The incorporation of micro-and nanoparticles into cutting fluids can substantially enhance their cooling and lubrication performance. This study investigates the lubrication and cooling mechanisms of castor oil-based nanofluids with hexagonal boron nitride (hBN) and alumina (Al₂O₃) nanoparticles, and the application of the nanofluids in minimum quantity lubrication (MQL). Six nanofluids were prepared with varying hBN‒Al₂O₃ volume ratios, and the wettability, viscosity, and tribological behavior of the nanofluids were evaluated. The performance of the nanofluids was tested via the cutting responses in the milling of 316L stainless steel. The results of friction tests show that hybrid nanofluids significantly reduce the friction coefficient by up to 76.28%. It can also minimize material wear rate and effectively inhibit the damage caused by hard particle shearing on the friction surface. The application of nanofluid MQL reduced cutting forces, tool wear, and surface roughness at low spindle speeds, with Al₂O₃‒hBN (2∶1) exhibiting the best overall performance. These findings demonstrate the strong potential of Al₂O₃–hBN hybrid nanofluids to markedly enhance MQL efficiency in milling operations. The results also provide key insights into the relationship between structure, property, and performance of the hybrid nanofluids and offer feasible strategies for optimizing sustainable machining processes.

Laser additive manufactured IN718 alloy components shows great potential for applications in high-end equipment such as aerospace engines; however, the high residual stresses introduced during the manufacturing process result in low-cycle fatigue behavior that differs significantly from that of conventional wrought or cast counterparts, thereby limiting the prediction accuracy of existing fatigue life models. To address this issue, a residual stress corrected Tanaka-Mura-Wu (TMW) fatigue life prediction model is proposed. The equivalent energy approach for mean stress correction is employed to introduce residual stress into the damage driving force expression in the form of equivalent strain energy, resulting in two correction formulations for strain-controlled and stress-controlled conditions. The validity of the modified model was verified using fatigue test data under both loading modes. Kendall’s tau coefficient revealed a significant negative correlation between residual tensile stress and specimen fatigue life. Under strain-controlled conditions, all predicted data points of the proposed corrected model fall within the twofold error band, and the root mean squared error (RMSE) value is reduced by 91% and 48% compared with the TMW and the Morrow-modified Manson-Coffin (M-MC) models, respectively. Under stress-controlled conditions, the RMSE value of the proposed corrected model is reduced by 58% and 66% compared with the TMW and the Smith-Watson-Topper modified Manson-Coffin (SWT-MC) models, respectively. These results confirm that incorporating residual stress into the TMW fatigue life model in the form of equivalent strain energy can effectively improve prediction accuracy, providing a theoretical basis for high-precision LCF life prediction and anti-fatigue design of laser additive manufactured Ni-based superalloys.

With the diversified development of aircraft structural forms, material properties, and mission modes, as well as increasingly harsh flight environments, higher requirements and new challenges have been posed for structural health monitoring (SHM) technologies. Fiber-optic sensors, with their unique advantages such as small core diameter, excellent flexibility, immunity to electromagnetic interference, integration of signal sensing and transmission, and ease of forming space-division/wavelength-division multiplexing monitoring arrays, have become an ideal choice for real-time multi-parameter monitoring and safety assessment of aerospace structures. This article briefly outlines common types of fiber-optic sensors suitable for aviation service environments, optimization configurations of fiber-optic monitoring networks, and self-healing methods. It comprehensively reviews the background requirements and current development status of multi-parameter monitoring in onboard environments, multi-physical field inversion, and identification methods for typical structural damage. On this basis, it summarizes several challenges and prospects faced by fiber-optic structural health monitoring technology in the aviation field.

Composite laminates are widely used in the aerospace industry due to their excellent mechanical properties; however, issues such as rapid damage progression and the difficulty of detecting initial damage have limited their further application. This paper employs a phase-transition–vacuum self-assembly method to fabricate a bionic thin-film sensor that combines rigidity and flexibility, enabling its low-damage embedding into glass fiber laminates and successfully monitoring the curing and flexural damage processes of composite materials. The thin-film sensor has a conductivity of up to 7680 S/cm, a sensitivity (GF value) of 4.01 within the recoverable range, a limit strain of 4.24%, and a strength of 13.58 MPa. When monitoring the curing of laminates, the sensor can clearly distinguish between the three stages of heating, temperature holding, and cooling. As verified by DMA, the resin gel point temperature can be determined from sudden changes in the sensor signal, with a maximum signal change rate of 35%. In drop-weight impact and cyclic three-point bending tests on laminates, the sensor demonstrated highly linear response characteristics. Sensors embedded in the neutral plane can accurately capture damage and step signals in the laminate prior to ultimate failure. This sensor enables full-process monitoring of composite structures from fabrication to failure, providing a viable approach for health monitoring of aerospace composite structures.

