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Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions
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Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were divided randomly into three subsets for training, validation, and testing the developed hybrid intelligent model. ANOVA results revealed that feed rate is highly affected by the surface roughness followed by the depth of cut. One-way ANOVA, including a Post-Hoc test, was used to evaluate the performance of three developed models. The hybrid model of Artificial Neural Network-Gravitational Search Algorithm (ANN-GSA) has outperformed Artificial Neural Network (ANN) and Artificial Neural Network-Genetic Algorithm (ANN-GA) models. ANN-GSA achieved minimum testing mean square error of 7.41 × 10−13 and a maximum R-value of 1. Further, its convergence speed was faster than ANN-GA. GSA proved its ability to improve the performance of BPNN, which suffers from local minima problems.

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Publication Date
Sun Oct 01 2023
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Optimized Artificial Neural network models to time series
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        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

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Publication Date
Fri Apr 21 2023
Journal Name
Aip Conference Proceedings
Study the contrast of thermal expansion behavior for PMMA denture base, single and hybrid reinforced using the thermomechanical analysis technique (TMA)
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This research investigated the effect of adding two groups of reinforcement materials, including bioactive materials Hydroxyapatite (HA) and halloysite nanoclay and bioinert materials Alumina (AL2O3) and Zirconia (ZrO2), each of them with various weight ratios (1,2,3,4 &5)% to the polymer matrix PMMA. The best ratios were selected, and then a hybrid was preparing Composite red from the best ratios from each group. Thermal properties, including thermal conductivity and Thermomechanical Analysis (TMA) technology, have been studied. The results showed that adding 3% Hydroxyapatite (HA) and 5% halloysite nanoclay to the polymethacrylate (PMMA) mer leads to an increase in thermal conductivity. It was also found from the Thermomechanical Analysis

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Publication Date
Sun Jan 01 2023
Journal Name
Technologies And Materials For Renewable Energy, Environment And Sustainability: Tmrees22fr
Study the contrast of thermal expansion behavior for PMMA denture base, single and hybrid reinforced using the thermomechanical analysis technique (TMA)
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Publication Date
Wed Jan 01 2020
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Estimating the reliability function of the asymmetrical hybrid parallel-series system: Applied study at the state company for vegetable oils industry
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The research studied and analyzed the hybrid parallel-series systems of asymmetrical components by applying different experiments of simulations used to estimate the reliability function of those systems through the use of the maximum likelihood method as well as the Bayes standard method via both symmetrical and asymmetrical loss functions following Rayleigh distribution and Informative Prior distribution. The simulation experiments included different sizes of samples and default parameters which were then compared with one another depending on Square Error averages. Following that was the application of Bayes standard method by the Entropy Loss function that proved successful throughout the experimental side in finding the reliability fun

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Publication Date
Sat Mar 30 2002
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
The Determination of the Variables of High Response on the Anodizing of Aluminum-Magnesium Alloy 5052 (Statistical analysis)
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Publication Date
Wed Sep 28 2022
Journal Name
Journal Of The College Of Education For Women
The Level of Aligning Scientific Research in Omani Universities with the Era of the Fourth Industrial Revolution in the Light of Oman’s 2040 vision and the Ways of its Development
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The current research aims to reveal the reality of coping the scientific research in Omani universities in the Sultanate of Oman with the requirements of the Fourth Industrial Revolution in the light of Oman’s 2040 vision. It also aims derive some suggestions to develop the scientific research in these institutions. The study has adopted a qualitative approach in which interviews were conducted. The sample consisted of (16) leaders of governmental and private higher education institutions, as well as, some experts in the field of Fourth Industrial Revolution. The theoretical significance of the study is represented by its response to Oman’s vison in 2040. It is further in line with the previous international reports and educational s

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Publication Date
Sun Sep 06 2009
Journal Name
Baghdad Science Journal
Effect of food on growth and fecundity Brine Shrimp Artemia spp. laboratory Conditions
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This in order to test the effect of food on growth and fecundity, two kinds of food have been used the algae Scendesmus quadricaudae and fresh water shrimp powder. For two generations, growth and productivity have been followed up. The fresh water shrimp has been noticed as a food better than algae, because it caused recording, for the two generation higher length rate for the weeks of experiment. The individuals length rate at the end of the forth week reached 9.35 and 9.48 mm for the first generation and second generation respectively. The average length weekly increase rate for the first and second generations individuals feeding on dried shrimp was higher through the first and second week compared to what was recorded when feeding alga

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Publication Date
Tue Nov 07 2017
Journal Name
Spe Symposium: Production Enhancement And Cost Optimisation
Wettability Alteration of Carbonate Rocks via Nanoparticle-Anionic Surfactant Flooding at Reservoirs Conditions
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Nanofluids, liquid suspensions of nanoparticles (NPs) dispersed in deionized (DI) water, brine, or surfactant micelles, have become a promising solution for many industrial applications including enhanced oil recovery (EOR) and carbon geostorage. At ambient conditions, nanoparticles can effectively alter the wettability of the strongly oil-wet rocks to water-wet. However, the reservoir conditions present the greatest challenge for the success of this application at the field scale. In this work, the performance of anionic surfactant-silica nanoparticle formulation on wettability alteration of oil-wet carbonate surface at reservoir conditions was investigated. A high-pressure temperature vessel was used to apply nano-modification of oil-wet

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Publication Date
Mon Jul 31 2017
Journal Name
Journal Of Engineering
Development of an On-Line Self-Tuning FPGA-PID-PWM Control Algorithm Design for DC-DC Buck Converter in Mobile Applications
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