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Go to Editorial ManagerDiabetic peripheral neuropathy represents one of the common long-terms complications that effect about fifty percentage?of diabetes patients. The habitual diagnosis tool based on nerve conduction study that examine the nerve damage and classify the patient status into normal and diabetic peripheral neuropathy with degree of severity without considering the effect on skeletal muscle and take on patient data. A complementary diagnostic tool proposed, in this study integrates the patient’s data including body mass index, age and duration of diabetic, average blood glucose levels, nerve conduction study that involves amplitude and latency of peroneal and tibial nerves and muscle ultrasound alongside the machine learning algorithms to facilitate the clinicians for a precise diagnosis. A group of control and diabetic patients utilized to gather the data with calculating the muscle thickness and statistical properties from the gray-level ultrasound images of six skeletal muscles. Support vector machine, naïve bayes, ensemble of bagged tree and artificial neural network supervised machine learning algorithms categorize each class with a high classification accuracy, 98.1% for tibialis anterior with naïve bayes algorithm. The outcomes of this study show a promising complementary diagnostic tool that will help the clinicians to perform an exact diagnosis and disclose the side effect on both nerves and muscles of diabetic patients.
The main idea behind this paper is to design and implement a cheap, smaller size, easily operable, easy interface and flexible 3-axis Computer Numerical Control (CNC) plotter machine. The lower cost is achieved by using 2 CD drives from old PC’s with their stepper motors as the main structure for the hardware. The two stepper motors already found in the CD drives used to control the pen movements onto X and Y axis and one servo motor on the Z axis. An Arduino Uno microcontroller is used to controls the proper synchronization of these three motors during printing/drawing process. The Arduino Uno is programmed with G-Code parser from PC that is connected to the Arduino via a USB cable to control the motors movement and synchronization. The plotter machine is implemented and tested by printed different images and texts on papers (8cm × 8cm) using a pen, the small size of the papers because of the small plotter size. The motors winding voltages were displayed on the oscilloscope during the printing process to investigate the synchronization between the three motors. The design of the circuit is simple, inexpensive and can be accomplished using commercially available components.
Transfer function characteristics of a DC machine are used in this paper to estimate speed and torque in four quadrant operation modes. Estimation speed and torque control based on a DC machine transfer function is implemented by measuring the DC chopper instantaneous average output voltage and current. MATLAB\SIMULINK is used to implement the DC drive circuit in the forward and reverse motoring and regenerative modes, respectively. The DC drive system is simulated at different speed and load torque values in steady state and dynamic operating conditions. Simulation results demonstrate success of the sensorless and PI controller systems, which gives satisfactory agreements between the estimated, actual and reference speed and torque values.
In the present work, theoretical and experimental Study of vibration of a drum type of Horizontal Washing Machine. The effect of the Isolators stiffness, damping coefficient and the drum mass for specific laundry capacity also has been studied. The work in this research has been carried out analytically by using MATLAB, and Study experimentally the effect of different speed and unbalance force during the spinning cycle of the washing machine at four sides of it. This analysis aims to reducing the excited vibration. This was achieved theoretically by investigate the effect of various parameters in order to assign property values to increase the isolation efficiency to reach optimum design. The results is show that drum vibration amplitude reduced to 42 % at spinning speed 1000 rpm and 41% at 1200, 1400 rpm when the applied selected parameters.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
In this paper the ability of fabricating laminate composites by manual layup was discussed. Heating method was used to manufacture the composites; heat was applied to approximately 12 hours with specific heat temperature. There were four types of laminate composites fabricated and studied in this research, containing Aluminum alloy 6061 as the common element in all types, two types of fibers; woven Carbon fiber with two different orientations: ±45°, ±60°, random fiberglass and with two types of resin; epoxy resin and polyester resin. Different types of composites were made to determine the effect of CNC milling machine to the measured surface roughness and for specified parameters. The weight fraction ratio of the fibers is 37%, polymer is 34% and 29% for Aluminum. The parameters selected are spindle speed, feed rate and depth of cut. The L9 Taguchi orthogonal arrays, signal to noise (S/N) ratio and analysis of variance (ANOVA) are selected to determine the effect of these parameters; it was analyzed by MINITAB 17 program. The results showed that the parameter were significant more to the epoxy resin specimens than polyester resin specimens. The optimal milling parameters for good surface finish for Aluminum – Carbon fiber composite are at 3000RPM, 1200mm/min, 1.2mm, and for Aluminum – Fiberglass composite are 5000RPM, 1800 mm/min, 2.0mm.