Aiming at the hail impact issue of aircraft composite structures, a spatially-constrained multi-task DenseNet (SCMT-DenseNet) is constructed to realize impact localization and energy classification. The four-channel guided wave signals collected by piezoelectric sensors are reconstructed into two-dimensional feature maps, and DenseNet is adopted to extract deep-level features. A spatial attention mechanism is embedded to strengthen the feature representation capability of critical regions. A three-branch architecture is designed for impact area classification, coordinate regression and energy grading. Joint optimization under spatial constraints is accomplished by means of dynamic Gaussian soft labels, grid graph Laplacian regularization and coordinate prior fusion. The experimental results demonstrate that the proposed method can achieve high-precision identification of hail impact positions and energy levels on composite plates. The accuracy of impact localization and energy grading reaches 96.1% and 99.6%, respectively. Moreover, favorable robustness can be maintained even when the number of sensors is reduced. This work provides an effective technical route for hail impact monitoring and evaluation of aircraft composite structures.

Power batteries for electric aircraft operate under complex service conditions, including high-rate discharge cycles, fast charging, temperature and pressure fluctuations, as well as vibration and impact loads. Their performance degradation and safety failure processes involve coupled electrochemical, thermal, and mechanical behaviors, imposing more stringent requirements on state monitoring and safety warning than those encountered in ground transportation applications. Conventional battery management systems (BMSs) primarily rely on external parameters such as voltage, current, and temperature for state estimation, making it difficult to directly perceive internal structural evolution and early-stage failure processes. To enhance battery health management and safety warning capabilities, smart battery technologies have attracted increasing research interest. This paper first analyzes the extreme operating environments, degradation and damage mechanisms, and state and safety monitoring requirements of electric aircraft power batteries. The development of key smart battery technologies, including thin-film sensors and fiber-optic sensing, is then reviewed. Particular emphasis is placed on recent advances in ultrasonic techniques for state-of-charge and state-of-health evaluation, degradation and mechanical damage identification, and early warning of thermal runaway. Finally, future development trends and key challenges for intelligent monitoring and health management systems of aviation power batteries are outlined.

Systematic experiments and CFD simulations were conducted to investigate the temperature-field evolution of particle-reinforced aluminum matrix composites during a deep cryogenic rapid heat treatment. After soaking in liquid nitrogen, specimens were plunged into boiling water; thermocouples embedded inside recorded the transient heating history, revealing the temperature-rise behavior. A 3D transient thermal-fluid-structure coupled CFD model was established based on Fluent software to simulate the heat transfer mechanism of multiphysics coupling effects during the specimen’s immersion into the hot medium. The results show good agreement between predicted and measured temperatures, confirming high model accuracy. A severe temperature gradient exists at the onset of heating, but the specimen rapidly equilibrates, its average temperature climbing from -194 ℃ to 92 ℃. During the rapid heating of the specimen, the maximum convective heat transfer coefficient at the cross-section reaches 2265.15 W/(m²·K). Throughout the process, a 95 ℃ cold annulus envelops the specimen wall, maintaining a 5 ℃ difference from the distant liquid.

This study investigated the microstructure characteristics of Ti65 alloy wires rolled in the β-phase region (T_β+20 ℃) and the α+β two-phase region (T_β‒30 ℃), as well as their influence mechanisms on tensile properties. The results indicate that differences in tensile properties are mainly affected by the texture characteristics of the α phase. The wire rolled in the β-phase region is composed of coarse α lamellar colonies with the c-axis parallel to the axial direction and fine α regions with similar texture, exhibiting strong texture and a high fraction of hard-to-slip orientations. Its tensile strength reaches 1248 MPa, which is significantly higher than that of the wire rolled in the α+β two-phase region. In-situ tensile EBSD analysis reveals that the hard-to-slip α lamellar colonies activate twinning deformation at the late deformation stage due to local stress concentration. The wire rolled in the α+β two-phase region consists of deformed primary α phase (α_p) and secondary α phase (α_s). A large fraction of α phase with a weak texture and high slip activity is predominant. Its plastic deformation is dominated by dislocation slip, resulting in lower tensile strength but higher ductility.

In this study, Al-6.3Cu-0.3Mn-0.3Zr-xEr alloys with varying Er contents were fabricated via a process route comprising casting, friction stir processing, hot rolling, and heat treatment. The influence of Er content on the microstructure and mechanical properties of the alloys was systematically investigated. The volume fraction of the Al₈Cu₄Er phase in the alloys increased with rising Er content. Friction stir processing effectively fragmented the Al₈Cu₄Er phases and promoted their uniform distribution within the matrix. This microstructural modification effectively suppressed grain coarsening during subsequent hot rolling. However, the excessive Al₈Cu₄Er phases formed by excessive Er addition (beyond an optimal level) inhibited the precipitation of grain-boundary pinning Al₃M phases and θ″ precipitates. This inhibition consequently led to grain coarsening and a reduction in alloy strength. The Al-6.3Cu-0.3Mn-0.3Zr alloys with an Er mass fraction of 0.15%-0.3% exhibited the optimum combination of mechanical properties.

