Electrospun polymer nanofibers (EPNF) constitute one of the most important nanomaterials with diverse applications. An overall review of EPNF is presented here, starting with an introduction to the most attractive features of these materials, which include the high aspect ratio and area to volume ratio as well as excellent processability through various production techniques. A review of these techniques is featured with a focus on electrospinning, which is the most widely used, with a detailed description and different types of the process. Polymers used in electrospinning are also reviewed with the solvent effect highlighted, followed by a discussion of the parameters of the electrospinning process. The mechanical properties of EPNF are discussed in detail with a focus on tests and techniques used for determining them, followed by a section for other properties including electrical, chemical, and optical properties. The final section is dedicated to the most important applications for EPNF, which constitute the driver for the relentless pursuit of their continuous development and improvement. These applications include biomedical application such as tissue engineering, wound healing and dressing, and drug delivery systems. In addition, sensors and biosensors applications, air filtration, defense applications, and energy devices are reviewed. A brief conclusion is presented at the end with the most important findings and directions for future research.

In machining operations, minimizing the usage of resources such as energy, tools, costs, and production time, while maximizing process outputs such as surface quality and productivity, has a significant impact on the environment, process sustainability, and profit. In this context, this paper reports on the utilization of advanced multi-objective algorithms for the optimization of turning-process parameters, mainly cutting speed, feed rate, and depth of cut, in the dry machining of AISI 1045 steel for high-efficient process. Firstly, a number of experimental tests were conducted in which cutting forces and cutting temperatures are measured. Then the material removal rate and the obtainable surface roughness were determined for the examined range of cutting parameters. Next, regression models were developed to formulate the relationships between the process parameters and the four process responses. After that, four different multi-objective optimization algorithms, (1) Gray Wolf Optimizer (GWO) and (2) Weighted Value Gray Wolf Optimizer (WVGWO), (3) Multi-Objective Genetic Algorithm (MOGA), and (4) Multi-Objective Pareto Search Algorithm (MOPSA), were applied. The results reveal that the optimal running conditions of the turning process of AISI 1045 steel obtained by WVGWO are a feed rate of 0.050 mm/rev, cutting speed of 156.5 m/min, and depth of cut of 0.57 mm. These conditions produce a high level of material removal rate of 4460.25 mm³/min, in addition to satisfying the surface quality with a roughness average of 0.719 µm. The optimal running conditions were found to be dependent on the objective outcomes’ order. Moreover, a comparative evaluation of the obtainable dimensional accuracy in both dry and wet turning operations was carried out, revealing a minimal relative error of 0.053% maximum between the two turning conditions. The results of this research work assist in obtaining precise, optimal, and cost-effective machining solutions, which can deliver a high-throughput, controllable, and robust manufacturing process when turning AISI 1045 steel.

This article reports an extended investigation into the precision hard turning of AISI 4340 alloy steel when machined by two different types of inserts: wiper nose and conventional round nose. It provides a closer look at previously published work and aims at determining the optimal process parameters for simultaneously minimizing surface roughness and maximizing productivity. In the mathematical models developed by the authors, surface roughness at different cutting speeds, depths of cut and feed rates is treated as the objective function. Three robust multi-objective techniques, (1) multi-objective genetic algorithm (MOGA), (2) multi-objective Pareto search algorithm (MOPSA) and (3) multi-objective emperor penguin colony algorithm (MOEPCA), were used to determine the optimal turning parameters when either the wiper or the conventional insert is used, and the results were experimentally validated. To investigate the practicality of the optimization algorithms, two turning scenarios were used. These were the machining of the combustion chamber of a gun barrel, first with an average roughness (Ra) of 0.4 µm and then with 0.8 µm, under conditions of high productivity. In terms of the simultaneous achievement of both high surface quality and productivity in precision hard turning of AISI 4340 alloy steel, this work illustrates that MOPSA provides the best optimal solution for the wiper insert case, and MOEPCA results are the best for the conventional insert. Furthermore, the results extracted from Pareto front plots show that the wiper insert is capable of successfully meeting both the requirements of Ra values of 0.4 µm and 0.8 µm and high productivity. However, the conventional insert could not meet the 0.4 µm Ra requirement; the recorded global minimum was Ra = 0.454 µm, which reveals the superiority of the wiper compared to the conventional insert.

This article presents an experimental investigation and statistical analysis of the effects of cutting conditions on the machining performance of AISI 1045 steel using a wiper-shaped insert. Experimental findings are used to compare the machining performance obtained using wiper inserts with those obtained using conventional round-nose inserts as recently reported in the literature. In addition, the effects of process conditions, namely cutting speed, feed rate, and depth of cut, are analyzed in order to obtain optimum conditions for both types of inserts. The goal is to achieve the optimal machining outcomes: minimum surface roughness, resultant cutting force, and cutting temperature, but maximum material removal rate. A full factorial design was followed to conduct the experimental trials, while ANOVA was utilized to estimate the effect of each factor on the process responses. A desirability function optimization tool was used to optimize the studied responses. The results reveal that the optimum material removal rate for wiper-shaped inserts is 67% more than that of conventional inserts, while maintaining a 0.7 µm surface roughness value. The superior results obtained with wiper-shaped inserts allow wiper tools to use higher feed rates, resulting in larger material removal rates while obtaining the same surface quality.

A common practice in the construction of residential and commercial buildings in Saudi Arabia is to insulate the outer walls and windows only. Other building components such as the roof, columns and slabs, and doors are usually neglected. Moreover, vital components such as the roof and windows are especially neglected in commercially built residential and commercial buildings. The aim of this study is to put this common impression and practice to the test by quantifying the contribution of every building component to the overall air-conditioning load of the building. The hypothesis evaluated in this paper is that despite the common practices, there could be an optimum selection of insulators for the building components that yields the lowest energy consumption and maximum savings not only in energy costs but also installation costs. The required air-conditioning load is determined using manual calculations and the HAP software package for 1022 possible configurations. The findings of the analysis point to the importance of the roof, as it is the major contributor to the thermal load, followed closely by columns and slabs, with 44.2% of the overall cooling load. It is found that a single wall consisting of 2 cm of cement plaster, 20 cm of cement–polyurethane brick, and 2 cm of cement plaster is less expensive and has higher thermal resistance than any of the more expensive double walls. The study found one scenario of possible configurations with the optimized selection of building materials and their insulation materials that provides the most effective insulation at the lowest cost.

Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges.