Spark-Plasma Sintering of Silicon Carbide Whiskers (SiCw) Reinforced Nanocrystalline Alumina

The combined effect of rapid sintering by spark-plasma-sintering (SPS) technique and mechanical milling of γ-Al₂O₃ nanopowder via high-energy ball milling (HEBM) on the microstructural development and mechanical properties of nanocrystalline alumina matrix composites toughened by 20 vol% silicon carbide whiskers was investigated. SiC_w/γ-Al₂O₃ nanopowders processed by HEBM can be successfully consolidated to full density by SPS at a temperature as low as 1125°C and still retain a near-nanocrystalline matrix grain size (∼118 nm). However, to densify the same nanopowder mixture to full density without the benefit of HEBM procedure, the required temperature for sintering was higher than 1200°C, where one encountered excessive grain growth. X-ray diffraction (XRD) and scanning electron microscopy (SEM) results indicated that HEBM did not lead to the transformation of γ-Al₂O₃ to α-Al₂O₃ of the starting powder but rather induced possible residual stress that enhances the densification at lower temperatures. The SiC_w/HEBMγ-Al₂O₃ nanocomposite with grain size of 118 nm has attractive mechanical properties, i.e., Vickers hardness of 26.1 GPa and fracture toughness of 6.2 MPa·m^1/2.     

A distortion corrected single camera-based weight estimation technique for industrial objects

This paper describes a unique single camera-based dimensional measurement with a self-calibration method of image-based measurement. The system has been designed and implemented in one of the integrated steel plants in India. The purpose of the system is to obtain the frontal cross-sectional area of an ingot irrespective of its distance from the camera head. Automatic calibration is achieved by attaching a magnetic template of known area. This self-calibrating system is further refined to correct for the various distortions arising out of lens characteristics. The results obtained through field trials have been reported and found to be quite encouraging.     

Deformation mechanisms and tensile superplasticity in nanocrystalline materials

Nanocrystalline materials provide a unique opportunity to investigate deformation mechanisms at an extremely fine microstructural scale. An intriguing question has been whether the deformation mechanisms scale with grain size to the nanocrystalline range or whether there are fundamental changes/transitions. The observations of low-temperature and high-strain-rate superplasticity in nanocrystalline materials with some unique features open up new possibilities for scientific and technological advancements. R.S. Mishra earned his Ph.D. in metallurgy at the University of Sheffield in 1988. He is currently adjunct assistant professor at the University of California at Davis. Dr. Mishra is also a member of TMS. S.X. McFadden earned his B.S. in materials engineering at California Polytechnic State University, San Luis Obispo, in 1996. He is currently a research associate at the University of California at Davis. Mr. McFadden is also a member of TMS. R.Z. Valiev earned his Dr.Sci. in solid-state physics from the Institute for Materials Science, Academy of Sciences of the USSR, Kiev, in 1984. He is currently the scientific director and professor at the Institute of Physics of Advanced Materials, Ufa State Aviation Technical University, Russia. Dr. Valiev is also a member of TMS. A.K. Mukherjee earned his D. Phil. in materials science at Oxford University in 1962. He is currently a professor of materials science at the University of California at Davis. Dr. Mukherjee is also a member of TMS.     

Multiphase Model of Semisolid Slurry Generation and Isothermal Holding During Cooling Slope Rheoprocessing of A356 Al Alloy

Metallurgical and Materials Transactions B - In the present paper, we present an experimentally validated 3D multiphase and multiscale solidification model to understand the transport processes...     

Optimization of HV electrode systems by neural networks using a newlearning method

To avoid a large number of iterations, optimization of electrode shapes has been done by artificial neural networks (NN). Two practical examples have been considered, an axisymmetric single-phase GIS bus termination and an axisymmetric transformer shield ring. The shape of the electrodes has been taken as quarter-ellipse or half-ellipse because an ellipse has more flexibility than a circle. For NN, the so-called resilient propagation algorithm, learning faster than the standard back-propagation algorithm, has been employed. The training sets as well as the test sets of NN have been prepared by charge simulation method