Staple yarns are formed by twisting assemblies of discontinuous fibers and exhibit high axial strength and radial compliance, making them widely used in textile applications. However, the microscale nature and random distribution of staple fibers make yarn formation difficult to characterize at the fiber level. In this study, a fiber-scale modeling framework is proposed to simulate staple yarn formation and woven fabric construction. A generative adversarial network model is employed to generate crimped fiber configurations, followed by twisting and weaving simulations to reconstruct the fiber-scale structures of staple yarns and woven fabrics. The proposed framework reproduces fiber migration, twist propagation, and fiber entanglement during yarn formation, and is further extended to construct homogeneously blended and segmented blended yarns and their corresponding woven fabrics. Good agreement is achieved between the simulated and experimental structures in terms of fiber distribution, microstructural morphology, and fabric texture. This work provides an effective framework for the fiber-scale digital modeling of staple yarns and woven fabrics, facilitating structural design and performance prediction.

Sorption-based atmospheric water harvesting (AWH) holds promise for on-demand water supply, yet combining high yield with fast kinetics remains challenging. Here, we present a hollow-fiber textile-supported composite sorbent and an integrated solar-powered water harvester. The composite sorbent is fabricated by embedding LiCl into a three-dimensionally oriented channel textile constructed from hollow Calotropis gigantea fibers. An airflow-assisted yarn assembly strategy is employed to form hollow-fiber yarns and further construct a three-dimensional textile with vertically aligned pore arrays. After LiCl integration, efficient water vapor sorption and rapid desorption are achieved. The sorbent attains water uptakes of 0.88, 1.39, and 2.34 g g−1 at relative humidities of 15

We present AutoSiMP, an autonomous pipeline that transforms a natural-language structural problem description into a validated, binary topology without manual configuration. The pipeline comprises five modules: (1) an LLM-based configurator that parses a plain-English prompt into a validated specification of geometry, supports, loads, passive regions, and mesh parameters; (2) a boundary-condition generator producing solver-ready DOF arrays, force vectors, and passive-element masks; (3) a three-field SIMP solver with Heaviside projection and pluggable continuation control; (4) an eight-check structural evaluator (connectivity, compliance, grayness, volume fraction, convergence, plus three informational quality metrics); and (5) a closed-loop retry mechanism. We evaluate on three axes. Configuration accuracy: across 10 diverse problems the configurator produces valid specifications on all cases with a median compliance penalty of +0.3% versus expert ground truth. Controller comparison: on 17 benchmarks with six controllers sharing an identical sharpening tail, the LLM controller achieves the lowest median compliance but 76.5% pass rate, while the deterministic schedule achieves 100% pass rate at only +1.5% higher compliance. End-to-end reliability: with the schedule controller, all LLM-configured problems pass every quality check on the first attempt - no retries needed. Among the systems surveyed in this work (Table 1), AutoSiMP is the first to close the full loop from natural-language problem description to validated structural topology. The complete codebase, all specifications, and an interactive web demo will be released upon journal acceptance.

To realize the effective electromagnetic (EM) protection, the demand of large-scale production and desirable flexibility for metamaterial absorber (MA) has been a hotspot. In this work, a flexible MA with the characteristics of textile textured meta-surface, dielectric lossy layer and temperature-strengthened reflective layer is prepared by means of chemical plating and laser-induced graphene (LIG) techniques. Co-Ni metallized CNTs and PEDOT: PSS are combined to form magneto-dielectric synergy for attenuation in medium layer, and the superiorities of fabric as substrate to enhance EM absorption in top layer is validated. The structural parameters of MA are optimized via CST stimulation, and the as-prepared MA exhibits a minimum reflection loss (RL) of -26.4 dB and an effective absorption band (EAB) of 8.0 GHz, with the radar cross section (RCS) bandwidth of 7.4 GHz in the X band (2-18 GHz), delivering a promising potential for radar stealth.

Woven fabric textures exhibit periodic warp–weft interlacing, strong directionality, and repetitive primitives. In imaging and industrial applications, defects such as dead pixels, contamination, and warp/weft floats may cause random or through-going structured loss, making texture completion an ill-posed inverse problem. Under a matrix completion framework, this study proposes an interpretable joint-prior model that combines low-rank regularization with two-sided orthonormal transform coefficient sparsity (OTCS). OTCS characterizes warp–weft periodic content, while the low-rank prior enforces global structural consistency. A nuclear-norm-based model, Nuc + OTCS, is first developed. To reduce the over-shrinkage of dominant singular values, truncated nuclear norm regularization (TNNR) is further introduced with a two-level optimization strategy of outer principal-subspace extraction and inner trace-compensated reconstruction, yielding TNNR + OTCS. Experiments on 16 woven fabric texture classes show that OTCS outperforms total variation, especially under stripe-shaped through-going loss. With dominant low-rank energy preserved, TNNR + OTCS achieves the best or tied-best results across loss ratios and mask types, and shows stronger robustness in high-loss and structured-loss settings.

