Keyhole pores are a common defect in laser powder bed fusion (LPBF) technology, particularly prevalent in thin-walled structures, and significantly affect their mechanical properties. Current research on keyhole pore detection predominantly uses supervised learning models to enhance the understanding of keyhole formation mechanisms, but most studies focus only on single-track or block samples. However, due to the difficulty in accurately labeling keyhole pores in complex thin-walled structures, an effective recognition method for this issue has not yet been fully established. Given that industrial LPBF typically employs stable process parameters, this study developed a method combining melt pool image analysis with unsupervised anomaly detection to identify pore-prone regions in thin-walled structures. This approach reduces labeling costs, and enhancing robustness and generalization capabilities. Thin-walled specimens with varying overhang angles and wall thicknesses were manufactured to systematically analyze the correlation between surface quality, pore distribution, and melt pool features. The results indicate that areas with high roughness on the down-skin surfaces of overhanging sections exhibit lower melt pool intensity. In contrast, keyhole pores tend to concentrate in regions with higher melt pool intensity. Building on the above research, a Transformer-based architecture is combined with a ProbSparse Attention and depth-wise separable convolution, enabling the extraction of multi-scale features from melt pool dynamics. Compared to Transformer models, the proposed model achieved an F1 score of 90.79 % in predicting pore-prone regions. Additionally, the inference speed is 1.204 ms per batch of 512 time-series samples, demonstrating the model's effectiveness and efficiency in accurately localizing defects in thin-walled structures.

Polyether-ether-ketone (PEEK) manufactured via fused deposition modeling (FDM) has demonstrated significant potential in aerospace applications, driven by its exceptional properties such as high strength, outstanding high-temperature resistance, and other superior characteristics. However, the persistent issue of weak interlayer bonding strength induced by FDM processing remains unresolved, with its mechanisms not yet thoroughly elucidated. In this work, a multiscale approach combined with the molecular dynamics (MD) and finite element analysis (FEA) is proposed to characterize the failure behavior of interlayer bonding of FDMed PEEK. A molecular-scale model of FDM interface mechanical performance for PEEK extruded and deposited layers is established using polymer consistent force field (PCFF). Utilizing this model, MD simulations are conducted, yielding PEEK interlayer performance and interfacial behavior. These parameters are incorporated as input parameters for macroscale FEA, enabling simulation analysis of PEEK interfacial failure behavior. Therefore, the interlaminar shear strength of PEEK is predicted using this approach and subsequently validated through experiments.

With the increasing complexity of modern electromagnetic (EM) detection, the stealth and comprehensive performance of military equipment are required. To address this, a pneumatically actuated reconfigurable broadband absorbing metamaterial is proposed herein, based on a multistable structural design. By integrating multistable lightweight flexible elements with a pneumatic driving mechanism, dynamic modulation of the EM absorption performance is achieved. Research demonstrates that hybrid algorithm-based rapid optimization can realize ultra-wideband absorption across the frequency range of 2–18 GHz, achieving a relative bandwidth of 160%. The metamaterial exhibits excellent polarization and angle insensitivity. The coupled design of the multistable reconfigurable structure not only provides buffered energy absorption for stable state configurations but also extends the absorption bandwidth to 2–40 GHz, covering the entire S-Ka band. Simulation applications demonstrate that the designed metamaterial significantly reduces radar cross section across a broad frequency range. Moreover, it achieves corresponding stealth performance against radars detecting from different directions through structural reconfiguration, offering an innovative solution for future intelligent stealth technologies and adaptive radar countermeasures.

