Investigation of Microstructure Properties and Quantitative Metallography by Different Etchants in the Service-Exposed Nickel-Based Superalloy Turbine Blade

In this study, the effect of etchant type and etching conditions on the root and airfoil microstructure of a service-exposed IN738 turbine blade has been investigated. The microstructure of superalloy components used at high temperatures, in addition to the usual microstructural changes, experiences deterioration in micrometer dimensions. In order to investigate these changes, electrochemical etching was performed on the samples with the chemical solution including 80% phosphoric acid, solution containing Cr₂O₃ and 55% glycerol. Chemical etching was performed with marble and etchant solution containing 60% glycerol. The results in terms of specifying the deterioration effects on microstructure of the blade applied at high temperature, the amount of γ′ phase and the best etchant were investigated. Among the solutions used for chemical etching, the solution containing 10 ml HNO₃, 50 ml HCl and 60 ml glycerol was appropriate for detection of segregations and dendrites, and among the electrochemical etching solutions, the Cr₂O₃ solution was found suitable for specifying γ′ precipitates’ morphology by scanning electron microscopy. In this research, the results of the quantitative analysis of the images provided by these etchants were also investigated.     

Fabricating boron nitride nanosheets from hexagonal BN in water solution by a combined sonication and thermal-assisted hydrolysis method

Boron nitride nanosheets (BNNSs) have unique and excellent thermal, electrical, and mechanical properties. But, their low efficiency in the production methods restricts their applications in the study of researchers. Hence, we reported a study in which BNNSs were successfully produced through a simple, affordable, and high-efficiency method. In this approach, hexagonal boron nitride block (h-BN) was firstly examined in aqueous solution under sonication (120 min) to produce hydroxyl functionalization and; furthermore, to penetrate water molecules between the layers. Then, with the explosion of the water molecules into the h-BN layers in the presence of heat, the space between the layers was increased. Finally, with the exfoliation of bulk h-BN layers through the ultrasonic irradiation, the ultrathin BNNSs were produced with an efficiency of 37%. The TEM and AFM images confirmed that the obtained BNNSs have mainly consisted of nanosheets with a thickness in the range of 3–8 nm. The results of UV–Vis analysis of BNNSs compared to h-BN showed a strong absorption peak at 204 nm^?1, which confirmed the presence of nanosheets. Also, other analyses, including XRD, BET, and FT-IR, confirmed the structure of BNNSs.     

On Infinite Horizon Optimal Control Problems with a Feed Forward Neural Network Scheme

Neural Processing Letters - In this paper, a class of infinite-horizon nonlinear optimal control problems is considered. The main idea is to convert the infinite horizon problem to an equivalent...     

Energy‐cost‐aware flow shop scheduling considering intermittent renewables,energy storage,and real‐time electricity pricing

The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on‐site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2‐stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on‐site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day‐ahead and real‐time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day‐ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real‐time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real‐time electricity supplied by the real‐time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on‐site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables.     

Enhancing multivariate pattern analysis for magnetoencephalography through relevant sensor selection

While the popularity of multivariate pattern classification is growing rapidly in magnetoencephalography (MEG) data analysis, the analysis pipelines used by the neuroscience community are still missing some fundamental machine-learning techniques and principles that would increase their effectiveness. Here, we show that MEG decoding accuracy improves significantly with the addition of feature selection methods to the analysis pipeline. We compare one unsupervised and two supervised feature reduction methods in the current study. Our results show that supervised feature selection methods like statistical dependency and mutual information improve decoding performance and attain higher session-to-session reliability compared to unsupervised dimensionality reduction methods like principal component analysis. Furthermore, we demonstrate that the selected sensors in the data related to a visual task at each time point are consistent with the pattern reflecting the sweep of information in the ventral visual pathway.     

Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features

In this study, a new procedure based on computer vision was developed for qualitative classification of black tea. Images of 240 samples from four different classes of black tea, including Orange Pekoe (OP), Flowery Orange Pekoe (FOP), Flowery Broken Orange Pekoe (FBOP), and Pekoe Dust One (PD-ONE), were acquired and processed using a computer vision system. Eighteen color features, 13 gray-image texture features, and 52 wavelet texture features were extracted and assessed. Two common heuristic feature selection methods: correlation-based feature selection (CFS) and principal component analysis (PCA), were used for selecting the most significant features. Seven of the primary features were selected by CFS as the most relevant ones, while PCA converted the original variables into 11 independent components. These final discriminatory vectors were evaluated by using four different classification methods including decision tree (DT), support vector machine (SVM), Bayesian network (BN), and artificial neural networks (ANN) to predict the qualitative category of tea samples. Among the studied classifiers, the ANN with 7–10–4 topology developed by CFS-selected features provided the best classifier with a classification rate of 96.25%. The other methods assayed provided slightly lower accuracies than ANN from 86.25% for BN till 87.50% for SVM and 88.75% for DT. In all the cases, the accuracy of the classifiers increased when using the CFS-selected features as input variables in front of PCA obtained ones. It can be concluded that image-based features are strong characterizing factors which can be effectively applied for tea quality evaluation.     

Comparison of the Effects of Environmental Treatments on Hydrogen Concentration and Energy Gap Variations of Hydrogenated Amorphous and Polymorphous Silicon Films Prepared by PECVD Technique

Semiconductors - Hydrogen to silicon (Si–H) bond concentration and strength play important roles in high quality hydrogenated amorphous silicon layers prepared by PECVD techniques. In this...     

Thermodynamic evaluation of hypereutectic Al–Si (A390) alloy with addition of Mg

This paper presents the thermodynamic evaluation of A390 hypereutectic Al–Si alloy (Al–17% Si–4.5% Cu–0.5% Mg) and alloys up to 10% Mg, using the Factsage® software. Two critical compositions were detected at 4.2% and 7.2% Mg where the temperatures of the liquidus, the start of the binary and of the ternary eutectic reaction are changed. These critical compositions show differences in the formation of Mg₂Si intermetallic particles during the solidification interval. For compositions up to 4.2% Mg, the Mg₂Si intermetallic phase first appears in the ternary eutectic zone. With Mg contents between 4.2% and 7.2%, Mg₂Si particle appears in both the binary and ternary eutectic reactions. Above 7.2% Mg, it solidifies as a primary phase and also during the binary and ternary reactions. The calculated liquid fraction vs. temperature curves also showed a decrease of the eutectic formation temperature (knee point temperature) with the addition of Mg content up to 4.2% Mg. This temperature becomes almost constant up to 10% Mg. The calculation of eutectic formation temperature shows a good agreement with differential scanning calorimetry (DSC) tests.