Fabric-based electromagnetic wave (EMW) absorbers have attracted widespread attention due to their favorable flexibility. However, most fabric-based EMW absorbers are manufactured via surface coating on conventional fabric substrates, which not only confines the EMW-absorbing behaviors to the fabric exterior but also poses potential environmental hazards with the involvement of organic solvents during production. In this work, a green strategy is proposed for producing intrinsic EMW-absorbing fibers through an aqueous process, with the senary high-entropy layered double hydroxide integrating graphene oxide (GO@SHE-LDH) as a functional constituent and the sodium alginate (SA)-waterborne polyurethane (WPU) dual-network system as a fiber matrix. The GO@SHE-LDH/SA-WPU (GLSW) fibers are woven into plain-weave fabrics, exhibiting a minimum reflection loss (RLmin) of -69.6 dB and a maximum effective bandwidth (EABmax) of 7.1 GHz below 2.2 mm thickness, with favorable terahertz shielding/absorption performance (49.8 dB/45.6 dB) covering the 0.5-4.5 THz band, which is attributed to the synergistic EMW-absorbing effect of multiscale mechanisms. This work provides a promising eco-friendly method to produce functional fibers toward EMW absorption.

In topology optimization (TO) and related engineering applications, physics-constrained simulations are often used to optimize candidate designs given some set of design conditions. Such models can be computationally expensive and do not guarantee convergence to a desired result, given the frequent nonconvexity of design optimization problems. Creating data-driven approaches to warm-start these models-or even replace them entirely-has thus been an active area for engineering design researchers. In this article, we propose an augmented vector-quantized GAN (VQGAN) that allows for effective compression of TO designs within a discrete latent space, known as a codebook, while preserving high reconstruction quality. Further, we present a new dataset of two-dimensional heat sink designs optimized via multiphysics topology optimization (MTO) to train and evaluate this model. To concretely assess the benefits of the VQGAN quantization process, we conduct a latent analysis of its codebook as compared to the continuous latent space of a deep autoencoder (AE). We find that VQGAN can more effectively learn topological connections despite a high rate of data compression. Finally, we leverage the VQGAN codebook to train a small GPT-2 model that is capable of rapid generation of thermally performant heat sink designs. We show that the transformer-based approach is more effective than using a Wasserstein GAN with gradient penalty (WGAN-GP) due to its superior preservation of topological connections in MTO and similar applications.

The matrix-free gather-batched-GEMM-scatter pattern eliminates global stiffness assembly for three-dimensional SIMP topology optimization, but the conventional three-stage implementation forces avoidable DRAM traffic between stages. We present a single fused CUDA kernel, implemented through CuPy's runtime compilation interface, that performs gather, per-element stiffness multiplication, and scatter accumulation in one pass. On a single RTX 4090 (24 GB), the fused path reaches a problem-size-dependent 4.6-7.3x end-to-end SIMP wall-time speedup across 216k-4.9M cantilever elements and 4.4x on the 499,125-element torsion benchmark. Against the same-precision FP32 three-stage baseline, the fused path still yields 2.3-4.6x on cantilever and 2.8x on torsion. Isolated CUDA-event cantilever-operator measurements reach 8.9-13.8x per matvec call, while separate instrumented board-power traces at 216k and 1M show 3.2-4.9x lower energy than matched FP64 runs. A separate bridge stress test shows the same FP32-versus-FP64 three-stage trend under one distributed-load case; direct fused-kernel bridge benchmarks are not reported. We also evaluate a BF16 WMMA variant: a separate PyTorch BF16 GEMM proxy on matching tensor shapes yields 14.3x, but direct condition-number estimates of 6.1e5-2.3e6 across 64k-512k uniform-density test states imply BF16 conditioning products of 2.4e3-9.1e3, far above the 256 threshold, observed alongside BF16 iterative-refinement stagnation at the two tested inner tolerances.

