Formation of adiabatic shear band in metal matrix composites

A modified single-pulse loading split Hopkinson torsion bar (SSHTB) is introduced to investigate adiabatic shear banding behavior in SiCp particle reinforced 2024 Al composites in this work. The experimental results showed that formation of adiabatic shear band in the composite with smaller particles is more readily observed than that in the composite with larger particles. To characterize this size-dependent deformation localization behavior of particle reinforced metal matrix composites (MMCp), a strain gradient dependent shear instability analysis was performed. The result demonstrated that high strain gradient provides a deriving force for the formation of adiabatic shear banding in MMCp. (C) 2004 Elsevier Ltd. All rights reserved.

Laser synthesizing NiAl intermetallic and TiC reinforced NiAl intermetallic matrix composite

The NiAl intermetallic layers and NiAl matrix composite layers with TiC particulate reinforcement were successfully synthesized by laser cladding with coaxial powder feeding of Ni/Al clad powder and Ni/Al + TiC powder mixture, respectively. With optimized processing parameters and powder mixture compositions, the synthesized layers were free of cracks and metallurgical bond with the substrate. The microstructure of the laser-synthesized layers was composed of 6-NiAl phase and a few gamma phases for NiAl intermetallic; unmelted TiC, dispersive fine precipitated TiC particles and refined beta-NiAl phase matrix for TiC reinforced NiAl intermetallic composite. The average microhardness was 355 HV0.1 and 538 HV0.1, respectively. Laser synthesizing and direct metal depositing offer promising approaches for producing NiAl intermetallic and TiC-reinforced NiAl metal matrix composite coatings and for fabricating NiAl intermetallic bulk structure. (C) 2004 Laser Institute of America.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.