This paper aims to establish the mechanical, corrosion and failure analysis using online acoustic emission of Al7075 alloy, Al7075-B 4 C composite and Al7075-B 4 C-Al 2 O 3 hybrid composite with different Boron Carbide (B 4 C) content of 5, 10, 15 & 20% and Nano Aluminium Oxide (Al 2 O 3 ) content of 2% on a weight basis with the high energy stir casting method. The casted samples have been characterized by x-ray Diffraction (XRD), Thermo gravimetric analysis (TGA/DSC), Energy Dispersive Spectrum (EDS), and Scanning Electron Microscope (SEM). In case of hybrid composite, the hardness and the tensile strength decrease when the content of Al 2 O 3 increases. However, in the present research, the addition of B 4 C with nano Al 2 O 3 particles in certain proportions has increased the hardness and tensile strength. In addition, the tensile fractographs of the specimens were analysed using SEM. Acoustic Emission (AE) method was used for monitoring the acoustic energy that are released at the time of deformation process and early crack detection. The influence of the volume fraction of the B 4 C particulates on the microstructural and corrosion characteristics of Al7075-B 4 C with nano Al 2 O 3 metal matrix composites (MMCs) was also studied. It has been observed from the literature that the direct strengthening of composites occurs due to the presence of hard ceramic phase, while the indirect strengthening arises from the thermal mismatch between the matrix alloy and reinforcing phase during solidification. Based on the database for material properties, the application area of HAMCs has been proposed in the present review. The effects of nanomaterial dispersion in the metal matrix and the formation of interfacial precipitates on these properties are also addressed. Particular attention is paid to the fundamentals and the structure-property relationships of such novel Nano composites.
The size minimization of Titanium Carbide (TiC) Particles was done by high energy mechanical milling. Later Al & TiC powders were mixed to frame cylindrical preforms with 95% density using a die set. The cylindrical preform was sintered in a muffle
After cataract, glaucoma is one of the second leading retinal diseases in the world. This paper presents the methodology to detect the glaucoma using principal component analysis. The images are involved in dilation as a preprocessing, enhancement using the contrast limited adaptive histogram equalization method, and followed by the extraction of features using principal component analysis. The extracted features are classified using support vector machine, Naive Bayes, and K-nearest neighbor classifiers. Comparing with other classifiers, the Naive Bayes provides high accuracy of 95% which demonstrates the effectiveness of the feature extraction and the classifier.
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