Multi-configuration lattice structures have recently been introduced into structural optimization due to their broadly tunable physical properties. Traditional methods for multi-configuration lattice optimization employ extreme strategies of complete fusion or separation, leading to a tradeoff between optimality and scalability that has not been fully addressed in the existing literature. The paper suggests decomposing the lattice library into pairs of lattices, through which multi-configuration lattice optimization is decoupled into the concurrent optimization of iso-value, combination category, and ratio within combination. A novel hybrid interpolation scheme is proposed to describe the effective mechanical behavior of the configuration-decoupled lattices. In this approach, polynomial models are employed to characterize the performance of individual lattice combinations, while the Uniform Multiphase Materials Interpolation model is used to integrate the contributions of all combinations. Benchmark experiments, including full-scale simulations, are conducted to validate the effectiveness of the framework. The proposed method enables rapid convergence to configuration layouts that align with the principal stress orientations. Compared to single-and dual-configuration designs, it achieves compliance reductions of 61.0% and 26.2 %, respectively, approaching the performance of density-based topology optimization. Extended numerical experiments reveal the joint influence of resolution and configuration count on the overall performance. This method achieves a better trade-off between optimality and extensibility, enabling more flexible utilization of large lattice databases in practical engineering fields.

Lack-of-fusion (LOF) defects critically degrade the structural integrity of laser powder bed fusion (LPBF) components, yet their reliable identification via in-situ monitoring remains elusive due to an incomplete understanding of defect evolution during multi-layer deposition. A primary source of diagnostic inaccuracy arises from the pronounced mismatch between transient, layer-resolved defect states captured by monitoring signals and the final defect morphology retained in the consolidated part. To elucidate the dynamic evolution of LOF defects, we developed and experimentally validated a high-fidelity multi-physics modeling framework that quantitatively resolved the coupled thermal, fluid, free-surface, and powder-melt interactions governing defect behavior. The model enabled direct tracking of LOF evolution across successive layers and revealed three fundamental evolution modes: trans-layer inheritance, inheritance termination, and self-healing. The mechanisms linking molten pool dynamics to multi-layer LOF defect evolution were revealed. The critical conditions governing their occurrence and transitions, including defect size, molten pool geometry, and local thermo-fluidic fields, were systematically identified. Based on these modes, we classified six representative multi-layer LOF evolution pathways. The results demonstrate that defect self-healing can substantially modify or eliminate initially formed LOF defects, leading to false-positive indications in surface-signal-based in-situ monitoring. This work provides the first systematic mechanistic framework for multi-layer LOF evolution in LPBF, offering quantitative guidance for interpreting in-situ signals and for developing physics-informed monitoring and defect-mitigation strategies.

To address the insufficient interlayer mechanical properties of glass fiber-reinforced polyetheretherketone （GF/PEEK） composites fabricated by fused deposition modeling （FDM）， resulting from the impracticality of post-process heat treatment for large-scale components and structural circuit-integrated components， an in situ thermal radiation-assisted strengthening method was proposed to enhance the interlayer mechanical properties along the build direction. The mechanical properties and interlayer strengthening mechanism of GF/PEEK composites under different thermal treatment strategies were systematically investigated by regulating the chamber temperature and in situ thermal radiation power， combined with tensile tests， interlayer tensile tests， and fracture surface morphology analysis. The results showed that increasing the chamber temperature improved the tensile properties in the horizontal direction. At a chamber temperature of 200 ℃， the tensile strength and Young’s modulus reached 62.72 MPa and 3.45 GPa， respectively. However， the interlayer tensile strength decreased from 20.61 MPa to 6.03 MPa， while the interlayer Young’s modulus increased from 1.90 GPa to 2.24 GPa. By contrast， in situ thermal radiation significantly enhanced the interlayer mechanical properties of specimens fabricated along the build direction. At a thermal radiation power of 255 W， the vertical tensile strength and fracture elongation increased to 2.34 and 7.91 times those of conventional FDM specimens， respectively， achieving mechanical properties comparable to those of post-process heat-treated specimens. Fracture surface analysis further revealed that in situ thermal radiation promoted interlayer polymer chain diffusion and molecular entanglement while reducing interfacial defects， thereby substantially improving the interlayer bonding performance. The proposed method effectively enhances the interlayer mechanical properties of FDM-fabricated GF/PEEK composites without requiring an additional post-process heat treatment， providing an effective processing strategy for the additive manufacturing of high-performance thermoplastic composite structures.

