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This study presents the debonding propagation in single NiTi wire shape memory alloy into linear low-density polyethylene matrix composite the study of using the pull-out test. The aim of this study is to investigate the pull-out tests to check the interfacial strength of the polymer composite in two cases, with activation NiTinol wire and without activation. In this study, shape memory alloy NiTinol wire 2 mm diameter and linear fully annealed straight shape were used. The study involved experimental and finite element analysis and eventually comparison between them. This pull-out test is considered a substantial test because its results have a relation with behavior of smart composite materials. The pull-out test was carried out by a u

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

The laser micro-cutting process is the most widely commonly applied machining process which can be applied to practically all metallic and non-metallic materials. While this had challenges in cutting quality criteria such as geometrical precision, surface quality and numerous others. This article investigates the laser micro-cutting of PEEK composite material using nano-fiber laser, due to their significant importunity and efficiency of laser in various manufacturing processes. Design of experiential tool based on Response Surface Methodology (RSM)-Central Composite Design (CCD) used to generate the statistical model. This method was employed to analysis the influence of parameters including laser speed,

Corrosion behavior of aluminum alloy 7025 was investigated in hydrochloric acid (pH=1) containing 0.6 mol.dm-3 NaCl in the existence and absence of diverse concentrations of sulphamethoxazole as environmentally friendly corrosion inhibitor over the temperature range (298-313)K. Electrochemical polarization method using potentiostatic technique was employed. The inhibition efficiency has been raised with increased sulphamethoxazole concentration but lessened at temperature increases. The highest efficiency value was 96.5 at 298 K and 2 x10-4 mol.dm-3 concentration of sulphamethoxazole. The sulphamethoxazole adsorption was agreed with Langmuir adsorption isotherm. Some thermodynamic parameter (△Gads) and activation energy (Ea) were determin

The present study was conducted to evaluate the effect of different inhibitors on the corrosion rate of aluminum in 50% (v/v) ethylene glycol solution at 80°C and pH 8.0 in which the electrochemical technique of linear sweep voltammetry was employed to characterize each inhibitor function and to calculate the corrosion rate from Tafel plots generated by a computer assisted  potentiostat.

It is found that both sodium dichromate and borax reduces the corrosion rate by polarizing the anodic polarization curve while sodium phosphate, potassium phosphate, and sodium benzoate reduces the corrosion rate by polarizing both the anodic and cathodic  polarization curve.

When inhibitor concentration increases from I g/l up

This paper focuses on the optimization of drilling parameters by utilizing “Taguchi method” to obtain the minimum surface roughness. Nine drilling experiments were performed on Al 5050 alloy using high speed steel twist drills. Three drilling parameters (feed rates, cutting speeds, and cutting tools) were used as control factors, and L9 (3³) “orthogonal array” was specified for the experimental trials. Signal to Noise (S/N) Ratio and “Analysis of Variance” (ANOVA) were utilized to set the optimum control factors which minimized the surface roughness. The results were tested with the aid of statistical software package MINITAB-17. After the experimental trails, the tool diameter was found as the most important facto

This study presents the effect of laser energy on burning loss of magnesium from the holes' drilled in aluminum alloy 5052. High energy free running pulsed Nd:Glass laser of 300 µs pulse duration has been used to perform the experiments. The laser energy was varied from 1.0 to 8.0 Joules, The drilling processes have been carried out under atmospheric pressure and vacuum inside a specially designed chamber. Microhardness of the blind drilled holes has been investigated .The results indicated that the magnesium loss could be manipulated by adjusting the focusing conditions of the laser beam. Almost, the obtained holes were free of cracks with low taper and low sputter deposition. .The holes performed under atmospheric conditions have high

 In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that the efficiency of this controller when compared with the results of the leg model that is controlled by PI+2D, PD+NID, and F

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet