Evolution of crystallographic texture and microstructure in the orthorhombic phase of a two-phase alloy Ti–22Al–25Nb

Evolution of crystallographic texture in the orthorhombic phase of a two-phase alloy Ti–22Al–25Nb (at%), consisting of orthorhombic (O) and bcc (β/B2) phases, was studied. The material was subjected to deformation in two-phase field as well as in the single β phase field. The resulting evolution of microstructure and crystallographic texture were recorded using scanning electron microscopy and X-ray diffraction. The orthorhombic phase underwent change in morphology (from platelets to equiaxed) on rolling in the two-phase field with the texture getting sharper with the amount of deformation. Rolling above β transus temperature led to hot deformation of single β phase microstructure and its subsequent cooling produced transformed coarse platelets of orthorhombic phase with texture in orientation relation with the high temperature deformed β phase.     

Metallic micro displacement capacitive sensor fabricated by laser micromachining technology

This paper presents design, fabrication and performance testing results of the micro displacement capacitive microsensor fabricated by femtosecond laser machining technology. The microsensor having overall dimensions of 1,275 (W)×1,153 (L) μm consisted of 20 pairs of comb fingers of 24 (W)×300 (L) μm with a gap between the fingers of 6 μm, suspension springs, inertial mass and support anchors. The sensor structure was fabricated from a 25 μm thick tungsten foil. The fabricated microsensor was able to deliver 230 fF capacitance variations for measured displacements up to 25 μm. The results on the performance testing and geometry evaluation under pins the laser micromachining technology as an effective tool to fabricate miniature functional components and mechanisms. The developed microsensor can be used for micro/nano scale displacement measurements in MEMS applications.     

Evolution and stability of texture during thermomechanical processing of Ti-24Al-11Nb alloy

The evolution of basal texture during thermomechanical processing of Ti-24Al-11Nb alloy has been studied as a function of different processing variables like hot rolling temperature, amount of deformation, cooling conditions etc. The stability of the deformation texture during post-rolling annealing and during theα ₂→β→α ₂ phase transformation cycle was also investigated. Unrestricted rolling of primaryα ₂ to maximum thickness reduction at the lowest rolling temperature has been found to be most favourable for obtaining a good basal texture. Texture of transformed (secondary)α ₂ is generally non basal when the transformation takes place from deformedβ. Rolling texture does not seem to change during annealing leading to recrystallization. Theα ₂→β→α ₂ phase transformation cycle does not change the starting basal texture and a starting non basal texture also does not give rise to basal texture due to this treatment.     

Performance evaluation of a multi-product system under CONWIP control

Analytical models of multi-product manufacturing systems operating under CONWIP control are composed of closed queuing networks with synchronization stations. Under general assumptions, these queuing networks are hard to analyze exactly and therefore approximation methods must be used for performance evaluation. This research proposes a new approach based on parametric decomposition. Two-moment approximations are used to estimate the performance measures at individual stations. Subsequently, the traffic process parameters at the different stations are linked using stochastic transformation equations. The resulting set of non-linear equations is solved using an iterative algorithm to obtain estimates of key performance measures such as throughput, and mean queue lengths. Numerical studies indicate that the proposed method is computationally efficient and yields fairly accurate results when compared to simulation.     

Use of alumina dispersant in alumina-spinel castable: Comparison between in situ and preformed spinels

The role of alumina dispersant as a flow modifier, completely replacing fume silica, on the properties of preformed and in situ spinel-containing low cement high alumina castables was investigated with variations in spinel content and granulometry. Both 10 and 20 wt.% spinel-containing compositions were prepared using preformed spinel or magnesia in alumina castable compositions with vibratable and self-flowing consistency. The castables underwent conventional processing methods and accessed for various refractory-related parameters post-heat treatments. No glassy phase was observed in the castables upon firing. The presence of self-flowing consistency led to improved characteristics. Preformed spinel-containing compositions showed improved density, strength, hot strength, and thermal shock resistance properties, whereas slag corrosion and penetration resistances were found to be higher for in situ spinel-containing castable.     

Effect of Annealing Temperature in Al 1145 Alloy Sheets on Formability, Void Coalescence, and Texture Analysis

This paper deals with a combined forming and fracture limit diagram and void coalescence analysis for the aluminum alloy Al 1145 alloy sheets of 1.8 mm thickness, annealed at four different temperatures, namely 200, 250, 300, and 350 °C. At different annealing temperatures these sheets were examined for their effects on microstructure, tensile properties, formability, void coalescence, and texture. Scanning electron microscope (SEM) images taken from the fractured surfaces were examined. The tensile properties and formability of sheet metals were correlated with fractography features and void analysis. The variation of formability parameters, normal anisotropy of sheet metals, and void coalescence parameters were compared with texture analysis.     

Grain-Size Effects on the High-Temperature Oxidation of Modified 304 Austenitic Stainless Steel

The high-temperature oxidation behavior of modified 304 austenitic stainless steels in a water vapor atmosphere was investigated. Samples were prepared by various thermo mechanical treatments to result in different grain sizes in the range 8–30 μm. Similar Σ3 grain boundary fraction was achieved to eliminate any grain-boundary characteristics effect. Samples were oxidized in an air furnace at 700 °C with 20 % water vapor atmosphere. On the fine-grained sample, a uniform Cr₂O₃ layer was formed, which increased the overall oxidation resistance. Whereas on the coarse-grained sample, an additional Fe₂O₃ layer formed on the Cr-rich oxide layer, which resulted in a relatively high oxidation rate. In the fine-grained sample, grain boundaries act as rapid diffusion paths for Cr and provided enough Cr to form Cr₂O₃ oxide on the entire sample surface.     

Spatial correlation in grain misorientation distribution

Grain misorientation was studied in relation to the nearest neighbor’s mutual distance using electron back-scattered diffraction measurements. The misorientation correlation function was defined as the probability density for the occurrence of a certain misorientation between pairs of grains separated by a certain distance. Scale-invariant spatial correlation between neighbor grains was manifested by a power law dependence of the preferred misorientation vs. inter-granular distance in various materials after diverse strain paths. The obtained negative scaling exponents were in the range of ?2 ± 0.3 for high-angle grain boundaries. The exponent decreased in the presence of low-angle grain boundaries or dynamic recrystallization, indicating faster decay of correlations. The correlations vanished in annealed materials. The results were interpreted in terms of lattice incompatibility and continuity conditions at the interface between neighboring grains. Grain-size effects on texture development, as well as the implications of such spatial correlations on texture modeling, were discussed.     

Static and dynamic characteristics of short wavy journal bearings

A general analysis has been done to investigate the static and dynamic performance of short wavy journal bearing. Reynolds equation is solved for both steady‐ and unsteady‐state operations. The steady‐state analysis has been done and compared with circular bearing with respect to their load capacity, flow rate, pressure distribution and friction factor. The unsteady‐state analysis has been used to determine the rotor dynamic coefficients for various eccentricity ratios. These coefficients are used to determine the stability parameters, such as critical mass and whirl ratio, and prove that wave bearing has not only better load carrying capacity but also provides better stability at high speed than circular bearing. Copyright © 2010 John Wiley & Sons, Ltd.     

Neural network modeling and analysis of the material removal process during laser machining

To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.