Fragmentation processes during explosion welding (review)

Greenberg, B. A.; Иванов, М. А.; Рыбин, В. В.; Elkina, O. A.; Пацелов, А. М.; Антонова, О. В.; Inozemtsev, A. V.; Tolmachev, T. P.

doi:10.1134/s0036029513100030

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…Sharp transitions from one non-planar core configuration to another, being thermally activated, yield the normal temperature dependence σ y (T). No dislocation selfblocking on stress-free heating after preliminary Fe and Mo strain was observed in [2], which confirmed once again the necessity of the two-valley relief for dislocation blocking without external stress.…”

Section: Possibility Of Self-blocking In Different Materialssupporting

confidence: 82%

“…We consider that this behavior of σ y (T) is possible only for the two-valley relief. The self-blocking of single dislocations observed in [2] confirms this concept, but the problem of the second valley (more deep or, on the contrary, less deep) remains unsolved.…”

Section: Possibility Of Self-blocking In Different Materialssupporting

confidence: 50%

“…Finally, this is due to the anisotropy of the antiphase boundary (APB) energy leading to the existence of two superdislocation configurations: the equilibrium configuration with the APB band in the cubic plane and the metastable configuration in the octahedral plane. The relief becomes two-valley one [2]. The equilibrium configurations are rectilinear and blocked ones in the region of anomalous behavior of σ y (T) and during self-blocking; in other cases, curvilinear dislocations are observed.…”

Section: Possibility Of Self-blocking In Different Materialsmentioning

confidence: 95%

“…The blocked segments are typically screw ones, but not always: they are 30-degree for superdislocations with the Burgers vector 1/2<112> in TiAl [2].…”

Section: Two-valley Potential Relief Of the (C + A) Dislocationmentioning

confidence: 99%

“…The self-blocking was proved by dislocation extension along the preferred direction and observation of rectilinear dislocations after stress-free heating. The self-blocking of dislocations in Ni 3 (Al, Nb) and TiAl intermetallides [2] as well as in Ni 3 Ge [3] was established. It is well known [4,5] that in these intermetallides, the anomalous temperature behavior of the yield stress σ y (T) is observed.…”

Section: Introductionmentioning

confidence: 99%

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Detection of (C + A)-type dislocation self-blocking in magnesium

et al. 2011

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The self-blocking effect predicted theoretically and then observed in intermetallides is established for a pure metal, namely, for magnesium. This effect can be observed only for magnesium single crystals whose axis is parallel to the c axis and whose yield stress behavior σ y (T) has a temperature anomaly. For such single crystals, the self-blocking of the (c + a)-type edge dislocations is established during pyramidal slip of type II. The self-blocking is proved by dislocation extension along the preferred direction without external stress. In this case, the < 1100 > directions appear preferred. TEM images of (c + a) dislocations extended along the preferred directions are presented. It is demonstrated that two effects -temperature anomaly of σ y (T) and dislocation self-blocking -have the common nature: a two-valley potential relief of the dislocation. A model of two-valley relief of the (c + a) dislocations in Mg is proposed.

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Section: Possibility Of Self-blocking In Different Materialssupporting

confidence: 82%

Section: Possibility Of Self-blocking In Different Materialssupporting

confidence: 50%

Section: Possibility Of Self-blocking In Different Materialsmentioning

confidence: 95%

“…The blocked segments are typically screw ones, but not always: they are 30-degree for superdislocations with the Burgers vector 1/2<112> in TiAl [2].…”

Section: Two-valley Potential Relief Of the (C + A) Dislocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of (C + A)-type dislocation self-blocking in magnesium

et al. 2011

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Microheterogeneous Structure of Local Melted Zones in the Process of Explosive Welding

Greenberg

Иванов²,

Inozemtsev³

et al. 2015

Metall Mater Trans A

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Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

Gebretsadik,

Kumar,

Fissha

et al. 2024

Discov Appl Sci

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The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in open pit mining. While various parameters can affect rock fragmentation, this study considers ten among them (i.e., spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) to train and test the models. However, due to a weak correlation with rock fragmentation, drill diameter, average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features including the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. Machine learning models (random forest regression, support vector regression, and XG boost), as well as a deep learning model (neural network regression), are applied to develop a practical way that can optimize the prediction of fragmentation. This study employs performance measures such as R-squared, RMSE, MSE, MAPE, and MAE. The optimization of the model revealed promising results, indicating that the architecture 5-64-32-16-1 exhibits strong performance. Specifically, the model achieved mean squared error (MSE) values of 41.32 and 28.59 on the training and test datasets, respectively. The R2 value for both training and test is 0.83. RFR is also performing well compared to SVR and XG boost with MSE values of 12.37 and 9.89 on training and testing data, respectively.in both sets, the R2 value is 94%. Based on permutation importance and shapely plot values, it is observed that the powder factor has the highest impact, while the burden has the lowest impact on fragmentation.

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Fragmentation processes during explosion welding (review)

Cited by 13 publications

References 7 publications

Detection of (C + A)-type dislocation self-blocking in magnesium

Detection of (C + A)-type dislocation self-blocking in magnesium

Microheterogeneous Structure of Local Melted Zones in the Process of Explosive Welding

Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

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