The improvement on constitutive modeling of Nb-Ti micro alloyed steel by using intelligent algorithms

Wu, Shijing; Zhou, Xiaoguang; Cao, Guangming; Liu, Zhenyu; Wang, Guodong

doi:10.1016/j.matdes.2016.12.058

Cited by 47 publications

(16 citation statements)

References 51 publications

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“…These values are more accurate than the constitutive equations model. It is in agreement with results observed in other studies [34][35][36][37][38][39][40][41][42][43][44]. …”

Section: Artificial Neural Network Analysissupporting

confidence: 93%

“…A typical ANN model is generally constructed using various steps, such as: (i) collecting the data; (ii) determining the input/output (target) parameters; (iii) analysing and pre-processing the experimental data; (iv) training the ANN; (v) testing the trained ANN; and, finally, (vi) evaluating the performance of the constructed ANN [34][35][36][37][38][39][40][41][42][43][44]. A popular learning method for algorithms with multilayer observations is back-propagation (BP).…”

Section: Artificial Neural Network Analysismentioning

confidence: 99%

Section: Artificial Neural Network Analysismentioning

confidence: 99%

“…It was necessary to achieve the network in a right form to be read. As a result, the initial data should be unified to make the ANN training more efficient [37]. The typical method for unifying is expressed as Equation (24): (24) where X is the initial data value; X min and X max are the minimum and maximum values of X, respectively; X is the associated data of the corresponding X.…”

Section: Artificial Neural Network Analysismentioning

confidence: 99%

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Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Mosleh

Mikhaylovskaya

Котов

et al. 2017

Metals

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Abstract:The paper focuses on developing constitutive models for superplastic deformation behaviour of near-α titanium alloy (Ti-2.5Al-1.8Mn) at elevated temperatures in a range from 840 to 890 • C and in a strain rate range from 2 × 10 −4 to 8 × 10 −4 s −1 . Stress-strain experimental tensile tests data were used to develop the mathematical models. Both, hyperbolic sine Arrhenius-type constitutive model and artificial neural-network model were constructed. A comparative study on the competence of the developed models to predict the superplastic deformation behaviour of this alloy was made. The fitting results suggest that the artificial neural-network model has higher accuracy and is more efficient in fitting the superplastic deformation flow behaviour of near-α Titanium alloy (Ti-2.5Al-1.8Mn) at superplastic forming than the Arrhenius-type constitutive model. However, the tested results revealed that the error for the artificial neural-network is higher than the case of Arrhenius-type constitutive model for predicting the unmodelled conditions.

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“…These values are more accurate than the constitutive equations model. It is in agreement with results observed in other studies [34][35][36][37][38][39][40][41][42][43][44]. …”

Section: Artificial Neural Network Analysissupporting

confidence: 93%

Section: Artificial Neural Network Analysismentioning

confidence: 99%

Section: Artificial Neural Network Analysismentioning

confidence: 99%

Section: Artificial Neural Network Analysismentioning

confidence: 99%

See 2 more Smart Citations

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Mosleh

Mikhaylovskaya

Котов

et al. 2017

Metals

View full text Add to dashboard Cite

show abstract

“…Approximation and subsequent prediction of an experimental flow curve dataset can be performed via derived flow stress models (an index of different models can be found in [20]). Nevertheless, in recent times, this approximation issue has often been solved utilizing artificial neural networks (ANNs), which allow a higher curve fit accuracy [13,19,[21][22][23][24]. The ANN approach is part of a wide family of biology-inspired mathematical techniques that are intended to solve complicated scientific and engineering tasks, e.g., highly nonlinear approximation issues [25].…”

Section: Introductionmentioning

confidence: 99%

Extension of Experimentally Assembled Processing Maps of 10CrMo9-10 Steel via a Predicted Dataset and the Influence on Overall Informative Possibilities

Opěla

Kawulok

et al. 2019

Metals

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Processing maps embody a supportive tool for the optimization of hot forming processes. In the present work, based on the dynamic material model, the processing maps of 10CrMo9-10 low-alloy steel were assembled with the use of two flow curve datasets. The first one was obtained on the basis of uniaxial hot compression tests in a temperature range of 1073–1523 K and a strain rate range of 0.1–100 s−1. This experimental dataset was subsequently approximated by means of an artificial neural network approach. Based on this approximation, the second dataset was calculated. An important finding was that the additional dataset contributed significantly to improving the informative ability of the assembled processing maps in terms of revealing potentially inappropriate forming conditions.

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Polyurethane with nano‐SiO₂ based surface sizing method for 3D printed carbon fiber reinforced nylon 6 composites

et al. 2022

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Continuous carbon fiber reinforced polymer composite 3D printing has drawn more attention due to its ability to manufacture composite structures with complex shapes. The surface treatment of CF is necessary to improve the combination between carbon fiber (CF) and matrix. In this work, polyurethane with nano‐SiO2 (PU/SiO2) was used to size CF to enhance the compatibility of CF and nylon 6 (PA6). The chemistry and morphology of the CF were characterized by Fourier transform infrared spectroscopy (FTIR), X‐ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The interface strength between CF and PA6 was tested by the fiber pull‐out test, and eventually obtained optimized concertation of PU/SiO2 sizing agents (with 25 wt% PU and 0.4 wt% nano‐SiO2). The results show that compared with untreated CF, the interfacial shear strength (IFSS) of CF and PA6 after sizing increased by 316.93%. In addition, several CF/PA6 composite specimens printed by a homemade composite 3D printer were used to do mechanical tests. The results show that the flexural strength increased by 33.87%, the tensile strength increased by 20.75%, and the interlaminar shear strength (ILSS) increased by 66.54%.

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The improvement on constitutive modeling of Nb-Ti micro alloyed steel by using intelligent algorithms

Cited by 47 publications

References 51 publications

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Extension of Experimentally Assembled Processing Maps of 10CrMo9-10 Steel via a Predicted Dataset and the Influence on Overall Informative Possibilities

Polyurethane with nano‐SiO₂ based surface sizing method for 3D printed carbon fiber reinforced nylon 6 composites

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The improvement on constitutive modeling of Nb-Ti micro alloyed steel by using intelligent algorithms

Cited by 47 publications

References 51 publications

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Extension of Experimentally Assembled Processing Maps of 10CrMo9-10 Steel via a Predicted Dataset and the Influence on Overall Informative Possibilities

Polyurethane with nano‐SiO2 based surface sizing method for 3D printed carbon fiber reinforced nylon 6 composites

Contact Info

Product

Resources

About

Polyurethane with nano‐SiO₂ based surface sizing method for 3D printed carbon fiber reinforced nylon 6 composites