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 the present research, AISI P20 mold steel was processed using the milling process. The machining parameters considered in the present work were speed, depth of cut (DoC), and feed (F). The experiments were designed according to an L27 orthogonal array; therefore, a total of 27 experiments were conducted with different settings of machining parameters. The response parameters investigated in the present work were material removal rate (MRR), surface roughness (Ra, Rt, and Rz), power consumption (PC), and temperature (Temp). The machine learning (ML) approach was implemented for the prediction of response parameters, and the corresponding error percentage was investigated between experimental values and predicted values (using the ML approach). The technique for order of preference by similarity to ideal solution (TOPSIS) approach was used to normalize all response parameters and convert them into a single performance index (Pi). An analysis of variance (ANOVA) was conducted using the design of experiments, and the optimized setting of machining parameters was investigated. The optimized settings suggested by the integrated ML–TOPSIS approach were as follows: speed, 150 m/min; DoC, 1 mm; F, 0.06 mm/tooth. The confirmation results using these parameters suggested a close agreement and confirmed the suitability of the proposed approach in the parametric evaluation of a milling machine while processing P20 mold steel. It was found that the maximum percentage error between the predicted and experimental values using the proposed approach was 3.43%.