AN INVESTIGATION OF CUTTING MECHANISMS AND STRAIN FIELDS DURING ORTHOGONAL CUTTING IN CFRPs

Fiber-reinforced plastics (FRPs) are typically difficult to machine due to their highly heterogeneous and anisotropic nature and the presence of two phases (fiber and matrix) with vastly different strengths and stiffnesses. Typical machining damage mechanisms in FRPs include series of brittle fractures (especially for thermosets) due to shearing and cracking of matrix material, fiber pull-outs, burring, fuzzing, fiber-matrix debonding, etc. With the aim of understanding the influence of the pronounced heterogeneity and anisotropy observed in FRPs, ``Idealized'' Carbon FRP (I-CFRP) plates were prepared using epoxy resin with embedded equispaced tows of carbon fibers. Orthogonal cutting of these I-CFRPs was carried out, and the chip formation characteristics, cutting force signals and strain distributions obtained during machining were analyzed using the Digital Image Correlation (DIC) technique. In addition, the same procedure was repeated on Uni-Directional CFRPs (UD-CFRPs). Chip formation mechanisms in FRPs were found to depend on the depth of cut and fiber orientation with pure epoxy showing a pronounced ``size effect.'' Experimental results indicate that in-situ full field strain measurements from DIC coupled with force measurements using dynamometry provide an adequate measure of anisotropy and heterogeneity during orthogonal cutting.

Thermo-optic Degradation of Single-Modedness in Active LMA fibers and Simple Compensation Mechanisms

We demonstrate significant thermo-optic degradation of single-modedness in active large mode area fibers due to heat generation in the fiber. We propose and demonstrate through simulations, simple compensation mechanisms using custom length dependent fiber coiling.

Effect of strain rate response and pin diameter on mechanically mixed layer formation and wear mechanisms in a Ti6Al4V-SS316L pair

Three mechanisms operate during wear of materials. These mechanisms include the Strain Rate Response (SRR - effect of strain rate on plastic deformation), Tribo-Chemical Reactions (TCR) and formation of Mechanically Mixed Layers (MML). The present work investigates the effect of these three in context of the formation of MML. For this wear experiments are done on a pin-on-disc machine using Ti64 as the pin and SS316L as the disc. It is seen that apart from the speed and load, which control the SRR and TCR, the diameter of the pin controls the formation of MML, especially at higher speeds.

Diffusion mechanisms in the Fe3ASi

In this paper, the possible reasons for the high thermal vacancy concentration and the low migration barriers for the Fe atom diffusion in the DO3 structure Fe3Si have been discussed.

Impact response and failure mechanisms of ultrahigh modulus polyethylene fiber composites and polyethylene fiber-carbon fiber hybrid composites

The impact response and failure mechanisms of ultrahigh modulus polyethylene (UHMPE) fiber composites and UHMPE fiber-carbon fiber hybrid composites have been investigated. Charpy impact, drop weight impact and high strain rate impact experiments have been performed in order to study the impact resistance, notch sensitivity, strain rate sensitivity and hybrid effects. Results obtained from dynamic and quasi-static measurements have been compared. Because of the ductility of UHMPE fibers, the impact energy absorption of UHMPE fiber composites is very high, thereby leading to excellent damage tolerance. By hybridizing with UHMPE fibers, the impact properties of carbon fiber composites can be greatly improved. The impact and shock failure mechanisms of these composites are discussed.

Ocean Waves Propagating over a Porous Seabed: Residual and Oscillatory Mechanisms

Two mechanisms for the wave-induced pore pressures in a porous seabed, i.e. oscillatory and residual excess pore pressures, have been observed in laboratory experiments and field measurements. Most previous investigations have focused on one of the mechanisms individually. In this paper, an analytical solution for the wave-induced residual pore pressure, which is not available yet, is derived, and compared with the existing experimental data. With the new solution, a parametric analysis is performed to clarify the applicable ranges of two mechanisms. Then, a simplified approximation for the prediction of wave-induced liquefaction potential is proposed for engineering practice.

Deformation Twinning Mechanisms in Nanocrystalline Ni

Extensive transmission electron microscopy examinations confirm that twinning does occur upon large plastic deformation in nanocrystalline Ni, for which no sign of deformation twinning was found in previous tensile tests. Compelling evidence has been obtained for several twinning mechanisms that operate in nanocrystalline grains, with the grain boundary emission of partial dislocations determined as the most proficient. (c) 2006 American Institute of Physics.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.