Particle-reinforced metal matrix composites are widely used due to their exceptional mechanical properties. Among these, the distribution of silicon carbide reinforcing particles plays a critical role in determining their overall performance. Unlike ultrasonic velocity and attenuation methods, which can only capture the average characteristics along the acoustic wave propagation path, the ultrasonic backscattering method enables the resolution of microstructural information to be resolved at specific spatial points within the material. Based on time-dependent integrated backscattering intensity, this study proposes an evaluation method for characterizing the distribution of reinforcing particles in metal matrix composites, which enables the tomographic imaging of reinforcement particles. The calculation of the integrated backscattering intensity is derived from ultrasonic scattering theory, and a mapping relationship between spatial locations and backscattering intensity is established. Experimental verification is conducted using SiC_p/Al composites as the research subject. The results show that, compared with metallographic and CT imaging results, the proposed method can effectively identify inhomogeneous distribution patterns within the material, such as point-like agglomerations and local enrichments. Furthermore, the backscattering method demonstrates higher sensitivity than the velocity and attenuation methods.

Large thin-walled structures are widely used in aircraft and aero-engines. Due to their large size, small thickness, and low stiffness, manufacturing tolerances tend to couple with elastic deformation during assembly, resulting in surface distortion and step discontinuities that degrade stealth and aerodynamic performance. Accurate prediction of assembly deviations at the design stage can reduce cost and improve efficiency, and support structural and tolerance design. To this end, a stiffness modal decomposition-based method for predicting assembly deviations in large thin-walled structures was proposed. Finite element stiffness models and part design tolerances were taken as inputs. The stiffness matrices of individual parts were decomposed to construct basic deviation fields, and a deviation transfer mapping model was established based on assembly constraints. Monte Carlo simulation was used to model manufacturing tolerance distributions, yielding the statistical characteristics of assembly deviations. Comparison with a commercial three-dimensional tolerance analysis software shows an error of approximately 3.8%. The results show that the proposed method accurately predicts the statistical distribution of assembly deviations and provides reliable support for assembly accuracy control of large thin-walled structures.

Powder spreading quality represents a critical factor that affects the part forming and process stability during the laser powder bed fusion (LPBF) forming process. However, existing detection methods still face limitations in real-time capability, accuracy, and adaptability to complex process scenarios. To address the real-time detection challenges posed by complex backgrounds and multi-scale anomalies during the powder spreading process, this paper proposes the powder spreading detection transformer (PS-DETR), an improved lightweight object detection model based on the real-time detection transformer (RT-DETR). PS-DETR reconstructs the network backbone with efficient local-global context aggregation, which reduces the model complexity while enhancing its feature extraction capability. Meanwhile, it incorporates an attention-based feature concatenation module, effectively enhancing its ability to integrate and focus on multi-scale abnormal features. These improvements were trained and validated on the image dataset collected from the powder spreading process for lattice structures. Experimental results show that the proposed PS-DETR model achieves a mAP@50 of 94.35%, representing an improvement of 3.97 percentage points over the baseline model while reducing the number of parameters by 29.6%, which confirms the effectiveness of the proposed modules and the model's superior performance.

Laser cladding technology can significantly enhance the surface properties of materials, and the appropriate selection of process parameters is key to obtaining high-quality cladding layers. The changes in process parameters during laser cladding have a significant impact on the performance of the cladding layer. Subsequently, a multi-objective optimization of process parameters was conducted using response surface methodology (RSM) and the second-generation non-dominated sorting genetic algorithm (NSGA‒II), and the comprehensive performance under the optimal process parameters was analyzed. By utilizing a central composite design (CCD) experimental scheme, a mathematical model was established to analyze the interaction between input variables (laser power, scanning speed, and powder feed rate) and response values (dilution rate, aspect ratio, and microhardness). NSGA‒II was employed to optimize the process parameters, and the comprehensive performance of the cladding layer under optimal parameters was tested. The results of multi-objective optimization indicate that under the conditions of 1250 W laser power, 7.9 mm/min scanning speed, and 3.2 r/min powder feed rate, the cladding layer had a dilution rate of 18.8%, an aspect ratio of 4.712, and a microhardness of 624.38HV0.5, achieving the best match for the target response values. The cladding performance under optimal process parameters shows that the microhardness of the cladding layer reaches 637.1HV0.5, 2.5 times that of the substrate. Under the same wear conditions, the volume wear rate of the cladding layer is only 13.5% of the substrate. Electrochemical corrosion results indicated that the corrosion resistance of the cladding layer reaches 81.5% of the substrate. Tensile test results show that the cladding samples achieve a yield strength of 420 MPa, a tensile strength of 810 MPa, and a elongation of 25%. This optimization method and its results can provide a reference for the parameter design and performance control of laser cladding processes for similar materials.