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Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6
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The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).

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Publication Date
Thu Dec 15 2022
Journal Name
Journal Of Baghdad College Of Dentistry
Force degradation of orthodontic elastomeric chains: A literature review
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Background: Elastomeric chains are used to generate force in many orthodontic procedures, but this force decays over time, which could affect tooth movement. This study aimed to study the force degradation of elastomeric chains. Data and Sources: An electronic search on Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, LILACS, and PubMed was made, only articles written in English were included, up to January 2022.Study selection: Fifty original articles, systematic reviews, and RCTs were selected. Conclusion: Tooth movement, salivary enzymes, alcohol-containing mouthwash, whitening mouthwash, and alkaline and strong acidic (pH <5.4) solutions all have a significant impact on elastomeric chain force degradation. The fo

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Publication Date
Fri Feb 17 2012
Journal Name
Smart Materials And Structures
Frequency tuning of piezoelectric energy harvesters by magnetic force
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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Laser
PDF Fiber Laser Effect on Bond Strength of Titanium implant abutment to Resin Cement
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Aim: surface modification of titanium using fiber laser 1064 nm to enhance the bond strength to resin cement. Material and Methods: thirty titanium discs of 0.6 cm x 0.3 cm (diameter and thickness respectively) were categorized after preparation into three groups (n=10) as follows: control group with no surface treatment and two test groups were treated with fiber laser after estimation the appropriate parameters in the pilot study which are 81 ns pulse duration, 30,000 Hz frequency, 50 µm spot size and 10,000 mm/s scanning speed and different average power values (10 W and 20 W) depending on the tested group. Titanium discs surface characterization was performed by scanning electron microscope (SEM), a

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Publication Date
Fri Mar 31 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
ROLE OF SERUM ZINC AND COPPER AND ZINC/ COPPER RATIO IN ALOPECIA AREATA
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Alopecia areata is considered as a major health problem, its importance is attributed to its
recent increased incidence in our population. Till now, there is no exact cause for alopecia areata
although researchers thought it's an autoimmune disease.
This clinical study was designed to evaluate the role of trace elements (zinc and copper) in patients
with alopecia areata. Twenty patients were diagnosed as having alopecia areata with an age range
(10-40 years) were involved in this study. Normal subjects of the same age group were also
evaluated as control. The level of serum Zn and Cu were measured by flame atomic absorption
spectrophotometry in both control and patient group. And the ratio of Zn/Cu was also estimated

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Publication Date
Tue Jul 14 2015
Journal Name
Ibn Al-haitham J. For Pure & Appl. Sci.
Effect of Annealing Temperature and Thickness on the Structural and Optical Properties of CdSeThin Films
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CdSe alloy has been prepared successfully from its high purity elements. Thin films of this alloy with different thicknesses (300,700)nm have been grown on glass substrates at room temperature under very low pressure (10-5)Torr with rate of deposition (1.7)nm/sec by thermal evaporation technique, after that these thin films have been heat treated under low pressure (10-2)Torr at (473,673)K for one hour. X-ray patterns showed that both CdSe alloy and thin films are polycrystalline and have the hexagonal structure with preferential orientation in the [100] and [002] direction respectively. The optical measurements indicated that CdSe thin films have allowed direct optical energy band gap, and it increases from (1.77- 1.84) eV and from

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Publication Date
Wed Jan 15 2020
Journal Name
Emerging Trends In Mechatronics
Interactional Modeling and Optimized PD Impedance Control Design for Robust Safe Fingertip Grasping
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Publication Date
Tue Oct 15 2019
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Combining Convolutional Neural Networks and Slantlet Transform For An Effective Image Retrieval Scheme
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In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN),

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Publication Date
Sun Oct 05 2025
Journal Name
Mesopotamian Journal Of Computer Science
DGEN: A Dynamic Generative Encryption Network for Adaptive and Secure Image Processing
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Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public image sets. Results show DGEN beats AES, chaos tricks, and other GAN ideas. Entropy reached 7.99 bits per pix

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Mon Jan 01 2018
Journal Name
Aip Conference Proceedings
Synthesis and characterization study of n-Bi2O3/p-Si heterojunction dependence on thickness
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