Carbon Fiber Reinforced Polymer (CFRP) and aluminum stacked are widely used in aircraft assemble thanks to the high strength-to-weight ratio. Riveting is an important joining technique of stacked structure and requires drilling and countersinking. Robotic machining systems are gradually used in the machining of holes due to their high flexibility. However, weakly rigid stacked structure and low-stiffness industrial robot system bring about complex and diverse countersinking depth errors, which significantly affects the fatigue life of components. In this paper, the influence mechanism of ultrasonic energy on the accuracy of robotic countersinking of stacked structure is investigated. Firstly, a workpiece deformation model is established with the thin-walled plate deformation theory, defined as static error. Then, the vibration of the industrial robot is calculated from the acceleration with the frequency domain integration, defined as dynamic error. The suppression of ultrasonic energy on the two kinds of errors were elucidated, respectively. Base on this, a depth compensation model of robotic ultrasonic countersinking is established. Finally, the feasibility of the accuracy compensation is experimentally verified, and the countersinking depth error can be controlled within +/- 0.09 mm. (c) 2025 Published by Elsevier Ltd on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Online melt pool monitoring has become a key enabler for quality assurance in laser powder bed fusion (LPBF). However, two geometrical quantities that strongly influence defect formation (melt pool depth and layer height) are difficult to measure directly during processing and are typically obtained via time-consuming destructive metallography. This study proposes a data-driven virtual sensing framework to predict melt pool depth and layer height for single-track LPBF of AlSi10Mg by fusing coaxial high-speed imaging features with process parameters. A coaxial high-speed camera (10 kHz) is integrated into the LPBF system to capture melt pool images, from which features describing melt pool size, shape, intensity, and texture are extracted and combined with laser power and scanning speed. We benchmark three regression models-support vector regression (SVR), random forest (RF), and a deep neural network (DNN) -under different input configurations and then retrain the selected model on the full dataset of 330 tracks using an 80/20 train-test split. The combined-input DNN consistently outperforms SVR and RF for both targets, achieving higher R2and lower RMSE, and a mode-aware optimization strategy further reduces extreme prediction errors near regime-transition boundaries (non-fusion and keyhole-like conditions). The proposed framework enables accurate, interpretable prediction of melt pool geometry from in-situ monitoring signals, supporting process-window design and reducing reliance on destructive measurements.

