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Optimization of T-6 cycle and Characterization of Si3N4 Reinforced High Strength Aluminum Metal Matrix Composites

Kumar Ashish, Rana Ravindra Singh, Purohit Rajesh, Namdev Anurag

Archives of Metallurgy and Materials

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2023

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Vol. 68, iss. 2

775--783

EN

In this research, AA7068/Si3N4 composites were fabricated through stir casting with the attachment of ultrasonic treatment. The quenching medium and aging duration significantly influenced the hardness of Al alloy samples. Peak hardness was achieved after 12 h of artificial aging at the temperature of 140°C. The addition of nano Si3N4 significantly refined the microstructure of unreinforced AA7068. The dispersion of intermetallic compounds (MgZn2) and grain boundary discontinuation were noticed after the T-6 heat treatment. Ultimate tensile strength, yield strength, and hardness were improved by 70.95%, 76.19%, and 44.33%, respectively, with the addition of 1.5 weight % Si3N4 compared to as-cast alloy due to the combined effect of heat treatment, hall-Petch, Orowan, thermal miss match, load-bearing strengthening mechanisms and uniform dispersion of reinforcement. A reduction in percentage elongation was noticed due to composites’ brittle nature by the effect of ceramic Si3N4 particles’ inclusion. The fracture surfaces reveal ductile failure for alloy and mixed-mode failure in the case of composites.

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Fabrication and Characterization of Al6061-Nano Al2O3 Composites by Ultrasonic Assisted Stir Casting followed by Heat Treatment and Hot Forging

Purohit Rajesh, Qureshi M.M.U., Kumar Ashish, Mishra Abhishek, Rana R.S.

Archives of Metallurgy and Materials

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2023

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Vol. 68, iss. 2

791--798

EN

The present work comprises the development of Al6061/nano Al2O3 composites with 0 to 4 weight percent in steps of 0.5 wt. % of nano alumina particles by using ultrasonic assisted stir casting. Casted samples were subjected to heat treatment and hot forging. Further forged and heat-treated gear blanks of nano Al2O3 (0 to 3.0 weight %) reinforced nanocomposites were machined to make spur gears for the wear test. The results have shown that nano Al2O3 reinforcement in the Al6061 matrix with heat treatment and forging improves the hardness and compressive strength up to 3.5 wt. %, after that, it starts decreasing because of the agglomeration of nano alumina particles. SEM results reveal grain refinement of the pure alloy after reinforcement. Removal of porosity and voids observed after forging operation. Wear resistance increasing with incorporation of Al2O3 nanoparticles in base alloy, reinforcement wt. %, precipitation hardening and hot forging also improves wear resistance and mechanical properties. These composites have widespread applications in gear, brake discs, crankshaft, clutch plates, pistons, and other components of automobiles and aircraft structures.

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Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals

Sharma Rishi Raj, Kumar Ashish, Pachori Ram Bilas, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

312--327

EN

Congestive heart failure (CHF) is a cardiac abnormality in which heart is not able to pump sufficient blood to meet the requirement of all the parts of the body. This study aims to diagnose the CHF accurately using heart rate variability (HRV) signals. The HRV signals are non-stationary and nonlinear in nature. We have used eigenvalue decomposition of Hankel matrix (EVDHM) method to analyze the HRV signals. The lowest frequency component (LFC) and the highest frequency component (HFC) are extracted from the eigenvalue decomposed components of HRV signals. After that, the mean and standard deviation in time domain, mean frequency calculated from Fourier-Bessel series expansion, k-nearest neighbor (k-NN) entropy, and correntropy features are evaluated from the decomposed components. The ranked features based on t-value are fed to least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel for automated diagnosis of CHF HRV signals. The study is performed on three normal datasets and two CHF datasets. Our proposed system has yielded an accuracy of 93.33%, sensitivity of 91.41%, and specificity of 94.90% using 500 HRV samples. The automated toolkit can aid cardiac physicians in the accurate diagnosis of CHF patients to confirm their findings with our system. Hence, it will help to provide timely treatment for CHF patients and save life.