Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network

Reddy, N.S.; Lee, C.S.; Kim, J.H.; Semiatin, S. L.

doi:10.1016/j.msea.2006.06.104

Cited by 36 publications

(14 citation statements)

References 28 publications

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“…The b-transus temperatures of the Ti-6Al-4V and Ti-7Al-1.5V alloys were estimated to be 1268 K and 1333 K (995°C and 1060°C), respectively. [18] A limited amount of work was conducted also on a set of three near single-phase b titanium alloys to confirm conclusions from modeling work regarding the flow stress of the b phase in Ti-6Al-4V. The compositions of these materials (Table II) were similar to those of the b phase in Ti-6Al-4V at temperatures of 1088 K, 1173 K, and 1223 K (815°C, 900°C, and 950°C).…”

Section: A Materialsmentioning

confidence: 66%

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A Self-Consistent Approach for Modeling the Flow Behavior of the Alpha and Beta Phases in Ti-6Al-4V

Kim

Semiatin

Lee

et al. 2010

Metall Mater Trans A

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The flow behavior of the a and b phases in Ti-6Al-4V was interpreted in the context of a self-consistent modeling formalism. For this purpose, high-temperature compression tests were conducted at various temperatures for a single-phase a alloy (Ti-7Al-1.5V), a variety of near-b alloys, and the two-phase alloy Ti-6Al-4V, each with an equiaxed microstructure. The flow behavior of the a phase in Ti-6Al-4V was deduced from the experimental results of the singlephase a alloy. The flow behavior of the b phase, which was predicted by using the self-consistent approach and the measured flow behaviors of Ti-6Al-4V and Ti-7Al-1.5V, showed good agreement with direct measurements of the various near-b alloys. From these results, it was shown that the strength of the a phase is approximately three times higher than that of the b phase at temperatures between 1088 K and 1223 K (815°C and 950°C). It was also concluded that the relative strain rates in the two phases varies significantly with temperature. The usefulness of the approach was confirmed by comparing the predicted and measured flow stresses for other Ti-6Al-4V and near-a alloys.

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Section: A Materialsmentioning

confidence: 66%

“…The volume fraction of a in Ti-6Al-4V was determined by both quantitative metallography and a neural-network approach. [18] After inserting the viscosity-like parameters k a and k a+b (Eq. [4]), and value of m (or average m at 1088 K [815°C]) into Eq.…”

Section: B Model Predictions For the B Phasementioning

confidence: 99%

A Self-Consistent Approach for Modeling the Flow Behavior of the Alpha and Beta Phases in Ti-6Al-4V

Kim

Semiatin

Lee

et al. 2010

Metall Mater Trans A

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“…For example, it is useful to predict the effect and the influence of an individual element or two on the permeability for any particular combination of input variables keeping the remaining unchanged. Sensitivity analysis helps to identify the important relationships between inputs and output [8,12,13]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Upon each presentation, the weights are adjusted to decrease the error between the networks' output and the targeted output. Various features in model development were clearly discussed in the earlier publications [12,13]. Sensitivity analysis was carried out on trained neural networks model.…”

Section: Data and Modeling Proceduresmentioning

confidence: 99%

Estimation of Permeability of Green Sand Mould by Performing Sensitivity Analysis on Neural Networks Model

Reddy¹,

Baek²,

Kim³

et al. 2014

Journal of Korea Foundry Society

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Permeability is the ability of a material to transmit fluid/gases. It is an important material property and it depends on mould parameters such as grain fineness number, clay, moisture, mulling time, and hardness. Modeling the relationships among these variable and interactions by mathematical models is complex. Hence a biologically inspired artificial neural-network technique with a back-propagation-learning algorithm was developed to estimate the permeability of green sand. The developed model was used to perform a sensitivity analysis to estimate permeability. The individual as well as the combined influence of mould parameters on permeability were simulated. The model was able to describe the complex relationships in the system. The optimum process window for maximum permeability was obtained as 8.75-10.5% clay and 3.9-9.5% moisture. The developed model is very useful in understanding various interactions between inputs and their effects on permeability.

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“…Present NN model requires for two kinds of dataset and they were collected from the published literature [6][7][8][9][10][11][12][13] as well as from own experimental results. Table 1 shows the dataset for the isothermal condition.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial neural network modeling of phase volume fraction of Ti alloy under isothermal and non-isothermal hot forging conditions

et al. 2007

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An artificial neural network (ANN) model was applied to simulate the phase volume fraction oftitanium alloy under isothermal and non-isothermal hot forging condition. For isothermal hot forging process, equilibrium phase volume fraction at specific temperature was predicted. For this purpose, chemical composition of six alloy elements (i.e. AI, Y, Fe, 0, N, and C) and specimen temperature were chosen as input parameter. After that, phase volume fraction under non-isothermal condition was simulated again. Input parameters consist of initial phase volume fraction, equilibrium phase volume fraction at specific temperature, cooling rate, and temperature.The ANN model was coupled with the FE simulation in order to predict the variation of phase volume fraction during non-isothermal forging. Ti-6AI-4Y alloy was forged under isothermal and non-isothermal condition and then, the resulting microstructureswere compared with simulated data.

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Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network

Cited by 36 publications

References 28 publications

A Self-Consistent Approach for Modeling the Flow Behavior of the Alpha and Beta Phases in Ti-6Al-4V

A Self-Consistent Approach for Modeling the Flow Behavior of the Alpha and Beta Phases in Ti-6Al-4V

Estimation of Permeability of Green Sand Mould by Performing Sensitivity Analysis on Neural Networks Model

Artificial neural network modeling of phase volume fraction of Ti alloy under isothermal and non-isothermal hot forging conditions

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