Significance Laser powder bed fusion(LPBF)is a key additive manufacturing process for producing high-performance,lightweight,and multifunctional complex metal components.However,the transient multi-physics nature of LPBF poses significant challenges to process stability,often leading to defects such as lack of fusion,porosity,and cracking,which can severely compromise the structural integrity and service reliability of fabricated parts.For advanced applications like multi-material LPBF,the fabrication process becomes even more intricate due to variations in thermo-physical properties among different materials,further intensifying the demand for robust in situ monitoring.In situ monitoring and diagnostic evaluation are therefore essential for assessing product quality and enabling stable,repeatable LPBF production.Artificial intelligence(AI),with its powerful capability to extract critical features from high-dimensional,noise-contaminated,and multi-source heterogeneous datasets,plays a pivotal role in enhancing the accuracy,intelligence,and real-time capability of defect diagnosis.This review first summarizes the requirements for in situ monitoring based on an analysis of defect formation mechanisms.Subsequently,it comprehensively reviews recent advances and limitations in LPBF in situ sensing technologies.The current state of AI applications in defect diagnosis is thoroughly discussed,and perspectives on the future development of AI-driven LPBF monitoring and control are provided. Progress Research on defect formation mechanisms in LPBF indicates that coupled multi-physics interactions,melt-pool behavior under cyclic heating-cooling and extremely non-equilibrium solidification,and various instabilities are the primary causes of defects.For multi-material LPBF,the process involves disparate material properties,and a single set of process parameters is often insufficient,making lack of fusion and cracking prevalent at material interfaces and leading to interfacial failure.The highly variable printability in multi-material LPBF results in more complex process signatures,imposing stricter requirements on the sensitivity,accuracy,and reliability of in situ monitoring as well as the precision of process control.Accordingly,LPBF in situ defect monitoring primarily targets unstable factors during fabrication,abnormal variations in physical fields,and changes in molten pool geometry and dimensions,enabling the acquisition of key signatures associated with defect initiation and evolution.These feature data form the basis for subsequent defect diagnosis and process control. Current LPBF in situ monitoring technologies encompass multiple sensing modalities,including optical,thermal,acoustic,and electromagnetic signals.Optical monitoring based on industrial cameras provides layer-wise information on powder spreading quality,surface morphology,and geometric features,while high-speed cameras capture transient melt-pool dynamics,spatter,and plume evolution on micro-to millisecond timescales.In thermal monitoring,photodiodes acquire full-field time-series signals of molten pool radiation intensity via point-wise measurements;thermal tomography and infrared thermography enable full-field temperature mapping during and after deposition.To mitigate measurement errors caused by dynamically changing surface emissivity,dual-wavelength ratio thermometry,which synchronously acquires radiation at two different wavelengths,can effectively improve temperature measurement accuracy.Furthermore,emerging techniques such as multispectral thermometry,thermionic emission monitoring,and embedded-sensor thermometry are under active development.For acoustic monitoring,laser ultrasonics enables near-surface and internal defect detection,whereas acoustic emission monitoring captures acoustic signatures generated by process instabilities.Coil-based or magnetoresistive sensor-based eddy current testing is a major approach in electromagnetic monitoring for in situ defect detection;meanwhile,near-field microwave imaging and related techniques are gradually being introduced into LPBF monitoring. In the realm of defect diagnosis and control,AI has become an indispensable tool for improving diagnostic accuracy and enabling intelligent control.For single-signal defect diagnosis,machine learning and deep learning models(e.g.,SVM,random forests,CNN/Transformer variants,and RNN/LSTM hybrids)have been widely applied to analyze different sensing signals for LPBF defect diagnosis.These approaches perform feature extraction,classification of defect-inducing factors,and defect identification with high reported accuracy.To overcome the limitations of single-signal monitoring and to reduce false alarms and missed detections,multi-signal fusion methods are being urgently developed.Multi-signal data fusion for defect diagnosis includes AI-driven integration of heterogeneous signals(e.g.,optical,thermal,acoustic)for defect identification,as well as the fusion of multi-layer time-series signals to analyze inter-layer defect evolution features.Such fusion strategies can significantly improve diagnostic reliability and enable cross-validation.Furthermore,physics-informed neural network-based approaches are emerging as a promising route to enhance model accuracy and interpretability under limited labeled data by embedding physical constraints,thereby reducing data dependence while substantially improving generalization capability.Finally,existing studies demonstrate that AI-enabled adaptive parameter adjustment can effectively improve build quality,paving the way towards"dark factory"intelligent manufacturing for LPBF. Conclusions and Prospects Significant progress has been achieved in LPBF in situ monitoring,diagnosis,and control.Currently,an LPBF in situ monitoring framework spanning optical,thermal,acoustic,and electromagnetic signals has been established,enabling the acquisition of process signatures across different spatiotemporal scales.While the capabilities of existing monitoring techniques continue to advance,emerging sensing approaches are also being actively explored.The monitoring paradigm is gradually evolving from pure process monitoring towards integrated process and outcome monitoring.AI has been extensively applied to defect diagnosis,markedly improving diagnostic efficiency and accuracy.Multi-signal data fusion effectively addresses the limited observability of single-signal monitoring and enhances both the accuracy and generalization of diagnostic methods.Physics-constrained diagnostic approaches strengthen model generalizability and interpretability,representing a promising future direction.In defect control,monitoring-informed adaptive parameter regulation and closed-loop control can effectively improve the reliability and consistency of build quality,laying the groundwork for intelligent manufacturing.However,owing to the spatiotemporal multi-scale nature of LPBF,existing monitoring approaches still struggle to simultaneously satisfy the requirements of high spatiotemporal resolution,large monitoring coverage,and long-duration observation.Current techniques are primarily designed to capture process signatures rather than directly measure defects.Moreover,diagnostic capabilities remain limited,and effective defect control is still insufficient,highlighting the critical need to further develop AI-driven LPBF in situ monitoring and control methods.Future research should prioritize:1)enhancing the capability to sense multi-source physical signals and advancing emerging sensing technologies;2)fully leveraging multi-physics signal data and simulation data through multi-signal fusion to enable real-time defect prediction and diagnosis;3)advancing digital twin and related technologies to achieve accurate quality assessment and real-time defect remediation,thereby establishing a new paradigm for intelligent quality control of complex,integrated LPBF structures.

