The high cutting temperature and poor thermal diffusion efficiency of nickel-based alloys during deep hole machining have become technical challenges in the hole machining field. In this paper, a finite element simulation model of Inconel-718 BTA ordinary drilling and vibration drilling processes was established by using Deform-3D finite element simulation software. The variations in the temperatures of the tool teeth and the workpiece at different positions of the nickel-based alloy under ordinary drilling and vibration drilling were investigated. Additionally, the wear pattern of each tool tooth under the two drilling methods was further analyzed by building an experimental platform for workpiece temperature detection, which reveals the wear and cooling mechanism of nickel-based alloy BTA deep hole drilling. The results show that the average temperatures of the external, intermediate, and central teeth were reduced by 18.1%, 21.1%, and 17.8%, respectively, during vibration drilling. In addition, the workpiece hole wall and hole bottom temperatures were reduced by 5.7% and 4.6%, respectively. To conclude, the experimental tests were consistent with the simulated temperature trends. BTA vibration drilling optimizes the heat exchange conditions between the cutter teeth and the workpiece during the drilling of nickel-based alloys, which effectively reduces the cutting temperature and, thus, improves the wear resistance of the cutter teeth.

The internal stress difference between soft-ductile aluminum alloy substrate and hard-brittle Ni–W alloy coating will cause stress concentration, thus leading to the problem of poor bonding force. Herein, this work prepared the Ni–W graded coating on aluminum alloy matrix by the pulse electrodeposition method in order to solve the mechanical mismatch problem between substrate and coatings. More importantly, a backward propagation (BP) neural network was applied to efficiently optimize the pulse electrodeposition process of Ni–W graded coating. The SEM, EDS, XRD, Vickers hardness tester and Weighing scales are used to analyze the micromorphology, chemical element, phase composition, and micro hardness as well as oxidation weight increase, respectively. The results show that the optimal process conditions with BP neural network are as follows: the bath temperature is 30 °C, current density is 15 mA/cm² and duty cycle is 0.3. The predicted value of the model agrees well with the experimental value curve, the relative error is minor. The maximum error is less than 3%, and the correlation coefficient is 0.9996. The Ni–W graded coating prepared by BP neural network shows good bonding with the substrate which has flat and smooth interface. The thickness of the coating is about 136 μm, which slows down the oxidation of the substrate and plays an effective role in protecting the substrate.

Airborne capture net devices mounted on unmanned aerial vehicles (UAVs) provide an effective physical countermeasure against rogue drones. However, conventional deterministic designs neglect parameter uncertainties such as manufacturing tolerances and assembly errors that degrade flexible net deployment dynamics. To resolve this, this study proposes a multi-objective robust design optimization framework integrating a feedforward neural network (FNN) with uncertainty quantification. A lumped-mass dynamic model is formulated; static deployment experiments validate its high fidelity during the initial phase (t ≤ 0.08 s), while subsequent full-deployment and contraction phases are captured via experimentally calibrated numerical extrapolation. To mitigate computational costs, an adaptive FNN surrogate model is developed, reducing single-evaluation times from hours to seconds. Monte Carlo simulations coupled with the non-dominated sorting genetic algorithm II (NSGA-II) are then employed to optimize the mean and standard deviation of deployment and effective distances. Compared with deterministic optimization, the robust solution yields a 23.8% increase in the 95th percentile of deployment distance with only a 1.5% decrease in effective distance. Crucially, the standard deviations of these metrics decrease by 22.5% and 38.5%, respectively, demonstrating significantly attenuated sensitivity to parameter variations and offering a valuable engineering framework for robust airborne interception.