Some aspects of Cold Deformation studies of Al-ZrB2 composites

Venkatesh, C.; Chaitanya, B.; Yadav, Khuvendra

doi:10.22161/ijaers.4.11.6

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The aspect ratios are varied between 0.4 -0.85. Venkatesh et al [22] manufactured the Al -ZrB2 composites powder metallurgical route using different proportions of powdered ZrB2, to study the aspect ratios 0.35, 0.5 and 0.65 are considered. As per the results formability stress is directly proportional to preform fractional density and inversely proportional to aspect ratio also very higher fracture strain obtained for preforms of min aspect ratios and high density.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigations of Al-Cr3C2 Composite Preform Densification and Deformation

Borra¹,

Davuluri²

2022

ACSM

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This investigation on strain hardening, densification and workability of the Sintered aluminium- Chromium Carbide composition of (Al-Cr3C2 of 2, 4 and 6%) preforms subjected to upsetting were investigated in this research. Industrial practitioners needed the workability data and densification mechanisms to plan and envisage the failure strains. In the current study, under triaxial stress state conditions Al-Cr3C2 preform with primary preform densities and various aspect ratios were compressed. Strain hardening, densification behaviors of aluminium- Chromium Carbide be investigated by gradually increasing the load till the fracture occurs. The outcome of adding Cr3C2 to Al and the impact of aspect ratio on formability was also extensively investigated. We looked at the parameters of stress ratio, instant varying strain rate, work hardening exponent, instantaneous density coefficient and densification attained.

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Section: Introductionmentioning

confidence: 99%

Experimental Investigations of Al-Cr3C2 Composite Preform Densification and Deformation

Borra¹,

Davuluri²

2022

ACSM

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Compression of Hyper Spectral Images using Tensor Decomposition Methods

Sucharitha

Sheela

2022

International Journal of Circuits, Systems and Signal Processing

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Tensor decomposition methods have beenrecently identified as an effective approach for compressing high-dimensional data. Tensors have a wide range of applications in numerical linear algebra, chemo metrics, data mining, signal processing, statics, and data mining and machine learning. Due to the huge amount of information that the hyper spectral images carry, they require more memory to store, process and send. We need to compress the hyper spectral images in order to reduce storage and processing costs. Tensor decomposition techniques can be used to compress the hyper spectral data. The primary objective of this work is to utilize tensor decomposition methods to compress the hyper spectral images. This paper explores three types of tensor decompositions: Tucker Decomposition (TD_ALS), CANDECOMP/PARAFAC (CP) and Tucker_HOSVD (Higher order singular value Decomposition) and comparison of these methods experimented on two real hyper spectral images: the Salinas image (512 x 217 x 224) and Indian Pines corrected (145 x 145 x 200). The PSNR and SSIM are used to evaluate how well these techniques work. When compared to the iterative approximation methods employed in the CP and Tucker_ALS methods, the Tucker_HOSVD method decomposes the hyper spectral image into core and component matrices more quickly. According to experimental analysis, Tucker HOSVD's reconstruction of the image preserves image quality while having a higher compression ratio than the other two techniques.

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Some aspects of Cold Deformation studies of Al-ZrB2 composites

Abstract: Abstract-In the present study Al-ZrB2 composite were made by powder metallurgy route adding different amounts of synthesized ZrB2 powder (2, 4, 6, 8, 10 wt. %)

Cited by 2 publications

References 7 publications

Experimental Investigations of Al-Cr3C2 Composite Preform Densification and Deformation

Experimental Investigations of Al-Cr3C2 Composite Preform Densification and Deformation

Compression of Hyper Spectral Images using Tensor Decomposition Methods

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