Ductility prediction of Ti aluminide intermetallics through neuro-fuzzy set approach

Gupta, Rohit; Mehta, Rama; Agarwala, Vijaya; Pant, Bhanu; Sinha, P. P.

doi:10.1007/s12666-010-0127-5

Cited by 2 publications

(6 citation statements)

References 29 publications

(31 reference statements)

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“…Therefore, it can be inferred that selection of input data decides the output accuracy in ANFIS as well as in ANN models. Prediction from ANFIS approach [19] and present ANN approach is found to be supporting each other. It is also true that, if scattering of data is more, ANN models are more suitable, since it generates output by adjusting the interconnections between the layers.…”

Section: Resultssupporting

confidence: 56%

“…Analysis of the data has also been attempted through adaptive neuro fuzzy inference system (ANFIS) [19] using Takagi Sugeno model with substractive cluster approach. Through ANIFIS models, prediction accuracy is found to be depending on number of input variables with extent of data mixing [19].…”

Section: Resultsmentioning

confidence: 99%

“…Through ANIFIS models, prediction accuracy is found to be depending on number of input variables with extent of data mixing [19]. Therefore, it can be inferred that selection of input data decides the output accuracy in ANFIS as well as in ANN models.…”

Section: Resultsmentioning

confidence: 99%

“…It is also true that, if scattering of data is more, ANN models are more suitable, since it generates output by adjusting the interconnections between the layers. In addition to that, ANN models have less dependency on modeling parameters as noted in ANFIS models [19], so prediction accuracy shall be certainly better with ANN models.…”

Section: Resultsmentioning

confidence: 99%

“…During the last decade, there has been an increased interest in applying new emerging theoretical techniques such as fuzzy inference system (FIS) and artificial neural network (ANN) for optimization-related problems [17][18][19][20]. These are the most common data driven models.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Study on Ductility of Ti Aluminide Using Artificial Neural Network

Gupta

Mehta

Agarwala

et al. 2011

International Journal of Aerospace Engineering

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Improvement of ductility at room temperature has been a major concern on processing and application of Ti aluminides over the years. Modifications in alloy chemistry of binary alloy (Ti48 Al) and processing conditions were suggested through experimental studies with limited success. Using the reported data, the present paper aims to optimize the experimental conditions through computational modeling using artificial neural network (ANN). Ductility database were prepared, and three parameters, namely, alloy type, grain size, and heat treatment cycle were selected for modeling. Additionally, ductility data were generated from the literature for training and validation of models on the basis of linearity and considering the primary effect of these three parameters. Model was trained and tested for three different datasets drawn from the generated data. Possibility of improving ductility by more than 5% is observed for multicomponent alloy with grain size of 10-50 μm following a multistep heat treatment cycle.

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Section: Resultssupporting

confidence: 56%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on Ductility of Ti Aluminide Using Artificial Neural Network

Gupta

Mehta

Agarwala

et al. 2011

International Journal of Aerospace Engineering

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Study of electronic properties of Al3Ti, AlTi and AlTi3 intermetallic compounds using DFT-FPLAPW

Machado

Luiggi

2014

JCM

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The electronic properties of Al-Ti intermetallics are receiving great attention in the specialized literature because these alloys present excellent mechanical performance at high temperatures. This consideration prompted our DFT-LDA and DFT-GGA ab initio study on electronic band structures, density of states, contour plots, Fermi Surface, Bulk modulus, and cohesive energy of intermetallics Al3Ti, AlTi, and AlTi3 using the FP-LAPW method. The band structure confirms their electrical conductivity, AlTi3 featuring a highly localized band associated to Ti 3d electrons around the Fermi level, not observed in the others. The DOS around the Fermi level is dominated by interactions between Al 3p and Ti 3d orbitals, whereas Al 3s and Ti 3d orbital interactions prevail in the regions of low and high energies, respectively. The density pseudogap in AlTi3 is located very close to the Fermi Level, granting a greater structural stability to this system. The Fermi Surface of these intermetallics, for which the authors found no previous reference in the literature, is reported. The difference in density distribution for the LDA and GGA methods is reflected in the contour plots. The Bulk modulus and cohesive energy values reported are in agreement with those reported in the literature.The study of the electronic properties of the AlTi system [12][13][14][15][16] has gained great acclaim thanks to the system's structural stability, reflected in its capacity for cohesiveness, its enthalpy of formation, and its mechanical properties [17][18][19][20][21][22][23][24][25][26][27]; all of which underscore the claim that ductility depends on the nature of the chemical bond. This ductility is associated to the directionality of the covalent bond between the Al 3p and the Ti 3d orbitals. Thus, by regulating this directionality, the fragility of these systems at low temperatures can also be regulated [28][29][30][31].Hong et al. [12] used the LMTO method to study the structural properties of Al 3 Ti for D0 22 , D0 19 , and L1 2 structures, obtaining results for the energies of formation, Bulk modulus, and phase stabilities of the three structures, the most stable phase corresponding to D0 22 . In addition, Hong et al. studied the AlTi 3 intermetallic [13] and, besides finding structural and electronic properties, they compared the bonding strengths of Al 3 Ti and AlTi 3 , to conclude that the Ti-Ti interactions of the latter influence the stability of the structure.Asta et al. [14] presented an ab initio study of the composition-temperature phase diagram of the Al-Ti system, and studied some structural properties of FCC structures, obtaining values for the formation energy, Bulk modulus, and atomic volume. Gutiérrez et al. [15,16] studied the charge density topology of Al 3 Ti, AlTi, and AlTi 3 , using the Dmol 3 program included in the computational package Material Studio, and were able to establish a relationship between topological density effects and macroscopic properties such as ductility. The compounds were systematically character...

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Ductility prediction of Ti aluminide intermetallics through neuro-fuzzy set approach

Cited by 2 publications

References 29 publications

Study on Ductility of Ti Aluminide Using Artificial Neural Network

Study on Ductility of Ti Aluminide Using Artificial Neural Network

Study of electronic properties of Al3Ti, AlTi and AlTi3 intermetallic compounds using DFT-FPLAPW

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