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Formation of Compressive Residual Stress by Face Milling Steel AISI 1045
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Abstract

     Machining residual stresses correlate very closely with the cutting parameters and the tool geometries. This research work aims to investigate the effect of cutting speed, feed rate and depth of cut on the surface residual stress of steel AISI 1045 after face milling operation. After each milling test, the residual stress on the surface of the workpiece was measured by using X-ray diffraction technique. Design of Experiment (DOE) software was employed using the response surface methodology (RSM) technique with a central composite rotatable design to build a mathematical model to determine the relationship between the input variables and the response. The results showed that both the feed rate and the cutting speed are the significant factors controlling the surface residual stress, while the depth of cut had no influence. A quadratic empirical model was developed with a 95% confidence level, and a good agreement was found between the experimental and predicted results. A numerical optimization was then conducted through DOE program to find the optimum surface residual stress at the optimum cutting parameters, depending on the maximum desirability obtained. The optimum compressive surface residual stress (-224.361 MPa) was found at cutting speed of 69.2 m/min, feed rate of 0.4 m/min and depth of cut of 0.4 mm.

Keywords: Face milling, Cutting parameters, Steel AISI 1045, Residual stresses, XRD; DOE, RSM, Modeling and Numerical optimization.

 

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Publication Date
Wed May 10 2023
Journal Name
Journal Of Engineering
Effect of Petroleum Products on Steel Fiber Reinforced Concrete
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This Investigation aims to study the effect of adding Steel fibers with different volume fractions Vf (o.5, 0.75, and 1% by volume of concrete) with aspect ratio 100 on mechanical properties of concrete, and also
finding the influence of petroleum products (Kerosene and Diesel) on mechanical properties of Steel Fiber Reinforced Concrete (SFRC).
The experimental work consists of two groups: group one consists of specimens (cubes and prisms) plain and concrete reinforced with steel fiber exposed to continuous curing with water. Group two consists of
specimens (cubes and prisms) plain and concrete reinforced with steel fiber exposed to kerosene and diesel after curing them in water for 28 days before exposure. The results of all te

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Retina Based Glowworm Swarm Optimization for Random Cryptographic Key Generation
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The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order t

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

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Publication Date
Fri Aug 30 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Artificial Intelligence and Cybersecurity in Face Sale Contracts: Legal Issues and Frameworks
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The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI genera

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Publication Date
Tue Nov 01 2016
Journal Name
Materials Science Forum
Study the Effect of Liquid Layer Level on the Formation of Zinc Oxide Nanoparticles Synthesized by Liquid-Phase Pulsed Laser Ablation
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This work is focused on studying the effect of liquid layer level (height above a target material) on zinc oxide nanoparticles (ZnO and ZnO2) production using liquid-phase pulsed laser ablation (LP-PLA) technique. A plate of Zn metal inside different heights of an aqueous environment of cetyl trimethyl ammonium bromide (CTAB) with molarity (10-3 M) was irradiated with femtosecond pulses. The effect of liquid layer height on the optical properties and structure of ZnO was studied and characterized through UV-visible absorption test at three peaks at 213 nm, 216 nm and 218 nm for three liquid heights 4, 6 and 8 mm respectively. The obtained results of UV–visible spectra test show a blue shift accomp

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Publication Date
Sun Feb 24 2019
Journal Name
Iraqi Journal Of Physics
The effect of triggering on the output performance of diode direct face pumping for disc laser in pulse mode
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The triggering effect for the face pumping of Nd:YVO4 disc medium of 4×5×0.5 mm was investigated using bulk diode laser at different resonator cavity length in pulse mode and at repetition rate of 1.3kHz. The maximum emitted peak power was found to be 100, 82, and 66 mW for resonator lengths of 10, 13.5, and 17.5 cm respectively, while the threshold pumping power was found to be 41mW. The maximum emitted peak power obtained was 300 mW when using external triggering and 10cm length, with repetition of 3Hz.

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Publication Date
Mon Oct 13 2025
Journal Name
Mesopotamian Journal Of Cybersecurity
Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network
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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

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Publication Date
Fri Mar 15 2019
Journal Name
Journal Of The College Of Education For Women
Intellectual Stress of Basic Education Faculty Professors
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   1-Identifying  the intellectual exertions have basic education faculty professors.

2-. Identifying the differences in intellectual exertions as a variable type (male-female) current research society is determined by the basic education college professors and both sexes Mustansiriyah University and academic year (2018-2019) and sample (200) textbook and teaching staff researcher has built a scale term This intellectual and extract his results showed statistical characteristics:-.  The College of basic education teachers suffer from stress. There were no differences between males and females in intellectual exertions.  Depending on the results, the researcher has developed a number of recommendations and

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Publication Date
Sun Oct 14 2018
Journal Name
Industrial Lubrication And Tribology
Thermal stress analysis of dry friction clutches
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Purpose

The purpose of this paper is to present an approach to compute accurately the distributions of the frictional heat generated, contact pressure and thermal stresses at any instant during the sliding period (heating phase) of the single-disc friction clutch system works in the dry condition and the complex interaction among them.

Design/methodology/approach

Numerical work was achieved using the developed elastic and thermal finite element models (axisymmetric models) to simulate the engagement of the single-disc friction clutch system.

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