Multi-material interpenetrating phase composites (IPCs) possess immense potential for designing next-generation, high-performance, lightweight load-bearing structures due to their topological co-continuity and synergistic mechanical attributes. However, most IPC designs focus on unit cell configurations rather than structural-level optimization for complex engineering conditions, limiting practical use and reinforcing the weight-performance trade-off. To bridge this gap, an initial analysis of interaction behaviors among different structural configurations under loading is conducted, revealing a synergistic reinforcement mechanism that provides a solid physical basis for structural optimization. Consequently, sheet-based triply periodic minimal surfaces are adopted as the core configurations. Based on the uniform multiphase material interpolation model, this study proposes a concurrent multiscale topology optimization framework specifically tailored for these IPCs. This framework introduces an innovative hybrid configuration strategy, seamlessly integrating lightweight single-phase lattices and high-strength IPCs within a unified design space. Furthermore, it enables the concurrent generation of three distinct spatial fields: the microscopic lattice configuration, the macroscopic relative density, and the interpenetrating phase distribution. Numerical and full-scale additive manufacturing validations comprehensively confirm the efficacy of the methodology. Notably, compared to conventional single-phase gradient designs, the optimized IPCs exhibit remarkable stiffness enhancements exceeding 50% for both the MBB and L-shaped beam cases. Ultimately, this study overcomes current technical bottlenecks in IPC optimization, providing a highly reliable paradigm for the multiscale optimization of advanced multi-material microstructures.

The mechanical performance of laser powder bed fusion (LPBF) fabricated high-entropy alloys (HEAs) remains constrained at both ambient and cryogenic temperatures. Ceramic particle reinforcement is considered a promising strengthening strategy, yet its effects on solidification behavior, microstructural evolution, and temperature-dependent mechanical response are not fully understood. Here, we report the first successful fabrication of a TiCN particle reinforced FeCoCrNiMn HEA via LPBF. The introduction of TiCN markedly refined the grain structure, increased dislocation density, and significantly weakened the characteristic <100> solidification texture inherent to LPBF-processed HEAs. Concurrently, in situ TiN precipitation effectively suppressed the formation of detrimental Mn-rich oxides, contributing to improved microstructural stability. These synergistic microstructural modifications resulted in a room-temperature yield strength of 748 MPa and an ultimate tensile strength of 893 MPa. Grain-boundary strengthening, dislocation strengthening, and Orowan strengthening collectively dominate the yield-strength increment. At 77 K, the alloy achieved a yield strength of 995 MPa, representing a 32% enhancement relative to ambient conditions. The underlying mechanisms governing strength enhancement and the concurrent ductility reduction at cryogenic temperature were elucidated. Overall, this work establishes a novel understanding of particle-enabled strengthening in LPBF HEAs and provides a microstructural design strategy for achieving high strength across both ambient and cryogenic service environments.