In the field of engineering, 3D printers are indispensable due to their high precision. This study focuses on the construction and optimization of a 3D printer using aluminum T-slotted bars for the frame, Raspberry Pi 4 for control, and Lightburn software for image printing and machine control. After assembling the main components and programming with Marlin firmware, the machine was tested for vibration and noise reduction. The research compared the vibration of a diode laser and spindle during printing, revealing significantly lower vibration with the laser compared to the spindle. These findings demonstrate the effectiveness of the constructed 3D printer in reducing vibration and noise during operation.
The technique of recording muscle signals is crucial in determining how effectively they can be utilized for individual benefit. This study focuses on hand movements recognized by using the Myo armband and Motion Processing Unit (MPU) 6050 sensors. Linear Discriminant Analysis (LDA), K-nearest neighbors (k-NN), and Support Vector Machine (SVM) were employed for classification. sEMG signals using the Myo armband for 7 hand gestures and 2 elbow movements were recorded from 10 healthy subjects. Results showed that SVM outperforms LDA and k-NN in accuracy in both cases, the sensor is worn once on the arm and again on the forearm. regions. The window size and choice of features significantly influence system accuracy, with SVM achieving an average accuracy of 89.84%. Besides that, the fusion of Myo Armband sensor and gyroscope sensor through OR rule makes significant enhancement in recognition accuracy with which is reached to 97.0135%. In conclusion, the Myo armband, when worn on the forearm, proves practical for hand gesture recognition, with SVM offering superior recognition accuracy. Furthermore, the combination of the Myo Armband sensor and the gyroscope sensor showed higher recognition accuracy.
The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.
Incremental forming is a flexible sheet metal forming process which performed by utilizes simple tools to locally deform a sheet of metal along a predefined tool path without using of dies. One limitations of single point incremental forming (SPIF) process is the error occur between the CAD design and the product profile. This work presents the single point incremental forming process for produced pyramid geometry and studied the effect of tool geometry, tool diameter, wall angle, and spindle speed on the dimensional accuracy. Three geometries of forming tools were used in experimental work: ball end tool, hemispherical tool, and flat with round corner tool. The sheet material used was pure Aluminum (Al 1050) with thickness of (0.9 mm). The experimental tests in this work were done on the computer numerical control (CNC) vertical milling machine. The products dimensions were measured by utilized the dimensional sensor measuring instrument. The extracted results from the single point incremental forming process indicated the best acceptance between the CAD profile and product profile was found with the ball end tool and diameter of (10 mm), wall angle (50°) and the rotational speed of the tool was (800 rpm).
The goal of the study is to predict the Man in the Middle attack in the packets of Wireshark program by using Support Vector Machines (SVM).In the time of using the internet, it has become a tool targeted by attackers and hackers; it is a serious threat to the devices. A uniqueness of an attack that appears in multiple identities for legitimate agencies. It is very necessary to know the behavior attack and predict the possible actions of an attacker. In this research a detection of Man in the Middle attack by monitoring the Wireshark program and recording any changes can be recognized in packet information. The classification of packets is divided into two categories (normal and abnormal). The proposed model is designed in many stages: loading data, processing data, training data, and testing data. The detection of SVM based on abnormal network packet through movement packets in the Wireshark program that needs to deal with current packets to recognize a new attack that one does not have prior knowledge of its detection, and there is a need for an intelligent way to separate network packets that represent normal. The proposed approach achieved an accuracy of 97.34% in detecting attacks. The results show that the proposed model effectively visualizes attacker behavior from data that represents abnormal network attackers. Research achieves successful accuracy in predicting abnormalities.