High entropy materials (HEMs) have attracted extensive attention as a promising expectant for electromagnetic waves (EMWs) absorber due to the unique designability. However, the preparation of common HEMs generally requires complex processes, which hinders their further application. Herein, a facile approach is proposed for entropy engineering in layered double hydroxides (LDH) based composite towards EMWs absorption. Ternary (FeCoNi), quaternary (FeCoNiMn) and quinary (FeCoNiMnCr) LDHs integrating GO/MXene (GM@LE-LDHs, GM@ME-LDHs and GM@HE-LDHs) composites are successfully synthesized via a one-step hydrothermal method with modulated EMWs absorbing performance through facile entropy engineering. The GM@HE-LDH delivers a minimum reflection loss (RLmin) of-52.34 dB and a maximum effective bandwidth (EABmax) of 5.01 GHz with less than 2 mm thickness, owing to the EMWs absorbing mechanism induced by the collaboration of entropy engineering on atomic scale and heterogeneous interface effects on nanoscale. This work provides a promising entropy engineering approach to prepare efficient EMWs absorber.

Shape-adaptive weaving enables near-net-shape forming of curved thin-shell composite preforms by directly tailoring warp-weft interlacing on non-developable surfaces, thereby reducing seams, overlaps, wrinkles and fiber-orientation deviations caused by conventional draping. However, its yarn layout design remains difficult because yarn paths, weft insertion ranges, short-weft terminations and forming-quality indicators are strongly coupled. Existing methods mainly rely on empirical rules or local corrections, making it difficult to accurately coordinate yarn coverage, orientation consistency, in-plane shear and yarn waviness, and therefore cannot meet the requirements of high-quality forming and rapid process design for complex curved preforms. This study proposes a surface-driven yarn layout generation method for curved shape-adaptive woven preforms. The target surface geometry is used as the input, and geodesic-based path generation is combined with textile graph modelling to automatically generate the yarn layout. A multi-objective optimization model is established to optimize yarn coverage, orientation deviation, in-plane shear and yarn waviness. A two-dimensional weaving chart is then generated for process design. The fabricated specimens achieve yarn coverage of 82.7%-96.2%, average absolute shear angles of 3.1°-7.7°, and computation time within 163 s. The prediction errors of average shear angle and yarn coverage are below 10.5% and 5.61%, respectively. These results demonstrate the capability of the proposed method for rapid yarn layout design, pre-weaving quality assessment and multi-objective optimization of complex curved woven preforms.

Metamaterial design (via architected cellular units) has evolved from patterning in a regular, periodic fashion to a random, aperiodic style to broaden the spectrum of the property space. While periodic designs excel at maximizing properties in specific directions (anisotropy), aperiodic or randomized designs are increasingly valued for their ability to homogenize effective properties (isotropy) and improve robustness against failure. However, this transition introduces geometric uncertainty-the stochastic variability inherent to the design and generation of aperiodic architected cellular materials (AACMs). The uncertainty in geometry cripples downstream workflows such as validation, optimization, and manufacturing. To manage this, state-of-the-art (SotA) strategies were developed to capture and incorporate randomness and enable statistical analysis in both traditional (e.g., probabilistic representations and uncertainty quantification) and data-driven (e.g., deep generative models and graph neural networks) manner. While traditional strategies often suffer from high computational cost and oversimplified assumptions about structure and uncertainty, data-driven approaches face challenges in interpretability and latent space entanglement. In contrast, we present an uncertainty-aware generative design framework that leverages a conditional hierarchical Wasserstein generative adversarial network (CH-WGAN) to synthesize diverse, high-fidelity 3D AACM by hypothesizing dependencies between periodic (nominal) cellular units and aperiodic (variant) ones. In CH-WGAN, a proposed parameter generator maps nominal control parameters and a dedicated aperiodicity code, together with latent noise, into a convex mixture of analytically defined base signed distance functions (SDFs), whose scale and periodicity distributions explicitly model geometric uncertainty. The critic, augmented with an InfoGAN-style Q-head, enforces a Wasserstein gradient penalty (GP) loss for realism, a latent-regression loss for invertibility, and entropy/Kullback-Leibler (KL) divergence to promote multimodal coverage of the uncertain design space. We introduce a periodicalization module that adaptively warps the underlying 3D grid according to learned periodicity distributions, enabling accurate aperiodicity modeling. Training on paired nominal-variant SDF data disentangles intrinsic geometry from uncertainty in periodicity, allowing CH-WGAN to generate multiple aperiodic, structurally diverse unit-cell variants for a periodic parent. Marching-cubes visualizations and statistical comparisons confirm that our learned uncertainty distribution closely matches real aperiodicity statistics. This framework provides a robust, interpretable, and generalizable tool for exploring AACM designs under geometric uncertainty.

