Evolution of Artificial Neural Network (ANN) model for predicting secondary dendrite arm spacing in aluminium alloy casting

Rao, D. Hanumantha; Tagore, G.R.N.; Janardhana, G. Ranga

doi:10.1590/s1678-58782010000300011

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Usually, an empirical equation is used to calculate the final SDAS: λ 2 (t 0 ) = K[t 0 ] 1 3 . The local solidification time can be determined directly from the cooling curve or the average solid/liquid interface velocity and temperature gradient, considering the solidification temperature interval, ∆T.…”

Section: Discussionmentioning

confidence: 99%

“…Most alloys consist of a dendritic microstructure entirely (wrought alloys) or partly (cast alloys) after solidification. In the case of wrought alloys, the homogenization time of microsegregation depends on the secondary dendrite arm spacing; it increases with the increase in the secondary dendrite arm spacing by the square law [1], while the physical properties (electric and heat conduction), chemical properties (corrosion) [2], mechanical properties (hardness, yield strength, final tensile strength, and elongation) [3,4], and performance of cast materials directly depend on the parameters of the dendritic microstructure, such as primary (PDAS) and secondary (SDAS) dendrite arm spacing. The part of the dendritic structure with the most volume consists of secondary dendrite arms, and the properties mentioned before mainly depend on the SDAS.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Dynamical and Empirical Simulation Methods of Secondary Dendrite Arm Coarsening

et al. 2022

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The physical and mechanical properties of an entirely (wrought alloys) or partly (cast alloys) dendritically solidified alloy strongly depend on the secondary dendrite arm spacing (SDAS). The casting practice and the simulation of solidification need a usable but simple method to calculate the SDAS during and at the end of solidification as a function of the cooling rate. Based on many solidification experiments, a simple equation to calculate the SDAS (empirical method) is known to use the local solidification time, which can be obtained from the measured cooling curves (equiaxed solidification), or can be calculated from the temperature gradient and front velocity (directional solidification). This equation is not usable for calculating the SDAS during solidification. Kirkwood developed a semi-empirical method based on the liquid phase’s diffusion, which contains only one geometric factor that seems constant for different alloys. This equation contains some physical parameters that depend on the temperature, so the equation cannot be integral in closed form. In the present work, first, we show the effect of the curvature of the solid/liquid interface on the equilibrium concentrations and then the different processes of SDA coarsening. In our earlier paper, we demonstrated that using the empirical method, the final SDAS can be calculated with acceptable correctness in the case of four unidirectional solidification experiments of Al-7wt%Si alloy. The present work shows that numerically integrated Kirkwood’s equations used the known cooling curve; the SDAS can be calculated at the end and during solidification in good agreement with these experimental results. Compared to the two calculation methods, we stated that the correctness of the methods is similar. Still, the results of the solidification simulation (the microsegregation) will be more correct using the dynamical method. It is also shown that with the dynamical method, the SDAS can be calculated from any type of cooling curve, and using the dynamical method, it is proved that some different SDASs could belong to the same local solidification time.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Dynamical and Empirical Simulation Methods of Secondary Dendrite Arm Coarsening

et al. 2022

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“…It has lowered Cr content and is alloyed with Co. For structural characteristics, the quantitative metallography methods have been used, as fallow:  The Saltykov rectangle method (Saltykov, 1958) for grain size evaluation has been used and ASTM E112-12 standard for grain size number determination.  For dendrite microstructure evaluation of cast alloys the SDAS factor calculation was used (Hanumantha Rao et. al, 2010).…”

Section: Experimental Materials and Methodsmentioning

confidence: 99%

The Quality Assurance of Cast and Wrought Aero Jet Engine Components Made from Ni-base Superalloys with Using of Quantitative Metallography Methods and Alloys Lifetime Prediction

Belán

Kuchariková

Mazur

et al. 2019

Quality Production Improvement - QPI

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The Ni-base superalloys are used in the aircraft industry for the production of aero engine most stressed parts, turbine blades or turbine discs. Quality of aero jet engine components has a significant influence on the overall lifetime of a jet engine as itself as well as the whole airplane. From this reason a dendrite arm spacing, grain size, morphology, number and value of γ′-phase are very important structural characteristics for blade or discs lifetime prediction. The methods of quantitative metallography are very often used for evaluation of structural characteristics mentioned above. The high-temperature effect on structural characteristics and application of quantitative methods evaluation are presented in this paper. The two different groups of Ni-base alloys have been used as experimental material: cast alloys ZhS6K and IN713LC, which are used for small turbine blades production and wrought alloys EI 698VD and EI 929, which are used for turbine disc production. Selected alloys have been evaluated in the starting stage and after applied heat-treatment at 850°C for 24 hrs. This applied heat-treatment causes structural changes in all alloys groups. In cast alloy dendritic structure is degraded and gamma prime average size has grown what has a negative influence on turbine blade creep rupture life. Wrought alloys show partially grain boundary melting and grain size changed due to recrystallization what causes mechanical properties decreasing – ultimate tensile strength mainly.

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“…Quality control, SDAS in particular, could also be performed by Light Optical Microscopy (LOM) of polished samples, but SDAS evaluation still relies on manual measurements. Although there exists research tackling the problem of SDAS prediction using ANNs [21], SDAS is not predicted directly from the microstructure image. Instead, the authors predicted SDAS based on processing parameters: pouring temperature, insulation on the riser and chill specific heat, while the dataset was based on numerical simulation results.…”

Section: Introductionmentioning

confidence: 99%

Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys

Nikolić

Štajduhar

Čanađija

2021

Metals

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This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction that has industrially acceptable prediction accuracy. The model was trained on images of polished samples of high-pressure die-cast alloy EN AC 46000 AlSi9Cu3(Fe), the gravity die cast alloy EN AC 51400 AlMg5(Si) and the alloy cast as ingots EN AC 42000 AlSi7Mg. Color images were converted to grayscale to reduce the number of training parameters. It is shown that a relatively simple CNN structure can predict various SDAS values with very high accuracy, with a R2 value of 91.5%. Additionally, the performance of the model is tested with materials not used during training; gravity die-cast EN AC 42200 AlSi7Mg0.6 alloy and EN AC 43400 AlSi10Mg(Fe) and EN AC 47100 Si12Cu1(Fe) high-pressure die-cast alloys. In this task, CNN performed slightly worse, but still within industrially acceptable standards. Consequently, CNN models can be used to determine SDAS values with industrially acceptable predictive accuracy.

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Evolution of Artificial Neural Network (ANN) model for predicting secondary dendrite arm spacing in aluminium alloy casting

Cited by 10 publications

References 7 publications

Comparison of Dynamical and Empirical Simulation Methods of Secondary Dendrite Arm Coarsening

Comparison of Dynamical and Empirical Simulation Methods of Secondary Dendrite Arm Coarsening

The Quality Assurance of Cast and Wrought Aero Jet Engine Components Made from Ni-base Superalloys with Using of Quantitative Metallography Methods and Alloys Lifetime Prediction

Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys

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