Spacecraft pose estimation using a monocular camera is a promising technique for current and future space missions. Monocular pose estimation algorithms based on deep learning have demonstrated superior robustness in space imagery compared to traditional methods. However, existing deep learning based methods still face challenges in handling truncated spacecraft images, which frequently occur during close-range rendezvous and docking. In such scenarios, evaluating the uncertainty of the estimated pose is equally crucial to prevent potential collision risks. To address truncated spacecraft images, we propose a dense correspondence hybrid architecture that predicts masks and 3D coordinates via a U-shape neural network to establish 2D-3D correspondences, followed by pose estimation using the PnP algorithm. For efficient uncertainty estimation, we introduce a deep evidential regression approach applied on the 3D coordinate predictions, enabling joint modeling of aleatoric and epistemic uncertainties within a single network and a single forward pass. Specifically, the coordinate regression outputs are modeled as a Normal-Inverse-Gamma (NIG) distribution, where the learning process is formulated as evidence acquisition. By applying regularization on the evidence, the network is encouraged to learn epistemic uncertainty. Furthermore, an uncertainty-aware PnP method is employed to enhance pose accuracy and approximately propagate uncertainty into the pose parameters. To evaluate truncation robustness, we construct a spacecraft truncation-robust dataset(STRD) which contains six spacecraft models with diverse geometries. Finally, extensive experiments on STRD and out-of-distribution (OOD) data demonstrate the generalization and effectiveness of our method in both pose and uncertainty estimation.

The application of lightweight lattice structures to complex curved surfaces is hindered by a geometric mismatch with traditional orthogonal arrays, leading to boundary incompatibility and performance degradation. Inspired by the microstructure of human bone, this study proposes a parallel design method integrating bidirectional isoparametric mapping and topology optimization to construct 3D conformal gradient heterogeneous lattice structures, which realizes the synchronous optimization of the geometric shape of the lattice structure and the conformal distribution characteristics of the macro curved surface. The core of the proposed method lies in the synergy between physical drivers and geometric mapping. Through compliance minimizing topology optimization combined with stress information intelligent selection, the density and configuration fields are obtained. Following data transfer via multi-scale mapping, orthogonal gradient heterogeneous lattices are generated in the parametric space and inversely mapped to ultimately form 3D conformal gradient heterogeneous lattice structures through bidirectional isoparametric transformation. Through the design, manufacturing, and experimental testing of semicircular beams and spacecraft re-entry capsule shells, the results demonstrate that compared with the traditional orthogonal uniform lattice structures, the structure optimized by this method has its stiffness and energy absorption performance improved by 275.9 % and 86.6 % respectively under the condition of maintaining basically stable strength. This work establishes a unified automated workflow for synchronizing geometry with performance-driven material distribution, providing significant progress for manufacturing high-performance lightweight lattice structures with complex geometers.

Deorbiter systems based on micro-spacecraft presents a promising approach for the active removal of space debris. Stable in-orbit deployment is a prerequisite for the successful completion of the entire mission. To investigate the reliability and launch dynamics of in-orbit launch systems for micro-spacecraft, this study developed a ground-based test system using the drop-tower method and employed a high-precision measurement system to capture the dynamic response of the micro-spacecraft during launch. A numerical model of the micro-spacecraft’s in-orbit launch dynamics was developed employing multi-body dynamics. This model accounts for thrust eccentricity, motion coupling between the micro-spacecraft and the satellite platform, contact collisions during launch, and other perturbation factors. The accuracy of the numerical model was validated by comparing the simulation results with those from ground tests. Based on this, Monte Carlo simulations were conducted to analyze and discuss the factors influencing the separation angular velocity of the micro-spacecraft. The results indicate that, for specific structural and parameter configurations, controlling the eccentricity angle of the solid-propellant thruster and the clearance between the micro-spacecraft and the separation mechanism within certain limits can effectively reduce the micro-spacecraft’s separation angular velocity.