Growing environmental concerns are driving the development of bio-based and recyclable thermosetting polymers. In this work, we synthesized a novel liquid cycloaliphatic epoxide containing silyl ether bonds from alpha-terpineol, a renewable monoterpene alcohol. After optimizing the accelerator content, the epoxy resin cured with methylhexahydrophthalic anhydride exhibited a high glass transition temperature (172 degrees C) and an acceptable tensile strength (57.2 MPa), the presence of silyl ether bonds imparts the resin with inherent degradability in fluoride and acidic organic solutions, offering a pathway for recycling. Finally, the optimized epoxy system was used to prepare a thermally conductive adhesive with boron nitride (BN) as the filler. The resulting epoxy composite 35 wt% BN, exhibited an enhanced thermal conductivity of 1.298 W/m & sdot;K. Crucially, the high-value BN fillers could be recovered non-destructively through selective chemical recycling of the epoxy matrix.

The treatment of ammonia nitrogen wastewater(ANW)has garnered significant attention due to the ecology,and even biology is under increasing threat from over discharge ANW.Conventional ANW treatment methods often encounter challenges such as complex processes,high costs and secondary pollution.Consider-able progress has been made in employing solar-induced evaporators for wastewater treatment.However,there remain notable barriers to transitioning from fundamen-tal research to practical applications,including insufficient evaporation rates and inadequate resistance to biofouling.Herein,we propose a novel evaporator,which comprises a bio-enzyme-treated wood aerogel that serves as water pumping and storage layer,a cost-effective multi-walled carbon nanotubes coated hydrophobic/hydrophilic fibrous nonwoven mat functioning as photothermal evaporation layer,and aggregation-induced emission(AIE)molecules incorporated as anti-biofouling agent.The resultant bioinspired evaporator demon-strates a high evaporation rate of 12.83 kg m-2 h-1 when treating simulated ANW containing 30 wt％NH4Cl under 1.0 sun of illumination.AIE-doped evaporator exhibits remarkable photodynamic antibacterial activity against mildew and bacteria,ensuring outstanding resistance to biofouling over extended periods of wastewater treatment.When enhanced by natural wind under 1.0 sun irradiation,the evaporator achieves an impressive evaporation rate exceeding 20 kg m-2 h-1.This advancement represents a promising and viable approach for the effective removal of ammonia nitrogen wastewater.

Fabric defect detection is an indispensable step in textile fabric production, and many deep-learning-based methods have been proposed. However, existing supervised methods are limited by the lack of annotated datasets, and unsupervised anomaly detection methods still fail to meet the requirements of practical applications. To address these issues, a novel unsupervised dual-scale method based on feature distance, called DSFD, is proposed. First, leveraging the periodic characteristics of fabric textures, a method for obtaining feature templates of image patches and a method for calculating anomaly scores of image patches are proposed based on a vector quantized variational autoencoder (VQ-VAE). Second, to address the issue that codebook vectors cannot be effectively activated using Euclidean distance-based quantization mechanism during model training and testing, a cosine similarity-based quantization mechanism is proposed. Ablation experiments demonstrate its effectiveness in improving defect detection performance. Finally, to enhance the robustness of the model when applied to different types of fabric images, a dual-scale method is proposed. The proposed method was compared with five state-of-the-art anomaly detection methods on three open-source datasets, achieving superior defect detection performance. It demonstrated a performance improvement of 2.1% in image-level defect detection and 1.3% in pixel-level segmentation in terms of area under the curve scores.

Hot rolling rolls are critical components in achieving metal plastic deformation and quality control during the hot rolling process. The surface quality of hot rolling rolls significantly affects the dimensional accuracy of rolled products. Therefore, predicting the remaining useful life (RUL) of rolls is crucial for planning maintenance or replacement strategies in advance. A data-driven approach SA-MSTCN is proposed, constructing a neural network that integrates self-attention mechanisms and multi-scale temporal convolution for RUL prediction of hot rolling rolls. The self-attention mechanism captures global features in the roll degradation process by computing correlations between input data at different time steps. Multi-scale temporal convolution extracts local degradation features using parallel convolution kernels of varying scales. Experiments on a roll dataset demonstrate that the proposed network effectively captures complex latent features from hot rolling roll degradation data. The network achieves accurate predictions of the RUL for hot rolling rolls.

