Editor-in-chief: Prof. Dr. Basim K. Nile
The drilling operation was found in the early time of modern technology due to the importance of this operation in manufacturing. The drilling operation was developed to meet the requirements of modern industry. The study of factors that influence the drilling operation is very important to develop and enhance the performance of drilling tools and machining. The objective of this study is to prove the closeness between experimental and FE works to measure the temperature in a specified point on the workpiece. This closeness will be evidence of the reliability of FE temperatures to extract the linear and nonlinear expressions in the SPSS program. For experimental work, a 10mm diameter drill bit of 118ᴼ point angle was used to make three drilling operations by using a radial drilling machine. Three cutting speeds of (100-200-300) rpm, with fixed feed rate and drilling depth as 0.15mm/rev, and 3mm, respectively. The experimental operations are included in the FE study for validation. For numerical study, 3D simulations based on the FE method by using Deform-3D Ver.11 commercial software were executed to explain the influence of drilling parameters as well as drill bit point angle on the temperature generated in the workpiece material in dry drilling of AISI 304 stainless steel. Two drill bits of HSS have been used in this study with 10mm diameter and these tools are different in point angle of 110° and 118°, respectively. The drill bits are imported from a specific website in the format of STL. The workpiece modeling shape is cylindrical with a diameter of 50mm and 5mm thickness. The cutting parameters include three cutting speeds as (100, 200, and 300) rpm, and three feed rates as (0.15, 0.25, and 0.35) mm/rev, and the depth of drilling is constant for all operations as 3mm. The results provided a good closeness between experimental and their FE operations and the decision was to assume the FE temperatures reliable, and qualified to be used in SPSS work. The results showed that the temperature generated in the machined models increased with speed for both tools and the temperature generated by the tool of 118° point angle is higher than the temperature generated by the tool of 110° point angle. The influence of feed rate was also investigated. There was a good closeness between FE temperatures and nonlinear regression ones which indicated to consider the temperature variation as nonlinear in this study.
Single-phase induction motors are used in multiple applications in whichhaving the correct value of the capacitor linked to auxiliary winding allows the motor to work effectively. The current study used finite element analysis based on Magnet software to investigate the effect of changing the running capacitor on the performance of a single-phase squirrel cage induction motor with non-uniform stator slots, as well as using AutoCAD to model the stator due to its asymmetrical slots. The design documentation for a 0.5 hp, 36 stator slots, and 48 rotor bars, 25 µF, four-pole tested model are used to simulate the motor. The precision of model outcomes is confirmed successfully by comparing its outcomes of rated current and torque with motor nameplate data. The effect of changing the running capacitor on the performance of a single-phase induction motor is discussed in this study. To demonstrate the simulation's versatility in motor design, the auxiliary branch capacitor was modified (increasing and decreasing) and the effect of each instance on the motor's performance was investigated.
Lung cancer has repeatedly surfaced as among the most fatal diseases that humanity has ever known. It is also one of the highest frequent malignancies and one of the leading causes of mortality. Lung cancer cases are quickly expanding. The disorder has a desire to keep asymptomatic in its initial phases, the ability to manifest is exceedingly difficult. As a result, early tumor identification is critical in healing. The sooner a patient is diagnosed, the better his or her prospects of healing and survival. In order to effectively diagnose the condition, technology is crucial. Based on their observations, several researchers have come up with solutions. In the latest years, a number of computer-aided diagnostic (CAD) procedures and systems have been proposed, implemented, and produced in order to use digital transmission to tackle this issue. Such algorithms employ a variety of machine learning and deep learning approaches, along with multiple methodologies based on image processing-based approaches for predicting cancer malignant levels. The purpose of this research is to find, compare, and evaluate a variety of image categorization, semantic segmentation, and other methodologies for categorizing and identifying lung cancer in its early phases.
In this research, heating systems in Iraqi houses were discussed and analyzed to study their effect on energy consumption during winter. The study area was regionalized into three regions depending on prevailing climates: north, central, and south. An online survey was conducted to collect usage patterns and observe occupancy behavior related to heating systems in Iraq. Heating systems were classified into three major categories: electrical, fuel, and augmented. Detailed questions were included in the survey to collect as much data as possible for crosschecking and future research.
Consequently, the heating systems usage collected from the survey was classified into three main categories according to the type of energy utilized. Subsequently, a descriptive statistical analysis was carried out and followed by a Chi-square analysis to investigate the mutual relationships among different variables involved in the study. Results show that electrical systems dominate others due to the safety and cleanness characteristics compared to other fuel systems such as kerosene or natural gas. Education level and culture play essential roles since intellectual people pay more attention to safety and environmental health.
Alternative fuels are a fun renewable resource that can help minimize particle pollution from internal combustion engines. At a constant engine speed of 2500 rpm, a comparative numerical analysis was undertaken to analyze the impacts of four alternative fuels (ethanol, hydrogen, gasoline, and liquefied petroleum gas (LPG)) on exhaust gas emissions. Carbon monoxide, nitrogen oxide, and unburned hydrocarbons are all monitored as exhaust gases. According to this study, using fuels including ethanol and hydrogen can significantly reduce emissions. With hydrogen, the majority of hazardous contaminants in exhaust gas are significantly decreased. In comparison to gasoline, hydrogen contains relatively clean unburned hydrocarbons. Ethanol and hydrogen are clean fuels that do not contribute to increase in net emissions from engines. The findings showed that ethanol fuel emits less carbon monoxide than regular gasoline, but LPG emits more CO. Furthermore, ethanol fuel burns cleaner and produces less CO than gasoline. In comparison to LPG fuel engines, NOx emissions were greater for gasoline fuel engines. Nonetheless, ethanol-fueled engines created less NOx than gasoline-fueled engines. When working in lean conditions, the NOx emission of the hydrogen-fueled engine was about ten times lower than that of a gasoline-fueled engine. The studies also demonstrated that hydrogen fuel engines emit less HC pollution than gasoline fuel engines, but gasoline fuel engines emit more than ethanol fuel engines.