Analytical and numerical prediction of localized necking in sheet metal forming processes

The goal of this research is to investigate, develop and validate analytical and numerical tools that can accurately predict failure in sheet metal forming operations. The strain-based forming limit curve (FLC) is one of the tools to predict the maximum permissible strains of thin metallic sheets which are loaded in the plane of the sheet to different states of stress. It may be used to assess forming operations in the press shops as well as unintentional deformations, such as vehicle, aircraft, or train crashes. The accurate prediction of failure in sheet metal stamping can shorten product lead times, decrease tooling costs, and allow for overall more rigorous and robust designs. In the present work, three main analytical models are discussed: the modified Marciniak and Kuczynski (M-K) model which included a varying defect orientation with respect to the principal stress directions, the effective stress ratio model and the major strain ratio model. The M-K predictions of the FLC are demonstrated with several yield criteria for eight different materials. Furthermore, the stress-based FLC theory of the effective stress ratio model is described, and its extension to include varying defect orientation is presented. Also the original non-incremental FLC theory of the major strain ratio model is presented, and its extension to include varying defect orientation is described. In addition, numerical and experimental data are used to investigate the key assumption of the three analytical models and the results show that the parameters investigated to predict failure (i.e., the incremental strain ratio, critical stress concentration factor and critical strain concentration factor) were not constant for the various strain paths for three analytical models considered. Finally, finite element analysis (FEA) is used to predict the FLC with a stress-based failure criterion and a comparison between three different element types (shell, solid and solid-shell) is investigated in detail. The numerical results show that despite the differences in stress distribution assumptions, shell, solid and solid-shell elements would not provide differences in failure prediction for the uniform, in-plane stretching states examined when a stress-based failure criterion is used

Public Awareness Toward Healthy Life: Sample from Iraqi Community 2020

Background: People must develop a healthy lifestyle to have a longer and healthier life. Objective: to study the Public awareness toward healthy life of Iraqi community, and it relation with some of demographic variables. Method & persons: A cross-sectional study conducted from 20th June – 20th September 2022, through online questionnaire (google form) distributed through any available channels (e-mails, Viber, Chat, Messenger, WhatsApp, Telegram, Facebook groups) Results: Nine hundred and fifty-six Iraqi persons enrolled in this study, with age mean and standard deviation 32.67± 11.954; the highest percentage of participants 589(51.2%) aged ≤ 29 years, females 577(60.4%), currently married 480(50.2%), medical & health field worker 458(47.9%), while all the non- medical persons 498, (52.1%), {students 212(22.2%), government non-medical worker 146(15.3%). most of the participants 812(84.9%) had once to twice brushing their teeth, and 408(42.7%) had sometimes using Dental floss, while 376(39.3%) of them never using dental floss, and only 39(41.1%)of the participant had regular teeth examination also good overall teeth health only in 387(40.5%). Acceptant-lifestyle 767(80.2%), and only 15(1.6%) had food & water intake poor lifestyle, while in overall-checkup the majority had poor overall-checkup 561(58.7%), then acceptable overall checkup 290(30.3%). Conclusion: Most of the participant had acceptance lifestyle in concerned food and water but had poor Overall checkup for vision, teeth, blood pressure, sugar, lipid and Regular doctor checkup in general

Hydroisomerization of alkanes over metal-loaded zeolite catalysts

Zeolite catalysis plays an important role in many industrial applications due to their unique properties and has become widely used in the area of oil refining. Of particular interest is Zeolite Y, which can be hydrothermally treated into its ultrastable form, USY. USY offers a superior practicality, especially when dealuminated and metal-loaded. The importance of alkanes hydroisomerization arises from the continuingly stricter regulations imposed on the utilization of gasoline as an automotive fuel. The requirements to reduce the aromatics content in gasoline present a need to find an alternative way to maintain its research octane number (RON). An alternative to gasoline's high-octane aromatic content is to increase the RON for the paraffinic content of gasoline, which can be accomplished through hydroisomerization. Commercially, bifunctional metal-loaded zeolites are used to hydroisomerize the light naphtha stream produced at overheads of atmospheric distillation towers. However, no such process exists for the low-value heavy naphtha cut. This targeted process would, if successful, greatly improve refiner's profitability.In this work, bifunctional USY zeolite catalysts are studied in the hydroisomerization of a normal alkane (nC7, RON = 0). This nC7, found in heavy naphtha, has been used as the 'model' compound. The impact of different reaction conditions and catalyst properties on catalyst activity and stability, in addition to the catalyst selectivity to high octane isomers is one step towards determining optimum conditions and preferential catalyst formulations that favour octane maximization. Six platinum-loaded USY zeolite catalysts, four in-house and two commercial, were tested in an atmospheric glass fixed-bed reactor and a stainless steel reactor purpose-built during the course of this thesis. Reaction temperatures ranged from 170 to 250oC at pressures between 1 and 15 bar. The hydrogen to hydrocarbon molar ratio was fixed at 9, with feed space time ranging from 35.14 to 140.6 kg.s/mol. In-house catalysts were hydrothermally treated at different severities, while commercial ones were originally dealuminated through acid-leaching treatments.Results have shown commercial catalyst CBV-712 gave the best performance and highest octane values for product isomers (>30). In addition, there was no coke generation. The next best catalyst was the most severely steamed in-house catalyst (USY-D) that has shown a remarkable performance at high pressures, almost eclipsing the performance of CBV-712, yet produced higher levels of coke. Other USY catalysts tested were less robust during reactions, probably due to imbalance in their acidic to metallic functions, or diffusion limitations arising from their pore structures. The best catalysts were, nonetheless, highly sensitive to sulfur presence in the feed, which severely impacted their activity, especially their metallic functions, and thus require sulfur-free feeds in order to demonstrate their full capacities. Simple kinetic modelling of experimental data was performed using the initial rates method and estimation of kinetic parameters, whose values were in good agreement with previous literature.EThOS - Electronic Theses Online ServiceSaudi AramcoGBUnited Kingdo

