Obtaining NiAl intermetallic compound using different milling devices

NiAl intermetallic compound was synthesized by mechanical alloying technique in planetary and attritor mills. The starting powders consisted of elemental mixtures of Ni and Al at Ni50Al50(at%) composition. In the planetary mill, compound formation occurred gradually during mechanical alloying, while the occurrence of a mechanically induced self-propagating reaction (MSR) can be suggested in the attritor mill. The NiAl obtained in both mill types was partially disordered with long-range order parameter not inferior to 0.66. Quantitative phase analysis using the Rietveld method was performed in as-milled samples, and this method was also employed to estimate changes in crystallite size and lattice strain of the NiAl produced during mechanical alloying. (C) 2011 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

Electromagnetic torque ripple and copper losses reduction in permanent magnet synchronous machines

This paper presents a method for electromagnetic torque ripple and copper losses reduction in (non-sinusoidal or trapezoidal) surface-mount permanent magnet synchronous machines (SM-PMSM). The method is based on an extension of classical dq transformation that makes it possible to write a vectorial model for this kind of machine (with a non-sinusoidal back-EMF waveform). This model is obtained by the application of that transformation in the classical machine per-phase model. That transformation can be applied to machines that have any type of back-EMF waveform, and not only trapezoidal or square-wave back-EMF waveforms. Implementation results are shown for an electrical converter, using the proposed vectorial model, feeding a non-sinusoidal synchronous machine (brushless DC motor). They show that the use of this vectorial mode is a way to achieve improvements in the performance of this kind of machine, considering the electromagnetic torque ripple and copper losses, if compared to a drive system that employs a classical six-step mode as a converter. Copyright (C) 2011 John Wiley & Sons, Ltd.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Surface integrity analysis when milling ultrafine-grained steels

This paper quantifies the effects of milling conditions on surface integrity of ultrafine-grained steels. Cutting speed, feed rate and depth of cut were related to microhardness and microstructure of the workpiece beneath machined surface. Low-carbon alloyed steel with 10.8 µm (as-received) and 1.7 µm (ultrafine) grain sizes were end milled using the down-milling and dry condition in a CNC machining center. The results show ultrafine-grained workpiece preserves its surface integrity against cutting parameters more than the as-received material. Cutting speed increases the microhardness while depth of cut deepens the hardened layer of the as-received material. Also, deformations of microstructure following feed rate direction were observed in workpiece subsurface.

On Ti-18Si-6B and Ti-7.5Si-22.5B powder alloys prepared by high-energy ball milling and sintering

Recently, a new ternary phase was discovered in the Ti-Si-B system, located near the Ti6Si2B composition. The present study concerns the preparation of titanium alloys that contain such phase mixed with α-titanium and other intermetallic phases. High-purity powders were initially processed in a planetary ball-mill under argon atmosphere with Ti-18Si-6B and Ti-7.5Si-22.5B at. (%) initial compositions. Variation of parameters such as rotary speed, time, and ball diameters were adopted. The as-milled powders were pressureless sintered and hot pressed. Both the as-milled and sintered materials were characterized by X-ray diffraction, scanning electron microscopy and energy-dispersive spectrometry. Sintered samples have presented equilibrium structures formed mainly by the α-Ti+Ti6Si2B+Ti5Si3+TiB phases. Silicon and boron peaks disappear throughout the milling processes, as observed in the powder diffraction data. Furthermore, an iron contamination of up to 10 at. (%) is measured by X-ray spectroscopy analysis on some regions of the sintered samples. Density, hardness and tribological results for these two compositions are also presented here.