In this paper, Response Surface Method (RSM) is utilized to carry out an investigation of the impact of input parameters: electrode type (E.T.) [Gr, Cu and CuW], pulse duration of current (I_p), pulse duration on time (T_on), and pulse duration off time (T_off) on the surface finish in EDM operation. To approximate and concentrate the suggested second- order regression model is generally accepted for Surface Roughness Ra, a Central Composite Design (CCD) is utilized for evaluating the model constant coefficients of the input parameters on Surface Roughness (Ra). Examinations were performed on AISI D2 tool steel. The important coefficients are gotten by achieving successfully an Analysis of V

A new scheme of plasma-mediated thermal coupling has been implemented which yields the temporal distributions of the thermal flux which reaches the metal surface, from which the spatial and temporal temperature profiles can be calculated. The model has shown that the temperature of evaporating surface is determined by the balance between the absorbed power and the rate of energy loss due to evaporation. When the laser power intensity range is 107 to108 W/cm2 the temperature of vapor could increase beyond the critical temperature of plasma ignition, i.e. plasma will be ignited above the metal surface. The plasma density has been analyzed at different values of vapor temperature and pressure using Boltzmann’s code for calculation of elec

This paper introduces a Laplace-based modeling approach for the study of transient converter-grid interactions. The proposed approach is based on the development of two-port admittance models of converters and other components, combined with the use of numerical Laplace transforms. The application of a frequency domain method is aimed at the accurate and straightforward computation of transient system responses while preserving the wideband frequency characteristics of power components, such as those due to the use of high frequency semiconductive switches, electromagnetic interaction between inductive and capacitive components, as well as wave propagation and frequency dependence in transmission systems.

Water is the basis of the existence of all kinds of life, so obtaining it with good quality represents a challenge to human existence and development especially in the desert and remote cities because these areas contain small populations and water purification requires great materials and huge amounts of fossil fuels resulting pollution of the environment. Cheap and environmentally friendly desalination methods have been done by using solar distillations. Passive solar stills have low yields, so in this research, the problem is overcome by connecting four heat pipes which are installed on the parabolic concentrator reflector with passive solar still to increase the temperature of hot water to more than 90°C, as a resul

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

When employing shorter (sub picosecond) laser pulses, in ablation kinetics the features appear which can no longer be described in the context of the conventional thermal model. Meanwhile, the ablation of materials with the aid of ultra-short (sub picosecond) laser pulses is applied for micromechanical processing. Physical mechanisms and theoretical models of laser ablation are discussed. Typical associated phenomena are qualitatively regarded and methods for studying them quantitatively are considered. Calculated results relevant to ablation kinetics for a number of substances are presented and compared with experimental data. Ultra-short laser ablation with two-temperature model was quantitatively investigated. A two-temperature model

Iraq has the second largest proven oil reserves in the world. According to oil experts, it is expected that the Iraq's reserves to rise to 200+ billion barrels of high-grade crude.

Oil is a strategic commodity for producing and exporting countries in general, and Iraq in particular, as demonstrated by the international experience that oil is an important means to achieve economic growth, an important tool in the overall economic, social and political development. It is also an important source of hard currency for any national economy and a means to connect the local economy and the global economy. In this paper we focus our attention on selecting the best regression model that explain the effect of human capita

This contribution investigates structural, electronic, and optical properties of cubic barium titanate (BaTiO3) perovskites using first-principles calculations of density functional theory (DFT). Generalized gradient approximations (GGA) alongside with PW91 functional have been implemented for the exchange–correlation potential. The obtained results display that BaTiO3 exhibits a band gap of 3.21 eV which agrees well with the previously experimental and theoretical literature. Interestingly, our results explore that when replacing Pd atom with Ba and Ti atoms at 0.125 content a clear decrease in the electronic band gap of 1.052 and 1.090 eV located within the visible range of electromagnetic wavelengths (EMW). Optical parameters such as a

The current study focuses on utilizing machine learning (ML) models in optimizing and predicting laser-induced surface cold hardening in aluminum alloy 6061-O using a pulsed fiber laser. Three input features were utilized: power density (Pd), frequency (f), and pulse overlap (OV), with surface hardness as the matching aim. A set of ML models, including XGBoost and Adaptive Ensemble, was employed. The dataset was preprocessed for normalization and outlier handling, hyperparameter tuning, and assessment through the grid search and cross-validation, and model performance was evaluated using the coefficient of determination (R²) and mean square error (MSE) metrics. The Linear regression and Random Forest regression models were excluded