Kidney renal failure is a life-threatening disease in which one or both kidneys are not functioning normally. The only available treatment other than a kidney transplant is to start on dialysis sessions, whether it is peritoneal or Hemo-dialysis[1].For some patients, the dialysis procedure is an exhausting and sometimes expensive trip to the specialized dialysis centers since it must be done about three times a week, depending on the physician's decision depending on the glomerular filtration rate of the kidneys[2-4].Different researchers have made many attempts over the years to replace conventional dialysis machines with more accessible at-home dialysis systems to provide patients with comfortable treatment sessions at the time they want without the need to change their lifestyle to fit the dialysis center's schedule.A review of the critical methods utilized in the creation and application of a portable dialysis machine that resembles the traditional dialysis center devices was conducted using a number of prior studies (research conducted between 2009 and 2024); the goal of all studies was to create a device that consists of filtering system, detection system to ensure there is no blood leakage and all parameters are within the acceptable limits, alarm system, and dialysate regeneration system, and each method will be described precisely in this review.As a result, the discussed studies found that using peristaltic pump pumps with a phase difference by half cycle between blood and dialysate will cause a higher urea clearance rate; multiple studies focused on the modification of the dialyzing filter to find that using Polyethene glycol surface-modified silicon nanopore membranes, dual-layer hollow fiber membranes, the use of BRECS cell therapy, carbon activated blocks, all contributed highly in enhancing the dialyzing process providing the patients with highly efficient blood purification session.
Technically, medical imaging modalities are quantitative, qualitative, and semi-quantitative. Such modalities can generate meaningful and valuable quantitative and qualitative data. Correlating predictive outcomes with quantitative and qualitative data is a difficult process. Thanks to modern computational hardware and advanced machine learning algorithms, it is not a demanding job to perform predictive analysis by cultivating quantitative and qualitative data. Radiomics is a popular topic that studies quantitative data from medical images in order to obtain biologically meaningful information for diagnosis, prognosis, theragnosis, and decision support. Handcrafted radiomics is a process including features based on shape, pixel, and texture-related knowledge from medical scans. In the pursuit of advancing the field of radiomics, we have developed a cutting-edge radiomics training simulator, powered by MATLAB. This tool has been designed for those familiar with MATLAB, making it easy for them to transition into the fascinating world of radiomics. MATLAB's user-friendly interface and strong support in the engineering community provide an ideal platform for this simulator, ensuring aspiring radiomics learners have access to the resources they need for success. Throughout the paper, purpose, design details and methodology of the simulator are described.
Many joints in the body depend on cartilage for their mechanical function. Since cartilage lacks the ability to self-heal when injured, treatments and replacements for damaged cartilage have been created in recent decades. The mechanical tests had an important role in the treatment and designing of the replaced cartilage. There are two types of cartilages in the knees: fibrocartilage (the meniscus, it is a special type of cartilage) and hyaline cartilage. Its mechanical properties are important because structural failure of cartilage is closely related with joint disorders. This study aimed to determine the stress-strain curve to give broader understanding of the material’s properties. The results of this study could help to develop computational models for evaluating mechanics of knee joint, predicting possible failure locations and disease progression in joints.The study involved two specimens taken from bovine, the first was the articular cartilage with subchondral bone and the second was the meniscus cartilage each one loaded on a compressive testing machine to compute the displacement, and the force applied, enabling the calculation of the stress-strain curve of the material.Specimen failure occurred in the articular cartilage surface at a force break of 73.8N and get force peak about 87.2 N. The meniscus cartilage failure had occurred at a force break of 29.2 N and get force peak about 34.9 N.
Friction stir spot welding (FSSW) is a modern solid-state joining process able to weld similar and dissimilar overlap joints in different classes of materials and is widely being considered for automotive industry. In this work, the mechanical behavior ) i.e. tensile shear tests, Microhardness(, and microstructure of friction stir spot welded joints were studied for AA6061-T6 aluminum alloy sheets with thickness of 1.6 mm. Series of FSSW experiments were conducted using vertical CNC milling machine type "C-tek". FSSW is carried out at different pin profiles (cylindrical, taper, and triangular) and tool rotational typically speeds, i.e. 800, 1000, 1200 and 1400 rpm. Based on the welding experiments conducted in this study, the results show that sheets welded by triangular pin tool have highest tensile shear load, of 3.2 kN, followed by welds with cylindrical pin, while welds made using taper pin has the tensile shear load 2.1 kN at optimum speed of 1200 rpm. Also the pin shape and rotational speed had an obvious effect on microstructural parameters i.e. hook height and bond width.
An important challenge confronted when using blanking to machine sheet metal is the treatment of the shearing force in demand for great strength and heavy stock. One of the methods used to decrease the force wanted is the increase of a punch shear angle. In this work, experiments were conducted to study the effect of shear angle for blank has a diameter (50 mm) on shear force of a low carbon steel sheet (AISI 1008). Low carbon steel is a very common material used in fabrication of sheet metal components, with thickness of (0.5 mm). Tools used in the blanking tests were one traditional flat end punch and four different bevel sheared rooftop punches, which rooftop punches were compared to. and it (0°, 5°, 10°, 15°, 20°) a punches diameter (49.95 mm) by clearance (0.025mm) for each side , with a blanking speed (500mm/min). A special blanking die set is designed and manufactured and was a blank cut by a hydraulic press whose capacity (20 ton). The results showed that the blanking forces of (AISI 1008) low carbon steel metal could be decreased radically with best bevel punch geometry. Using (10°) shear angle at the punch end, the cutting forces decreased up to (90%) compared to the ones of the traditional flat end tool
Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
In this study a Nickel-Titanium-Cupper shape memory alloys was manufactured by powder metallurgy (PM) technique, powder mixture of 50% Ti , 47% Ni and 3% Cu was prepared by mixing for two hours and compacted in a press machine using various compacting pressure (600, 700 and 800) MPa , sample was then sintered for 5 hrs in an electrical tube vacuum furnace using sintering temperature of (850?C, 900?C and 950?C) .phase analysis of samples was conducted by X-ray diffraction test, the effect of different sintering temperature and compacting pressure on the porosity, microhardness ,compression strength and the shape memory effect (SME) was studied, the result showed decrease in the porosity and increasing in the shape recovery ,compression strength and microhardness with increasing compacting pressure and at lower sintering temperature and hence the best results was at 800MPa compacting pressure and 850?C sintering temperature.
This extensive and thorough review aims to systematically outline, clarify, and examine the numerous exploratory data analysis techniques that are employed in the intriguing and rapidly advancing domain of functional MRI research. We will particularly focus on the wide array of software applications that are instrumental in facilitating and improving these complex and often nuanced analyses. Throughout this discourse, we will meticulously assess the various strengths and limitations associated with each analytical tool, offering invaluable insights relevant to their application and overall efficacy across diverse research contexts and environments. Our aim is to create a comprehensive understanding of how these tools can be best utilized to enhance research outcomes. Through this analysis, we aspire to equip researchers with critical knowledge and essential information that could profoundly influence their methodological selections in upcoming studies. By carefully considering these factors, we hope to contribute positively to the ongoing progression of this important field of inquiry, fostering innovation and enhancing the impact of future research findings in functional MRI studies.
Medical image segmentation plays a crucial role in the realm of medical imaging. The process involves the division of an image to obtain a comprehensive view and ensure precise diagnostics. There are various methods that are employed, ranging from traditional approaches to the more advanced deep learning techniques. Both play a significant role in enhancing healthcare. With the continuous advancement in technology, there is a growing need for accurate segmentation. While traditional methods such as thresholding and region growing are effective, they may require human intervention for complex cases. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the process by learning intricate details and accurately segmenting the image. When these methods are combined, healthcare professionals can achieve high-quality, precise results. Furthermore, with the advancements in hardware and technology, real-time segmentation is now possible. Generally, the process of dividing medical images into segments is extremely important for the progress of healthcare with the help of artificial intelligence and the most recent advancements in the industry, such as explainable AI and multimodal learning. However, this meticulously detailed and in-depth review provides an all-encompassing and extensive analysis of the current methods utilized, their multitude of applications across various fields, and the promising emerging advancements that have the potential to pave the way for remarkable future improvements and innovations.
There is very close relation between the pile capacity and surrounding soil conditions . In cohesionless soil the pile effected on surround soil by compact loose ,cohesionless deposits through a combination of pile volume displacement and driving vibrations .the pile foundation usually designed to exceed the weak soil to the firm deposit .in this study we shall try to improve the weak soil surround the pile and observe the effect of improvement on pile capacity for driven pile._x000D_ The improvement suggested in this study is compacting for surrounding soil . for this purpose we prepare testing program by selection two types of sand soil one as the origin soil and the other as improving soil (soil will be compacted and replace surround pile model) . pile model prepared for this purpose is consist of reinforcement steel bar covered with cement mortar , 50 kN automatic electromechanical compression machine was used for testing load- settlement test on pile model. The Testing procedure includes changing the diameter of soil compacted around pile model and execute the load settlement test and compare the results.
Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.
One of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.
Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.
Recently, three-dimensional models 3DM in the prosthetics field gained popularity, especially in the context of residual limb shape creation resulting from collecting medical images in Digital Imaging and Communications in Medicine DICOM format from a magnetic resonance imaging MRI after image processing accurately. In this study, a three-dimensional model of the residual limb for a patient with transtibial amputation was realized with the integration of artificial intelligence and a computer vision approach demonstrating the benefits of AI segmentation tools and artificial algorithms to generate higher accuracy three-dimensional model before prosthetic socket design or in case of comparison the 3D model generated from MRI with another 3D model generated from another technique, where a residual limb of a 23 years old male patient with amputation in the left leg wearing a prosthetic socket liner, and having 62 kg weight, 168 cm height, with high activity level. The patient was scanned using GE Medical Systems, 1,5 Tesla Signa Excite. MRI images in DICOM format were read to retrieve essential metadata such as pixel spacing and slice thickness. These images were processed to obtain a model that reflects the real shape of the residual limb using a specific algorithm, and the 3D model was extracted using AI segmentation tools. The obtained 3D model result with high resolution proves the potential of the artificial intelligence approach with deep learning to reconstruct 3D models concluding that AI has an instrumental role in medical image analysis, particularly in the areas of organ and tissue classification and segmentation., thus generating automatic and repetitive a 3D model.
Recently, considering polymer composite in manufacturing of mechanical parts can be caused a fatigue failure due to the very long time of exposure to cyclic loading and may at environmental temperatures higher than their glass transition temperature; therefore, in this paper, a comprehensive investigation for bending fatigue behavior at room and elevated temperatures equal to 60 °C, 70°C, and 80 °C will be done. Rotating bending test machine was manufactured for this purpose supplied with a connected furnace to perform fatigue tests at elevated temperatures. The obtained results appeared that the increase in applied stress and temperature caused a clear reduction in fatigue life; also the addition of carbon nanotubes enhanced the fatigue life at different temperatures by 183%, 205%, 218%, and 240%, respectively while the addition of short carbon fibers improved fatigue life by 324%, 351%, 387%, and 415%, respectively. As well as, Polyamide 6,6/carbon fiber composite appeared fatigue limit at temperatures equal to 20°C and 60°C and stresses approximately equal to 55 MPa and 38 MPa respectively.
Reverse Engineering is a process of re-producing existing parts by obtaining digital models using a special data taken from the original parts using specific techniques. It can be used to redesign existing parts either due to lost data or the parts are no longer available. In this paper, surface modelling technique using special data taken from CMM (Coordinate Measuring Machine) was employed to redesign a candle holder. Specific MATLAB code was generated to model the data taken from the surface of a candle holder made of glass. Bezier curve technique was implemented in this research to model the curve of the outer surface of the candle holder. Various orders of Bezier curves were discussed and used to give better approximation of the original data curve with error percentage monitoring each time. The thickness of the candle holder was reduced from 5mm to 3mm and the volume reduction was calculated. The amount of reduction in the glass volume when reducing the thickness was found to be 210mm3. In addition, the amount of increase in the area of glass section was calculated to be 138.5mm2. This reduction gives a better vision of the amount of glass saved using this procedure. Two different shapes were found and plotted by varying the control points coordinates.