In multiscale lattice optimization under thermomechanical loading, surrogate models must deliver not only accurate effective properties but also reliable gradients. This study identifies nonphysical derivative sign reversals in high-order polynomial surrogate surfaces as a source of instability in thermoelastic optimization. These gradient errors are then amplified through the thermal-load and heat conduction terms in the adjoint sensitivity analysis, biasing the search direction and causing iterative oscillations. To address this issue, we propose a monotonicity-constrained Bézier surface surrogate. By exploiting the variation-diminishing property of the Bernstein basis and the structured form of its derivatives, we impose monotonicity constraints on the control points along the level-set parameter direction. This enforces consistent monotonic behavior over the feasible domain while preserving fitting accuracy (median R2 above 0.993). Numerical examples show that the proposed surrogate suppresses oscillations caused by nonphysical gradients and improves robustness in complex multi-lattice spaces. In four-configuration optimization, the average per-iteration cost is reduced by 17.2% compared with a conventional polynomial surrogate. A double-clamped beam example reveals the applicable window of multi-lattice strategies as the relative strength of mechanical and thermal effects varies. Under strong uniform temperature change (ΔT=−20∘C and +20∘C), the triple-configuration lattice reduces compliance by 59.5% and 57.5% relative to SIMP topology optimization, and under a temperature difference across the top and bottom surfaces, the reduction reaches 57.12% at T1=10∘C and 9.3% at T1=0∘C. A rudder-core optimization with high-fidelity reconstruction and coupled simulation further shows that the BCC+Cubic design reduces compliance and maximum displacement by up to 21.1% and 12.9%, respectively, supporting the applicability of the framework to engineering thermoelastic multiscale design.

In the aerospace sector, additive manufacturing drives rapid iterations toward lighter-weight, functionally integrated absorber structures. However, geometric deviations from material flow in fused deposition modeling (FDM) processes limit the prediction accuracy of electromagnetic performance and design iteration efficiency. With respect to typical square absorbing structures, this study proposes a coupled computational fluid dynamics (CFD)–CST simulation method that, by incorporating equipment motion trajectories and control strategies into a finite‑volume numerical deposition model, quantifies the influence of process parameters on deposition morphology. This enables structural shape prediction during 3D printing, with minimum and average relative errors of 1.57% and 6.23%, respectively, for the top‑view contour areas relative to the experimental values. Reconstructing these simulation-derived results into an electromagnetic model reveals that the ideal geometric model, neglecting process deformation, fails to replicate absorptive capabilities in the millimeter-wave band, whereas CFD-reconstructed geometry yields greater consistency with actual printed samples. This alignment enhances the geometric fidelity of the input model, thereby bolstering the reliability of subsequent electromagnetic simulations. This approach establishes a portable framework for verifying absorptive performance and optimizing the manufacturing of FDM-produced functional devices.

The time-optimal rendezvous problem is crucial for efficiently executing on-orbit servicing (OOS) missions in the future. To fulfill the detection requirement during rendezvous process, it is an essential issue that the maneuvering spacecraft flies over the designated waypoint. This paper presents an innovative methodology for planning the time-optimal spacecraft rendezvous trajectory, involving the constraints related to a flyover waypoint and being forced by a constant thrust. The method is specifically designed to handle the optimal problems with the shortest and unspecified flyover time and terminal rendezvous time. First, this article outlines the scenarios for a time-optimal rendezvous that incorporates the constraints of a flyover waypoint. Second, a time-normalized relative dynamic model for maneuvering spacecraft is derived using the Clohessy-Wiltshire (CW) equation. Third, the time-optimal control output under the constant thrust is provided leveraging Pontryagin's minimum principle (PMP). Meanwhile, an indirect solution equation is established with the constraints of relative position and velocity for the flyover waypoint during the rendezvous process. Finally, a computational methodology for solving this time-optimal problem is proposed, integrating the initial guess for the unspecified time, multi-objective particle swarm optimization using multiple search strategies (MMOPSO) and Newton-Raphson method (NRM). Simulation results demonstrate that the method can effectively and practically solve the time-optimal rendezvous trajectory planning under a constant thrust, while satisfying the constraints of the flyover waypoint. Moreover, Monte Carlo simulations are performed, the results of which indicate that the proposed methodology exhibits strong robustness and fidelity.

The Portevin-Le Chatelier (PLC) effect, arising from dynamic strain aging (DSA), manifests as serrated flow during plastic deformation. While extensively studied in conventionally manufactured (CM) aluminum alloys, its behavior in additively manufactured (AM) counterparts remains poorly understood. In this work, the effects of temperature ( T ), strain rate (s ), and microstructure (grain morphologies and precipitates) on the PLC behavior of a representative laser powder bed fused (LPBF) Al-Mg-Sc-Zr alloy are investigated. The PLC behavior exhibits strong dependence on T and s , occurring only within an intermediate deformation window. A distinct strain-dependent transition of serration types is highlighted-from type B within the L & uuml;ders plateau to type A or mixed ( A + B ) at higher strains-which has rarely been observed in CM alloys. While further investigation is required, this transition is mainly attributed to evolving strain partitioning between coarse columnar and ultrafine equiaxed grains in the LPBF-induced bimodal structure. Direct aging reveals that dense Al3(Sc, Zr) precipitates suppress the PLC effect by reducing strain partitioning and consuming vacancies, whereas grain coarsening after over-aging eliminates the L & uuml;ders plateau. This work highlights the critical role of microstructural heterogeneity in governing DSA-mediated flow instabilities in AM Al alloys. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Lack-of-fusion (LoF) defects are among the most critical in laser powder bed fusion (LPBF), as they can substantially degrade the structural integrity and service performance of fabricated components. Although data-driven defect detection based on in-situ monitoring signals has advanced rapidly in recent years, most studies have focused on simple geometries. In contrast, complex components remain insufficiently investigated due to geometry- and scan-strategy-induced variations in melt-pool signals. To address this challenge, this study proposes a detection and evaluation framework for LoF defects in complex LPBF components. LoF defects were intentionally induced in a grid component by locally reducing the laser power, guided by the LoF formation mechanism. Scan vector length was introduced as a key geometric-process variable to characterize geometry- and scan-strategy-induced variations in melt-pool signals, revealing a significant negative correlation with mean melt-pool intensity (Spearman’s correlation coefficient: −0.6536). A Transformer-based unsupervised time-series anomaly detection model was then developed and integrated with intra-layer and inter-layer filtering strategies. These strategies reduced false positives and recovered missed defect regions, increasing the F1 score from 0.549 to 0.900 and the intersection over union (IoU) from 0.378 to 0.818. The low coefficients of variation across different defect-inducing laser powers, 0.95% for F1 score and 1.8% for IoU, further indicate robust performance under varying LoF severity levels. These results demonstrate the effectiveness of incorporating geometry-related scan information and hierarchical filtering for reliable in-situ LoF detection in complex LPBF components.

Electromagnetic metamaterial skins have become the core carrier for optimizing the electromagnetic performance of aircraft owing to their superior electromagnetic modulation capabilities. However, traditional manufacturing processes struggle to adapt to the complex curved surface structures of aircraft skins. While emerging hybrid additive manufacturing technologies offer a breakthrough direction for these process bottlenecks, challenges such as insufficient printing precision, poor adaptability for multi-material synergistic jetting, and high difficulty in conformal manufacturing on complex surfaces remain. Addressing the manufacturing requirements of aircraft electromagnetic metamaterial skins, this paper proposes a conformal conductive/dielectric hybrid structure inkjet integrated manufacturing technology. A conductive/dielectric material hybrid curved surface inkjet system was developed, constructing a five-axis motion control architecture with upper-lower computer coordination and designing the orifice structure of the array printhead. Data loading, printing path planning, and data generation were performed for the curved surface of the aircraft flap metamaterial, realizing the integrated printing of conductive/dielectric hybrid structures on complex conformal metamaterial skins. Experimental results demonstrate that this manufacturing technology effectively enhances the precision of multi-material printing on curved surfaces, achieving high-precision deposition of conductive/dielectric structures on non-developable surfaces, thereby providing equipment support and technical solutions for the engineering manufacturing of aircraft electromagnetic metamaterial skins.