The problem that the thermal safety of flexible electronic devices is difficult to evaluate in real time is addressed in this study by establishing a BP neural network (GA-BPNN) temperature prediction model based on genetic algorithm optimisation. The model uses a BP neural network to fit the functional relationship between the input condition and the steady-state temperature of the equipment and uses a genetic algorithm to optimise the parameter initialisation problem of the BP neural network. To overcome the challenge of the high cost of obtaining experimental data, finite element analysis software is used to simulate the temperature results of the equipment under different working conditions. The prediction variance of the GA-BPNN model does not exceed 0.57 °C and has good robustness, as the model is trained according to the simulation data. The study conducted thermal validation experiments on the temperature prediction model for this flexible electronic device. The device reached steady state after 1200 s of operation at rated power. The error between the predicted and experimental results was less than 0.9 °C, verifying the validity of the model’s predictions. Compared with traditional thermal simulation and experimental methods, this model can quickly predict the temperature with a certain accuracy and has outstanding advantages in computational efficiency and integrated application of hardware and software.

Buckling stability of thin films on compliant substrates is universal and essential in stretchable electronics. The dynamic behaviors of this special system are unavoidable when the stretchable electronics are in real applications. In this paper, an analytical model is established to investigate the vibration of post-buckled thin films on a compliant substrate by accounting for the substrate as an elastic foundation. The analytical predictions of natural frequencies and vibration modes of the system are systematically investigated. The results may serve as guidance for the dynamic design of the thin film on compliant substrates to avoid resonance in the noise environment.

The field of electronic skin has received considerable attention due to its extensive potential applications in areas including tactile sensing and health monitoring. With the development of electronic skin devices, electronic skin can be attached to the surface of human skin for long-term health monitoring, which makes comfort an essential factor that cannot be ignored in the design of electronic skin. Therefore, this paper proposes an assessment method for evaluating the comfort of electronic skin based on neurodynamic analysis. The holistic analysis framework encompasses the mechanical model of the skin, the modified Hodgkin–Huxley model for the transduction of stimuli, and the gate control theory for the modulation and perception of pain sensation. The complete process, from mechanical stimulus to the generation of pain perception, is demonstrated. Furthermore, the influence of different factors on pain perception is investigated. Sensation and comfort diagrams are provided to assess the mechanical comfort of electronic skin. The comfort assessment method proposed in this paper provides a theoretical basis when assessing the comfort of electronic skin.

The continuous expansion of wireless communication application scenarios demands the active tuning of electromagnetic (EM) metamaterials, which is essential for their flexible adaptation to complex EM environments. However, EM reconfigurable systems based on intricate designs and smart materials often exhibit limited flexibility and incur high manufacturing costs. Inspired by mechanical metastructures capable of switching between multistable configurations under repeated deformation, we propose a planar kirigami frequency selective surface (FSS) that enables mechanical control of its resonant frequency. This FSS is composed of periodically arranged copper square-ring resonators embedded in a kirigami-structured ecoflex substrate. Through simple tensile deformation, the shapes and positions of the square-ring resonators on the kirigami substrate are altered, resulting in changes to the coupling between capacitance and inductance, thereby achieving active tuning. Combining EM finite element simulations and transmittance measurements, we demonstrate that biaxial mechanical stretching allows for continuous adjustment of the FSS resonant frequency and −10 dB bandwidth. Additionally, the FSS exhibits excellent polarization and incident angle stability. Structural parameterization of the square-ring kirigami FSS was conducted to elucidate the deformation–electromagnetic coupling mechanism underlying the active tuning. These insights provide a foundation for guiding the application of square-ring kirigami FSS in various practical engineering domains.

The design of high-aspect-ratio wings enhances the flight efficiency of UAVs but also introduces significant aeroelasticity challenges. The efficient optimization of wing structures in complex environments has become critical. To address the current challenges in balancing wing strength with lightweight structural designs, this study proposed an intelligent solution method for optimizing wing dimensions and structural layout. Driven by mechanical simulation data, the method established a mapping relationship between the structural layout and dimensions of the wing and its bending stiffness. This approach was further enhanced by the mind evolution algorithm (MEA) to optimize the solution performance of the surrogate model. The wing structure optimization model was established using the multi-objective grey wolf optimizer (MOGWO) based on the surrogate model for search and optimization. This study focused on the composite material wing of a long-endurance unmanned aerial vehicle (UAV). The established MEA-BP surrogate model demonstrated high computational efficiency, with the prediction error standard deviation (STD) of wing deflection not exceeding 0.495 mm. The optimization model required 175 s to calculate the Pareto front solutions. The optimized structure resulted in a 28.32% increase in wing equivalent stiffness, and weight only increased by 6.67% compared to the original structure. These results showcased the effectiveness of the proposed method and validated the feasibility of integrating intelligent optimization algorithms and machine learning in the field of aircraft design.