Particle Swarm Optimization Algorithm (PSOA) has emerged recently as an efficient and powerful technique for the optimization of real parameters. The current study presents control scheme for electro-hydraulic actuator system which utilizes particle swarm optimization (PSO) for off-line tuning of the Fuzzy Proportional-Derivative (Fuzzy PD) controller. The gains and Membership Functions (MFs) tuned by PSOA which has been implemented depending on the performance indices: ITAE (Integral Time of Absolute Error), ISE (Integral Square of Error), and IAE (Integral Absolute of Error).
Responding quickly and economically to the diversification of customer needs has forced manufacturing companies adopting approaches to delivering low cost, high quality sustainable products based on finding a link between the design or the manufacturing processes and other key elements of sustainability; economic, environmental, and social. However, these approaches had limited success. The most likely reason for the lack of integration between the design and manufacturing stages of the product and complexity of addressing the above mentioned three key elements of sustainability due to existing of many variables in relation to design, manufacturing, locations, logistic operations and so on. Taking into account the required integration as well as the associated complexity of considering sustainability elements can lead to large space alternative solutions and it is more difficult to use only exact methods to the optimization of such problem. This paper presents a genetic algorithm (GA) approach aiming to optimize a high sustainability performance by designing a product and the corresponding manufacturing processes for that product. Process optimization is carried out in terms of the highest fitness function achieved where different objectives are to be optimized simultaneously. The proposed GA approach is applied to the industrial case example. The proposed approach can assist decision makers to help explain when justifying their decision on what are the best product design and its manufacturing processes to obtain high sustainability performance.
The convergence of cloud and edge computing in smart manufacturing offers significant potential for improving efficiency in Industry 4.0. However, task scheduling in this context remains a complex, multi-objective challenge. This study introduces a novel Cloud-Edge Smart Manufacturing Architecture (CESMA), leveraging a hybrid approach that integrates NSGA-II and the Improved Monarch Butterfly Optimization (IMBO) algorithms. The combination utilizes NSGA-II's global search and non-dominated solution capabilities with IMBO's fine-tuning and local optimization strengths to enhance task scheduling performance. Where CESMA combines the scalability and analytics power of cloud computing with edge-based real-time decision-making to address the dynamic demands of smart manufacturing. Through extensive simulations and experiments, the feasibility and effectiveness of CESMA are validated, showing improved task scheduling quality, resource utilization, and adaptability to changing conditions. This research establishes a robust platform for managing the complexities of task scheduling in cloud-edge environments, advancing intelligent manufacturing processes, and contributing to the integration of evolutionary algorithms for real-time industrial decision-making
Designing large structures like dams requires carefully selecting various geometric, hydraulic, and structural characteristics. The required structural design and performance criteria are considered when selecting these characteristics. In order to find the best solution, a variety of restrictions must simultaneously be carefully taken into account. This study presents an effective method for determining the optimal shape design for concrete buttress dams. The research was divided into two crucial phases. The dam's initial design and subsequent modeling were mostly done using DIANA FEA and traditional design and stability analysis. After that, a genetic algorithm was used on the MATLAB platform to control optimizing the dam's shape. Three design factors were used in this phase to alter the goal function and to reduce the amount of Concrete used, which decreased project costs. These variables covered three areas of the buttress's cross-section. Two important limitations were scrutinized during this optimization process: establishing a safety margin against overtopping and preventing sliding. The analysis included a detailed assessment of Shear friction stability to complete a thorough stability study. The optimization efforts had a spectacular result, resulting in a significant 52.365% reduction in the total volume of Concrete used, dropping from 19147.5 cubic meters to 9122.55 cubic meters. This decrease was made possible by reducing three distinct components (X1, X2, X3), with respective proportions of 37.5%, 13.33%, and 30%, including two segments related to the buttress and the final segment linked (slab) to the strip footing.
Indeed, there are many hydrology variables influence on the operating of dam and reservoir system. Thus, modelling of dam operation is a complicated issue due to the nonlinearity of such hydrological parameters. Hence, the identification of a modern model with a high capacity to cope with the operation of the dam is extremely important. The current research introduced good an optimization algorithm, namely Genetic Algorithm (GA) to find best operation rules. The main aim of the suggested algorithm is to minimize the difference between irrigation demand and water release value. The developed algorithm was applied to find operation rules for Timah Tasoh Dam, Malaysia. This research used significant evaluation indexes to examine the algorithms' performance. The results indicated that the GA method achieved low Vulnerability, high Resilience and Reliability. It has been demonstrated that the GA method will be a promising tool in dealing with the problem of dam operation.
RSM and DOEs approach were used to optimize parameters for hypoeutectic A356 Alloy. Statistical analysis of variance (ANOVA) was adopted to identify the effects of process parameters on the performance characteristics in the inclined plate casting process of semisolid A356 alloy which are developed using the Response surface methodology (RSM) to explain the influences of two processing parameters (tilting angle and cooling length) on the performance characteristics of the Mean Particle Size (MPS) of α-Al solid phase and to obtain optimal level of the process parameters. The residuals for the particle size were found to be of significant effect on the response and the predicted regression model has extracted all available information from the experimental data. By applying regression analysis, a mathematical predictive model of the particle size was developed as a function of the inclined plate casting process parameters. In this study, the DOEs results indicated that the optimum setting was approx. (44) degree tilt angle and (42) cm cooling length with particle size (30.5) μm
Hilla cities center of province Babil is one of the most important cities in Iraq 100 km (62 mi) south of Baghdad .its relate the Governorate Baghdad with Governorates south Al-Thawra signalized intersection is one of the most important intersections in AL-Hilla city Being a major crossing point to the governorates of holy Karbala and Baghdad.The excessive traffic volumes, during the peak periods (at morning and evening), of vehicles that entering the intersection increase traffic density, reduce travel speed, increase travel time, and increase the delay values This paper aims to assess the traffic performance of Al-thawra signalized intersection in Babil city that is consistent with the existing conditions and intersection's geometric properties. For all approaches, video recording has been used to collect traffic volume data. These data are taken out of videos from Babil police cameras (department of communications and information systems)..The intersection was evaluated and analyzed using the SYNCHRO 10.0 software, and the best option was selected. The outcome of the evaluation process indicated that the intersection is operated at the level of service (LOS F) and with a control delay of 162.5 seconds.by recommending a variety of strategies, ranging from signal optimization to geometric enhancements. The suggestion of widening the pavement in the north-south direction was found to be the best solution. resulting in a decrease in vehicle delays from 162.2 seconds to 95.4 seconds and the level of service remains constant (LOS F) considered an acceptable and cost-effective solution to the intersection's problems.
