Recrystallization behavior of boron-doped Ni76Al24

Considerable hardness recovery and almost complete restoration of order were observed during annealing of 85 pct cold-rolled boron-doped Ni₇₆Al₂₄ prior to recrystallization. Recrystallization kinetics were investigated over a wide range of temperatures at small intervals of transformed volume fraction. The variation of transformed volume fraction with time showed two unusual distinct regions characterized by separate Avrami exponents and activation energies. The exponent decreased from 2.2 at 800 °C to 0.7 at 950 °C for X > 0.6 and had a temperature-insensitive value of 3 for X > 0.6. The activation energy below 900 °C was 145 kJ/g atom for X < 0.6 and 110 kJ/g atom for X > 0.6, and that above 900 °C was about 435 kJ/g atom. Equations for the kinetics have been derived based on concurrent recovery in the untransformed regions during recrystallization. The predictions of the equations agree exceedingly well with the experimental results and yield separate values of the activation energies for interface migration and recovery.     

Splat cooling of aluminium-manganese alloys

The gun technique of splat cooling is utilized to extend the solid solubility of manganese in aluminium by a factor of four above that at the eutectic temperature. The supersaturated solid solutions can be retained up to 250° C without any significant decomposition. Isochronal and isothermal studies of the variation of the lattice parameter of the 6.4 wt % manganese solid solution yield a value for the activation energy of 30 kcal mol^?1 which is considerably higher than that for the splat cooled aluminium-silicon alloys. The decomposition of the supersaturated solid solution leads to the precipitation of the equilibrium Al₆Mn phase.     

Free convection in the laminar boundary layer flow of a thermomicropolar fluid past a vertical flat plate with suction/injection

Summary The effect of suction/injection in the laminar free convection flow of a thermomicropolar fluid past a nonuniformly heated vertical flat plate has been considered. The conditions under which similarity exists have been examined. The resulting system of non-linear ordinary differential equations has been solved numerically after transforming the infinite domain of boundary layer coordinate into a finite domain. The effects of variation of the boundary condition parameter and suction/injection parameter on the velocity, microrotation and temperature fields and the heat transfer coefficient have been studied graphically. The skin-friction parameter and the gradient of microrotation on the wall have been tabulated. It is found that there is significant increase in velocity, skin-friction and the heat transfer coefficient with the decreasing concentration of microelements.With 4 Figures     

MS.D.8: Electrical conductivity investigation of masonry

RILEM Technical CommitteesCategory D: In-Situ and Non-Destructive Test Proposed Test Method

MS.D.8: Electrical conductivity investigation of masonry     

Morphological feature extraction and KNG-CNN classification of CT images for early lung cancer detection

Lung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non-Gaussian Convolutional Neural Network (KNG-CNN). KNG-CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non-Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC-IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG-CNN method is used for effectual classification of tumour > 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.