It is important to join materials for systems that require high-performance and to minimize the defects that may occur during this joining. Welding is the most common way for joining materials but for lightweight and similar/ dissimilar materials, Friction Stir Welding preferable for its high-performance joining properties. Lightweight and durable materials such as aluminum alloys are widely used in sectors such as defense industry, aerospace industry, automotive industry, and high-speed train manufacturing. Some of these materials cannot be welded by conventional methods due to their high thermal conductivity and low melting point. In welding processes, material properties are expected to be as close as possible to base material. Friction stir welding (FSW) is a joining method that provides welding below the melting point of materials that cannot be welded by conventional methods or where the welding process causes the mechanical structure of the material to deteriorate. In this study, Friction Stir Welding process, advantages and disadvantages and application fields of Friction Stir Welding were examined.

Solar air heaters play a crucial role in distributing heated air at low to medium temperatures. The heart of these systems lies in the absorber plate, which directly absorbs solar heat energy and then efficiently transfers it to the flowing air. However, the challenge lies in achieving optimal thermal efficiency by modifying the absorber plate roughness. Traditional smooth absorber plates have a limited contact area for heat transfer, leading to suboptimal performance. Using passive techniques, such as corrugation on the absorber plate, may increase thermal efficiency by creating turbulence in the laminar sublayer. In this study, corrugated solar air heaters are considered, with the absorber plates roughened into square, semicircular, and triangular ribs. The flow characteristics of heat due to the roughness of the absorber plate is simulated using the computational fluid dynamic (CFD) technique. The ANSYS Fluent 2019R3 is used to investigate the turbulent air flow in the absorber plate. The simulation analyses are performed over a Reynolds number range of 4000–18,000 using three different pitches. As the main contribution, two different model equations, namely M1 and M2, for the correlation model of the solar heat transfer were proposed to predict the Nusselt number, where M2 is an original model and M1 is newly established with the fine-tuned coefficients. Also, for the first time in the literature, the recent swarm optimization algorithm, namely Honey Formation Optimization with Single Component (HFO-1), is used to optimize the solar air heater and a comparison study with a popular non-swarm optimization, namely the Generalized Reduced Gradient (GRG) optimization, is provided. The Nusselt number was obtained using HFO-1 with a percentage error (MAPE) of 2.15% and 0.86% for the models M1 and M2, respectively. Moreover, the average achievement of HFO-1 on the proposed correlation models is 50% better than that of the GRG optimization, with respect to the RMSE.

This review explores the integration of machine learning (ML) and artificial neural networks (ANNs) in optimizing alloy production modeling and print control within Laser Engineered Net Shaping (LENS), a key additive manufacturing process. It investigates theoretical foundations, methodologies, case studies, and emerging trends to enhance process efficiency, improve product quality, and accelerate production cycles. A comprehensive literature review was conducted across academic databases and industry reports using keywords such as “machine learning,” “artificial neural networks,” and “Laser Engineered Net Shaping.” Both theoretical and experimental perspectives were analyzed to provide a well-rounded discussion. Findings indicate that ML and ANN models enhance understanding of alloy production, optimizing configurations and reducing defects. Real-time ML-driven optimization enables adaptive adjustments to process parameters, ensuring improved quality and accuracy. ANNs effectively predict key alloy microstructure properties, supporting informed decision-making and process refinement. Integrating ML and ANNs into LENS facilitates adaptive manufacturing, dynamically responding to changing conditions and alloy compositions.

Accurate temperature regulation is essential for ensuring product quality, operational safety, and energy efficiency in industrial electric furnace systems. However, the inherent thermal inertia, time-delay effects, and nonlinear dynamics of furnace processes often make precise temperature control a challenging task. Motivated by these challenges, this study proposes an optimization-based control framework aimed at improving the temperature regulation performance of electric furnace systems. The proposed approach integrates a proportional–integral–derivative (PID) controller with the recently developed gazelle optimization algorithm (GOA) for automatic tuning of the controller parameters. First, a mathematical model of the electric furnace is established to describe the dynamic relationship between the control input and the furnace temperature output. Based on this model, a PID controller is implemented to regulate the furnace temperature. The parameters of the PID controller are then optimized using GOA, a nature-inspired metaheuristic algorithm that mimics the adaptive predator–prey survival strategies observed in gazelle herds. In order to achieve a balanced improvement in both steady-state and transient performance, a composite objective function is introduced. The proposed performance index combines the integral of absolute error with additional transient performance indicators related to maximum overshoot and settling time. The effectiveness of the proposed GOA-based tuning framework is evaluated through extensive simulation studies and statistical analyses conducted over multiple independent optimization runs. The results demonstrate stable convergence behavior, with the optimization process achieving a minimum objective value of 2.4251, a maximum value of 2.5347, and an average value of 2.4674 across 25 runs. The optimized control system exhibits improved dynamic characteristics, including a rise time of 1.8509 s, a settling time of 3.6834 s, and a low overshoot of 1.5104%. To further assess its effectiveness, the proposed GOA–PID control strategy is compared with several widely used controller tuning methods reported in the literature, including genetic algorithm, Ziegler–Nichols, Cohen–Coon, Nelder–Mead, and direct synthesis approaches. Comparative results indicate that the proposed method achieves a superior balance between response speed, stability, and temperature tracking accuracy.

Superhydrophobic surfaces offer many advantages beyond just being hydrophobic (water repellent) to the surface. The superhydrophobic property can be achieved by artificially creating geometric structures on the material surface. These geometric structures reduce the contact area between the liquid and the surface. The contact angle between the liquid and the surface gives rise to two conditions: hydrophobic and hydrophilic. If the contact angle between the surface and the liquid is above 90 degrees, a hydrophobic state occurs. If the angle is below 90 degrees, the surface is in a hydrophilic state. One of these two states is determined depending on the need and provides alternative solutions for many problems that currently await engineering interventions. Scientific studies in the field of superhydrophobia are increasing day by day. Interest in superhydrophobia is expected to grow further, as it offers environmentally friendly and economical solutions to ongoing challenges in various sectors. Superhydrophobic materials also offer a method of preventing icing due to their ability to prevent liquid retention on the material surface through their water repellent properties. Since the reduction of the contact area between the liquid and the material surface on superhydrophobic surfaces leads to a decrease in the friction factor, the friction of the flow on the material will also decrease. These properties of superhydrophobic materials generate interest in sectors such as aviation and marine. This study describes the properties of superhydrophobic surfaces created through various methods on materials, focusing on applications such as anti-icing and reduction of friction factor.

Photonic crystals (PCs) hold great potential for designing new optical devices because of the possibility of the manipulation of light with PCs. There has been an increase in research on tuning the optical properties of PCs to design devices. We design organic semiconductor-based PC structures and calculate optical properties using the plane wave expansion method and finite-difference time-domain method in an air background for a hexagonal lattice. We showed the possibility of the solar cell arrays for a 2D PC cavity on an organic semiconductor base infiltrated with a nematic liquid crystal. E7 type has been used as a nematic liquid crystal and 4,4′-Bis[4-(diphenylamino) styryl]biphenyl as an organic semiconductor material.