Investigation of Creep Feed Grinding Parameters and Heat treatment Effects on the Nickel-base Superalloys

The Nickel base Superalloys are the most famous complicated and useable of Superalloys to make hot zone components of the gas turbines. The complicated dimensional tolerances, specially at the root of the blade show importance of grinding processes at the production of blades root. The prediction of the effect of machining parameters on the soundness of component surface strengthening for reaching to a suitable surface finishing and avoiding from crack formation at the work part during machining operation often is not easy and feasible so needs to more industrial investigation.This research is about frame 5 blade designed by GE and made from Superalloy IN738LC has been investigated. The formation of a plastically deformed and heat affected zone during grinding of Superalloy IN738LC with a high depth of cut but slow work speed (creep feed grinding) was investigated. Parameters such as work speed, depth of cut and radial dressing speed have been considered as variables and their effects have been studied. During experimental performed, the voltage and current of motor measured and power and special energy calculated.Some samples heat-treated (of the 1176℃ for 1 hr under neutral argon gas and cooling rate of 15℃/min up to 537℃ and then air cooling) to study grains recrystallization. Other samples have been created from the roots of blades and then coated by Nickel to measure boundary layer micro-hardness. The results show that increasing work speed leads to increasing the use power. Increasing the depth of cut, by increasing material removal rate, and the radial dressing speed, by decreasing power, lead to decreasing special energy. The temperature created by grinding lead to decreasing plastic deformation and boundary layer formation. When the radial dressing speed changes from 1 to 0.6μm/rev and other parameters are kept unchanged the roughness of surface increases and the special energy decreases. Sufficient dressing is very essential in limiting the width of the molten zone to few micrometers. As a result, it was found that local melting at contact spots to be a rather common mechanism during grinding of superalloys, lead to so-called white layers which can easily be observed on metallographic cross sections.     

Inhibiting effects of benzotriazole on the corrosion of α-Al-bronze in saline water

The inhibiting effect of benzotriazole (BTA) on the corrosion of -Al-bronze (Cu-7% Al) in 3.4% NaCl was studied. BTA showed good inhibition effects from short up to extended periods of time (about six weeks). The morphologies of the alloy surface were monitored after various periods of corrosion in the absence and presence of BTA, using SEM. The corrosion products were identified by X-ray diffraction. Corrosive attack occurs very early in the absence of BTA, leading to general and pitting corrosion. BTA was found to have a stronger inhibiting effect on the anodic dissolution of copper than on the cathodic reduction of oxygen. The current-potential relation is divided into two regions: region I within which BTA has a strong effect on the charge transfer kinetics, and a limiting current region where BTA has no significant inhibiting effect. It is also shown that the interaction of BTA with a Cu₂0-covered alloy surface is faster than on reduced alloy surfaces, although the protection efficiency on the latter is slightly better than on the former.     

Interface evolution in the platelet-like SiC@C and SiC@SiO<Subscript>2</Subscript> monocrystal nanocapsules

Carbon-coated SiC@C nanocapsules (NCs) with a hexagonal platelet-like morphology were fabricated by a simple direct current (DC) arc-discharge plasma method.The SiC@C NCs were monocrystalline,120-150 nm in size,and approximately 50 nm thick.The formation of the as-prepared SiC@C NCs included nucleation of truncated octahedral SiC seeds and subsequent anisotropic growth of the seeds into hexagonal nanoplatelets in a carbon-rich atmosphere.The disordered carbon layers on the SiC@C NCs were converted into SiO2 shells of SiC@SiO2 NCs by heat treatment at 650 ℃ in air,during which the shape and inherent characteristics of the crystalline SiC core were obtained.The interface evolution from carbon to SiO2 shells endowed the SiC@SiO2 NCs with enhanced photocatalytic activity due to the hydrophilic and transparent nature of the SiO2 shell,as well as to the photosensitive SiC nanocrystals.The band gap of the nanostructured SiC core was determined to be 2.70 eV.The SiC@SiO2 NCs degraded approximately 95％ of methylene blue in 160 min under visible light irradiation.     

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged. Hence, in this paper, it is proposed that the DWT method can be implemented on a field-programmable gate array in a digital architecture (FPGA-DA). We examined the effects of quantization on DWT performance in classification problems to demonstrate its reliability concerning fixed-point math implementations. The Advanced Encryption Standard (AES) algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks (ANN) method. By reducing hardware area by 57%, the proposed system has a higher throughput rate of 88.72%, reliability analysis of 95.5% compared to the other standard methods.     

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

A multilayer feedforward neural network with two hidden layers was designed and developed for prediction of the phosphorus content of electroless Ni–P coatings. The input parameters of the network were the pH, metal turnover, and loading of an electroless bath. The output parameter was the phosphorus content of the electroless Ni–P coatings. The temperature and molar rate of the bath were constant ( 91^° \textC, 0.4 \textNi^{\text + +} /\textH₂ \textPO₂ ^{- -} 91^\circ {\text{C}},\:0.4\,{\text{Ni}}^{{{\text{ + + }}}} /{\text{H}}_{2} {\text{PO}}_{2}^{{ - - }} ). The network was trained and tested using the data gathered from our own experiments. The goal of the study was to estimate the accuracy of this type of neural network in prediction of the phosphorus content. The study result shows that this type of network has high accuracy even when the number of hidden neurons is very low. Some comparison between the network’s predictions and own experimental data are given.     

A comparative study of kriging variants for the optimization of a turbomachinery system

Kriging is a well-established approximation technique for deterministic computer experiments. There are several Kriging variants and a comparative study is warranted to evaluate the different performance characteristics of the Kriging models in the computational fluid dynamics area, specifically in turbomachinery design where the most complex flow situations can be observed. Sufficiently accurate flow simulations can take a long time to converge. Hence, this type of simulation can benefit hugely from the computational cheap Kriging models to reduce the computational burden. The Kriging variants such as ordinary Kriging, universal Kriging and blind Kriging along with the commonly used response surface approximation (RSA) model were used to optimize the performance of a centrifugal impeller using CFD analysis. A Reynolds-averaged Navier–Stokes equation solver was utilized to compute the objective function responses. The responses along with the design variables were used to construct the Kriging variants and RSA functions. A hybrid genetic algorithm was used to find the optimal point in the design space. It was found that the best optimal design was produced by blind Kriging, while the RSA identified the worst optimal design. By changing the shape of the impeller, a reduction in inlet recirculation was observed, which resulted into an increase in efficiency.     

Disassembly Sequencing Using Tabu Search

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included     

Array algorithms for H∞ estimation

We develop array algorithms for H^∞ filtering. These algorithms can be regarded as the Krein space generalizations of H ² array algorithms, which are currently the preferred method fur implementing H² filters. The array algorithms considered include two main families: square-root array algorithms, which are typically numerically more stable than conventional ones, and fast array algorithms which, when the system is time-invariant, typically offer an order of magnitude reduction in the computational effort. Both have the interesting feature that one does not need to explicitly check for the positivity conditions required for the existence of H^∞ filters, as these conditions are built into the algorithms themselves. However, since H^∞ square-root algorithms predominantly use J-unitary transformations, rather than the unitary transformations required in the H² case, further investigation is needed to determine the numerical behavior of such algorithms     

A fast and compact elimination method of empty elements from a double‐array structure

A double‐array is a well‐known data structure to implement the trie. However, the space efficiency of the double‐array degrades with the number of key deletions because the double‐array keeps empty elements produced by the key deletion. This paper presents a fast and compact elimination method of empty elements using properties of the trie nodes that have no siblings. The present elimination method is implemented by C language. From simulation results for large sets of keys, the present elimination method is about 30–330 times faster than the conventional elimination method and maintains high space efficiency. Copyright © 2003 John Wiley & Sons, Ltd.