential information about the quality of the model and is presented in many publications [27,34,46,55]. Such an assessment is facilitated by the scatter plots presented in many publications, where the measured and calculated values of the dependent variable are compared [28,32,33,36,41,46,48,62,[65][66][67][68][69].…”

Section: Model Selection and Overfitting Problemmentioning

confidence: 99%

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Sitek

Trzaska

2021

Metals

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Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

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Construction of hot deformation processing maps for 9Cr-1Mo steel through conventional and ANN approach

Cited by 25 publications

References 33 publications

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

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