Biometric Template Protection based on Hill Cipher Algorithm with Two Invertible Keys

The security of stored templates has become an important issue in biometric authentication systems this because most of the biometric attacks target the biometric database beside the difficulty of issuing the templates again. Thus, to protect the biometric templates it must be encrypted before storing in database. In this paper we proposed an efficient encryption method based on two invertible and random keys to enhance and overcome the weakness of hill cipher algorithm the keys generated using upper triangular matrices with Pseudo-Random Number Generator (PRNG) using two large and random encryption keys. The proposed encryption method provides sufficient security and protection for the biometric templates from attacks, where the experimental results showed high efficiency comparing with the traditional Hill Cipher and existing methods

An internet of things based smart waste system

The importance of preserving the environment from waste and its pollution lies in many matters such as preserving people health, enhancing the aesthetic character of cites, attracting tourists, and protecting society from environmental disasters. The environmental wastes are the main dilemmas in our daily life and in the world at large. With the existence of modern technology, development and the field of the internet, many solutions have been undertaken to get rid these dilemmas. In this paper, a smart waste system based on internet of things (IoT) technique has been proposed using ESP-32 Wi-Fi microcontroller. This system can be adopted to avoid the accumulation of waste in the streets that distort the face of civilization, also to reduce the burden of workers and limit the workforce. The system is based on a multiple sensors in the garbage baskets, as they measure the waste level by using ultrasonic sensor, the moisture percent and temperature degree using DHT-22 sensor. The sensors data are processed by ESP32 microcontroller and displayed to both LCD screen using I2C protocol and mobile application using IoT cloud. System baskets automatically open their covers when the person approaches with a distance less or equal to 30 cm to throw garbage. Any approval waste basket is automatically discharged through an underground dump system using conveyor belt if the basket is full by 80% garbage and/or the basket moisture reaches to 40%

The classification of the modern arabic poetry using machine learning

In recent years, working on text classification and analysis of Arabic texts using machine learning has seen some progress, but most of this research has not focused on Arabic poetry. Because of some difficulties in the analysis of Arabic poetry, it was required the use of standard Arabic language on which “Al Arud”, the science of studying poetry is based. This paper presents an approach that uses machine learning for the classification of modern Arabic poetry into four types: love poems, Islamic poems, social poems, and political poems. Each of these species usually has features that indicate the class of the poem. Despite the challenges generated by the difficulty of the rules of the Arabic language on which this classification depends, we proposed a new automatic way of modern Arabic poems classification to solve these issues. The recommended method is suitable for the above-mentioned classes of poems. This study used Naïve Bayes, Support Vector Machines, and Linear Support Vector for the classification processes. Data preprocessing was an important step of the approach in this paper, as it increased the accuracy of the classification

A Simulation of Wireless Sensor Network Using ZigBee

Sensor networks have been a wide research area, during the last years. Wireless sensor networks are distributed network structures in which many sensors connect wirelessly to communicate with one another. In this paper the IEEE 802.15.4/ZigBee is used due to its low-power, rate and cost which allows the communication of two way wireless sensor network. In this paper IEEE 802.15.4 performance is analyzed based on OPNET simulator which allows the abilities of generating correct results and analysis to identify the actual behaviour of the real system. With this simulator program, the effect of performance parameters like throughput, data traffic received and data traffic sent for three system topology scenarios are presented. Keywords: WSN, IEEE 802.15.4, performance

An adaptive clustering and classification algorithm for Twitter data streaming in Apache Spark

On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy. Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which can cluster data within a lesser computational time, especially for data streaming is needed. The presented adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization. At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority ofpresented approach comparing with the existing methods in terms of precision, recall, F-score, convergence, ROC curve and accuracy