Composite pressure vessels (i.e. types III and IV) are widely used for compressed natural gas (CNG) vehicles, as storage cylinders to reduce the weight while maintaining high mechanical properties. These vessels can achieve 70-80% of weight saving, as compared to steel vessels (type I). So, prediction of first ply failure and burst pressure of these vessels is of great concern. Thus, this paper involved a review of literature regarding the first ply failure and burst pressure of composite pressure vessels (types III and IV). The review included the researches related to the simulation, mathematical modeling, and experimental analysis. The study focused on simulation-related research more than others due to the complexities of mathematical modeling of such problems in addition to the high cost of experimental tests. The results indicated that the stacking sequence of layers, vessel thickness and the type of selected composites were the main factors that mainly affect the vessel burst pressure performance. Accordingly, the optimization in the vessel structure (composite fabric architecture) parameters plays an important role in the performance of burst pressure. This in turn will lead to a high vessel durability, longer life-time and better prediction of burst pressure. Furthermore, the study showed that the prediction of first ply failure is more important than burst pressure knowledge of pressure vessels because it gives an initial prediction of vessel failure before the final failure occurrence. This in turn, may prevent the catastrophic damage of vessel.
This study aims to improve different properties of sustainable self-compacting concrete SCC containing treated and modified polyethylene terephthalate PET fibers. For this purpose, gamma ray surface treatment and geometric modification were utilized for the used PET fibers. Concrete fresh properties include slump flow, T500mm, L-box and sieve segregation while mechanical properties include compressive, split tensile strength, flexural strength, static modulus of elasticity and impact strength. Further, physical properties and related durability properties comprise dry density, ultrasonic pulse velocity, porosity and water absorption. The results obtained demonstrated that the treatment and the modification used for the PET fibers slightly reduced the fresh properties of produced sustainable SCC (slump flow, T500 mm, L-Box and sieve segregation). However, they were within the limits of the SCC specification as reported in EFNERC guidelines. Further, concrete hardened properties in terms of compressive strength, splitting tensile strength, flexural strength, modulus of elasticity, impact strength, ultrasonic pulse velocity, decrease in the dry density, decrease in porosity and water absorption increased significantly.
The increasing price of fossil derivatives, global warming and energy market instabilities are a major problem. In recent years, these problems led to an increasing using of renewable energy sources such as wind energy. Wind turbine used to extract this energy from the wind to produce power or electricity. Due to low cost, easy for maintenance and it is, portability the most com-monly used among wind turbines is small axis wind turbine. Analysis to optimization power coef-ficient ( ) of a small wind turbine blade design model (Primus Wind power AIR 40 Wind Tur-bine 12VDC) are evaluated and discussed in this study. A shape of blade wind turbine is the pri-mery parapeter affected the power output of wind turbine. In this type of turbine NACA2411 used as the blade airfoil as represent shape of blade. For this goal, 185 different airfoils selected. For this purpose, using the XFOIL software to simulate the properties of each airfoil at Re (1.0*105, 1.5*105, 2.0*105, 2.5*105, 3.0*105 and 3.5*105) and angle of attack from 0˚ to 10˚, Then elimination criteria was performed for removing those airfoils would not suitable for the purpose up on their effiency. At the end of analysing Matlab software used for calculate the power coeffi-cient and selecting the best airfoils design for used manufacture anew blade for that type of small wind turbine with better power coefficient. The output of XFOIL and matlab software showed by tabulates and graphs. As a results show 3 airfoils were selected due to their performance better than other airfoils from an initial group of 185 as exemplification of the methodology namely S1210,SD7034 and S2091, The maximum that has been achieved by which used airfoil S1210 equal to 0.52 at Re 350000.
Turning is the most popular machining operation. The quality of the product may be determined using a variety of metrics, such as the surface generation method and the surface roughness of the product. This work uses cutting variables to obtain the best surface quality through a mathematical model. The suggested surface generation in this work results from deriving it using the Bezier technique, with degree (5th) having six chosen control points. One of the critical indicators of the quality of machined components is the surface roughness created during the machining process. Surface roughness improvement via machining process parameter optimization has been extensively researched. The Taguchi Method and actual tests were employed for evaluating the surface quality of complicated forms; regression models with three different variables for the cutting process, such as cutting speed, depth of cut, and feed rate, were also used. According to the experimental findings, the most significant effect of feed rate on the surface roughness is approximately (40.9%), and the more minor effect of depth of cut on the surface roughness is almost (16.23%). In addition, the average percentage error is 4.93%, the maximum error is 0.14 mm, and the minimum error is -0.143 mm for the prediction using the regression equation.