Application of artificial neural networks for modelling correlations in titanium alloys

Malinov, Savko; Sha, Wei

doi:10.1016/j.msea.2003.09.029

Cited by 134 publications

(63 citation statements)

References 30 publications

(38 reference statements)

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“…Without limiting the neuron number in hidden layer, the BP-ANN model with two hidden layers or multiple hidden layers can achieve higher training . Generally, the neuron number in each hidden layer should be increased with increasing training sample number 13,42 . And an empirical formula for neuron number of each hidden layer is expressed as Equation 2.…”

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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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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“…These data have then been used to train and test fuzzy logic based ANNs, which have then been used to predict the influence of alloying additions on hardness and modulus (which has been checked by experiment). Malinov and Sha 44 have integrated a range of Ti alloy property models (based on MLPs trained and tested from literature data, for: time temperature transformation diagrams, processing property models, fatigue life and corrosion resistance models) to allow optimisation of the various inputs to achieve the desired combination of property outputs in a graphical user interface to allow ease of use.…”

Section: Physical and Mechanical Propertiesmentioning

confidence: 99%

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

Reed¹,

Starink²,

Gunn³

et al. 2009

Materials Science and Technology

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Many adaptive numerical modelling (ANM) techniques such as artificial neural networks (including multilayer perceptrons), support vector machines and Gaussian processes have now been applied to a wide range of regression and classification problems in materials science. Materials science offers a wide range of industrial applications and hence problem complexity levels from well physically characterised systems (e.g. high value, low volume products) to high volume low cost applications with intrinsic scatter due to commercial manufacturing processes. The authors review a number of recent examples in the literature, with the aim of identifying best practice in the use of these techniques as part of a multistrand modelling approach. The importance of understanding the basic principles of these modelling techniques and how they can link with other modelling strategies is emphasised. In particular the authors wish to identify the importance of hybrid physically based ANM in taking the field forward, which can range from, at the most basic level, careful data selection and data preprocessing to a full integration of physically based models with advanced ANM. A number of case studies are presented to illustrate the main points of the paper. Keywords: Neural networks, Support vector machines, Gaussian processes, Adaptive numerical modelling, Data driven techniques, Hybrid modelling, Applications in materials science IntroductionThe links between processing variables, microstructure evolution, resultant properties and hence mechanical performance are core tenets of materials engineering and a variety of modelling approaches have been applied to these materials modelling challenges. In some cases, a clear physical basis for such models has now been established, but often these can apply only to well defined conditions, which may not be representative of either genuine industrial (scaled up) production processes, or materials performance under realistic service conditions. The advantages of physically based models are:(i) they are robust to interpolation and extrapolation (as long as the underlying physical mechanisms being modelled are in operation) (ii) they should not require large amounts of data, as typically they only have a limited number of fitted parameters; ideally, a physically based model may have no fitted parameters (iii) the mechanistic verifications derived from physically based modelling may also offer insights into novel materials optimisation strategies (iv) a physically based model is relatively transparent, i.e. the mathematical underlying relationships between input and output parameters may be written in a reasonably clear mathematical form: this is part of a loosely defined group of methods which the authors term 'white box'. However, in a truly integrated processing-performance model, with the levels of uncertainty and complexity associated with industrial practice and complex service conditions, expressing these interlinked processes purely on physics based principles is unlikely to be feasible. ...

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“…Further enhancement of mechanical properties and performance of titanium alloys through the control of the size, shape and distribution of the grains of various phases, texture, structure and strength of grain boundaries and other microstructural factors requires deeper understanding and quantitative description of the relationships between these microstructural variables and material properties. Functional relations between microstructure parameters and alloy properties can be developed by neural network modelling [4][5]. However this approach requires creation of large dataset containing results of experiments made on various materials with different microstructures generated through heat treatment and thermomechanical processing.…”

Section: Introductionmentioning

confidence: 99%

Primary Creep Behaviour of Two-Phase Titanium Alloy with Various Microstructure

Ziaja¹,

Motyka²,

Kubiak³

et al. 2016

Archives of Metallurgy and Materials

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Creep and fatigue properties of two-phase titanium alloys show strong dependence on microstructure, especially morphology of the α and β phases which can be controlled to certain extent by proper selection of hot working and heat treatment conditions. In the paper the creep behaviour of Ti-6Al-2Mo-2Cr alloy (VT3-1) at elevated temperature was modelled. Finite element analyses of primary creep stage were carried out taking into account some microstructural features of the two-phase alloy that were included in the physical model and different properties of α and β phases. In order to verify results of calculations distinct types of microstructure were developed in the alloy by heat treatment and creep tests were carried out at elevated temperature (450°C) at various stress levels. Based on the FEM simulations the effect of changes of some microstructure features on primary creep strain development was estimated.

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Application of artificial neural networks for modelling correlations in titanium alloys

Cited by 134 publications

References 30 publications

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

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

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

Primary Creep Behaviour of Two-Phase Titanium Alloy with Various Microstructure

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