Abstract   

Lightweight materials is used in the sheet metal hydroforming process,  because it can be adapted to the manufacturing of complex structural components into a single body with high structural stiffness. Sheet hydroforming has been successfully developed in industry such as in the manufacturing of the components of automotive.The aim of this study is to simulate the experimental results ( such as the amount of pressure required to hydroforming process, stresses, and strains distribution)  with results  of finite element analyses (FEA)  (ANSYS 11)  for aluminum alloy (AA5652) sheets with  thickness (1.2mm) before heat treatm

Abstract

 Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it

            The widespread use of the Internet of things (IoT) in different aspects of an individual’s life like banking, wireless intelligent devices and smartphones has led to new security and performance challenges under restricted resources. The Elliptic Curve Digital Signature Algorithm (ECDSA) is the most suitable choice for the environments due to the smaller size of the encryption key and changeable security related parameters. However, major performance metrics such as area, power, latency and throughput are still customisable and based on the design requirements of the device.

The present paper puts forward an enhancement for the throughput performance metric by p

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T

The enhancement of heat exchanger performance was investigated using dimpled tubes tested at different Reynolds numbers, in the present work four types of dimpled tubes with a specified configuration manufactured, tested and then compared performance with the smooth tube and other passive techniques performance. Two dimpled arrangements along the tube were investigated, these are inline and staggered at constant pitch ratio X/d=4, the test results showed that Nusselts number (heat transfer) of the staggered array is higher than the inline array by 13%.  The effect of different depths of the dimple (14.5 mm and 18.5 mm) has been also investigated; a tube with large dimple diameter enhanced the Nusselts number by about 25% for the ran

This research investigated the importance and priorities of the project overhead costs in Iraq via a questionnaire using the fuzzy analytic hierarchy process technique (FAHP). Using this technique is very important in the uncertain circumstances as in our country. The researcher reached to frame an equation through the results of the priorities of weights include the percentages of each of the main items of the project overhead costs. The researcher tested this equation by applying it to one of the completed projects and the results showed suitability for the application. The percentages of the (salaries, grants, and incentives) and (fieldwork requirements) in equation represent approximately two-thirds of project overhe

The present paper addresses cultivation of Chlorella vulgaris microalgae using airlift photobioreactor that sparged with 5% CO2/air. The experimental data were compared with that obtained from bioreactor aerated with air and unsparged bioreactor. The results showed that the concentration of biomass is 0.36 g l-1 in sparged bioreactor with CO2/air, while, the concentration of biomass reached to 0.069 g l-1 in the unsparged bioreactor. They showed also that aerated bioreactor with CO2/air gives more biomass production even the bioreactor was aerated with air. This study proved that application of sparging system for cultivation of Chlorella vulgaris microalgae using either CO2/air mixture or air has a significant growth rate, since the biorea

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In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent root-

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings