The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.

This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems.

Wildfire suppression technologies have become increasingly critical in the face of escalating climate challenges and growing threats to ecosystems and human settlements. While early detection remains essential for effective wildfire management, the speed and efficiency of suppression technologies are equally vital to containing the spread. Unmanned aerial vehicles (UAVs), particularly quadcopters, have been widely explored for tasks such as monitoring fire propagation and deploying fire-extinguishing balls. Despite their limitations in direct suppression due to harsh environmental conditions, quadcopters can be effective when deployed as part of an intelligent swarm system. However, aerial-only drones face serious constraints in hostile fire zones, prompting the need for innovative, ground-compatible drone designs. The main aim of this study to introduce a novel hybrid uncrewed system to the scientists in the field. The design is termed the Bimodal MorphoCopter (BMC), which incorporates a planar inverted slider-crank mechanism to enable seamless transformation between aerial (drone) and terrestrial (all-terrain vehicle, ATV) modes. The proposed mechanism offers morphable, bimodal functionality, allowing drones to switch modes and directly engage wildfires from the ground, particularly in areas surrounding settlements such as Los Angeles. To achieve this functionality, the retraction motion of the coupler through the output link must be transformed into a solely extension — a key mechanical challenge addressed in this research. The kinematic design problem is formulated and solved using the Bee Algorithm, an optimization technique inspired by the foraging behavior of honeybees. The results aim to enhance swarm-based wildfire intervention systems by providing robust, adaptive ground-air mobility in dynamic fire-prone environments.