Conventional filters for air filtration typically feature compact nonwoven structures, which not only lead to high pressure drop, significant energy consumption, and a decay in filtration efficacy, but are also uncleanable, resulting in substantial pollution upon disposal. In this study, filters with high-voltage electrostatic loading capability were developed with a dopamine binding layer to facilitate the establishment of an Ag conductive layer on the surface of ultraloose woven structure fabrics (pore size: 73.7 μm). The high-voltage-loaded woven structure filtration (VLWF) system was constructed with a negative-ion zone, a high-voltage filtration zone, and a grounded filter. The morphological, chemical, and electrical properties of the filters and the filtration performance of the VLWF system were evaluated. The single-pass filtration efficiencies for PM2.5 and E. coli were 67.4% and 97.0%, respectively. Notably, the pressure drop was reduced to 6.2 Pa, and the quality factor reached 0.1810 Pa−1 with no detectable ozone release. After three cycles of ultrasonic cleaning, approximately 58.4% of filtration efficiency was maintained without any increase in air resistance. The removal of PM2.5 and microorganisms by this system was not solely reliant on blocking and electrostatic attraction but may also involve induced repulsion and biostructure inactivation. By integrating the ultraloose woven structure with high-voltage assistance, this VLWF system effectively balanced the requirements for high filtration efficacy and low air resistance. More importantly, this VLWF system provided a cleanable filter model that reduced the pollution associated with conventional disposable filters and lowered costs for customers.

Industrial robots, widely employed to boost production efficiency, encounter escalating risks of joint faults as their service time lengthens. However, end-effector motion anomalies may stem from faults in the end-effector itself or from motion propagation in other joints. Moreover, the scarcity of fault samples for detection poses significant challenges. Install extra accelerometers for more precise fault diagnosis might increase the system’s complexity and costs. To tackle these challenges, this study leverages the ease of data acquisition to analyze current data from multi-joint industrial robots. A hybrid learning method is proposed for cross-device fault detection to identify the defective joint. This method integrates features from deep networks and spectral analysis to harness knowledge from both other robots and the target robot. An unsupervised model is used to assess the status of the joints based on the fused features. The proposed method’s effectiveness is validated through ablation studies and method comparisons. Results demonstrate that it accurately detects the abnormal joints without misjudgment.

To investigate the rapid capillary wicking behavior of twisted filament yarns over short vertical distances, this study employs a computational fluid dynamics simulation model to analyze and compare the liquid flow characteristics of yarns with varying structural configurations. To validate the model, a series of wicking experiments were conducted on polyester yarns using high-speed photography. The results demonstrate good agreement between the experimental data and the simulation outcomes. The study examines the effects of six twist levels, 0, 200, 400, 600, 800, and 1000 twists per meter (tpm), and four fiber packing arrangements, namely circumferential/open, hexagonal, and two variations of random packing, on the distribution of the liquid velocity field. The results reveal that with increasing twist, the wicking rate initially increases slightly or remains stable, but decreases significantly beyond 400 tpm. Among the structural configurations, circumferential packing more closely resembles the pore characteristics of random packing than hexagonal packing. While the predominant wicking direction follows axial migration, a minor lateral component of liquid flow is also observed.

High-performance and lightweight materials design is a pressing need in aerospace applications (e.g., aircraft ailerons, flaps, and rudders). However, the unique functionality requirements in strength, weight, and resistance to environmental factors, such as temperature fluctuations and corrosion, challenge traditional structure design methods such as topology optimization. While sandwich panel composites with lattice cores are widely used in aerospace components and modern additive manufacturing techniques open new possibilities for sandwich core structure design with requirement functionalities, the delicate design brings computational challenges for both optimization and manufacturing. This paper presents an inverse design framework for sandwich structure optimization with implicitly represented architected cellular materials to address these issues. Specifically, cellular materials are implicitly represented (described by implicit functions) as building blocks in the core structure design. A multi-objective topology optimization problem is formulated to maximize the core structure’s mechanical and thermal performances. Lastly, the nature of function representation for ease-of-additive manufacturing computations is illustrated with a direct slicing algorithm without generating memory-expensive standard tessellation language (STL) files. The proposed design framework is validated in two practical aerospace design case studies, and experimental results demonstrate the effectiveness of the proposed optimization algorithm and STL-free scheme for additive manufacturing.

To improve the quality of hydroentangled nonwoven cotton fiber web and optimize the parameters of hydroentanglement, the hydroentanglement process was simulated using a finite-element method. A cotton fiber web was established based on the fractal principle. In this work, a finite-element model of the hydroentanglement process was developed using LS-DYNA software. The effects of water jet pressure and water jet distance on the fiber web thickness were investigated using the model. A relevant experiment was designed to verify the effectiveness of the model. Computer simulations and experiments show that a higher water jet pressure and a shorter water jet distance will decrease the fiber web thickness. The results show that there is great potential for this research in the field of computer-assisted methods in hydroentanglement technology.