Digital twins in machining are increasingly investigated for monitoring and prediction; however, their deployment at the machine-tool level for executable closed-loop regulation remains limited. This paper proposes a machine-tool-level integrated digital twin framework for CNC milling that combines virtual commissioning, online surface roughness prediction, and constrained adaptive regulation within a unified workflow. An offline mechanism twin is first developed for scenario-based virtual commissioning under representative command/load conditions, generating a deployable baseline consisting of transferable servo parameters and calibrated closed-loop dynamic response signatures. An online process twin is then constructed for roughness-oriented prediction and regulation through multi-source fusion of static process parameters and dynamic signals using a lightweight CNN-Transformer architecture. To support sensor-limited operation, a cutting-force simulation model with structured residual compensation is introduced, providing surrogate force-related information and achieving prediction performance comparable to that obtained with measured-force inputs (mean error 5.23% vs. 4.56%). Based on the predicted roughness state, a digital-twin-enabled model predictive control strategy computes constrained spindle-speed and feed-rate updates, which are executed through LinuxCNC HAL-based writeback. Milling experiments under representative controlled conditions demonstrate the feasibility of the proposed framework for roughness-oriented closed-loop regulation, establishing an executable path from machine-tool-level digital twins to controller-oriented online machining regulation.

Three-dimensionally printed continuous carbon fiber reinforced polymer (3DP CCFRP) composites have emerged as promising materials for self-sensing applications in structural health monitoring (SHM). This study investigates the deformation-electrical resistance variation behavior of a single line of 3DP CCFRP under uniaxial tensile loading from the elastic stage to failure. Tensile experiments reveal distinct resistance variation patterns: an initial linear increase attributed to fiber elongation, followed by a sharp rise corresponding to evolving fiber breakage, and subsequent fluctuations caused by progressive fiber pull-out and damage evolution. Cyclic tensile tests confirm the repeatability and reversibility of the linear deformation-resistance variation response within a limited strain range. To interpret these resistance variation behaviors, a corresponding mathematical model is proposed, incorporating carbon fiber breakage and inter-fiber contact as key contributors to deformation-resistance variation behavior. These features are quantitatively represented by the fiber breaking rate and contact density, which are characterized through a breakage prediction model and validated using computed tomography (CT) analysis. The model's predictions exhibit strong agreement with experimental measurements, particularly in the linear regime where the extracted gauge factors show high consistency. The findings establish a direct correlation between microstructural features and damage mechanisms, and the deformation-electrical resistance response of 3DP CCFRP composites, offering a theoretical foundation for the optimization of their sensing performance in SHM applications.

In complex engineering environments, the duration of dynamic loads experienced by structures is highly variable. Traditional topology optimization approaches typically address only a single load duration, posing substantial safety risks. This study proposed a multi-duration robust topology optimization method for the design of fiber-reinforced composite structures (FRCS) subjected to dynamic loads with multiple potential durations. Operating under the assumption of linear elastic dynamics, the method employed time-averaged displacement fields to guide fiber orientation updates, effectively addressing the time-varying characteristics of principal stress directions during dynamic responses. To capture the morphological variability of structures under different load durations, a novel metric called the Morphological Dispersion Index (MDI) was introduced. Guided by the MDI, representative sub-durations were selected, and robust topology optimization was achieved by aggregating the objective functions and sensitivities of each sub-duration throughout the optimization process. Experimental validation of the numerical simulation framework was conducted using 3D printing and Digital Image Correlation (DIC). Benchmark cases demonstrated that variations in load duration significantly influenced the optimization outcomes. Compared to designs optimized for a single load duration, the proposed method reduced structural elastic strain energy by 29.98% to 75.80%, while maintaining stable and efficient structural configurations. The method exhibited strong adaptability across varying load durations, offering a new perspective for the robust design of FRCS under dynamic loading conditions.

To improve the interlaminar bonding of CF/PEEK composites and evaluate the temporal stability of ultraviolet-induced surface modification, CF/PEEK prepreg tapes were treated by short-wavelength ultraviolet irradiation. The effects of ultraviolet (UV) treatment conditions and post-treatment storage time on the surface characteristics of the prepreg tapes and the interlaminar properties of the resulting laminates were investigated. Contact angle and surface free energy analyses, X-ray photoelectron spectroscopy, white-light interferometric three-dimensional surface profiling, interlaminar shear strength testing and fracture-surface SEM analysis were used to examine the associations among surface wettability, chemical composition, morphological evolution, and interlaminar performance. The results show that UV treatment improved the surface wettability and polarity of the prepreg tapes, increased the O/C atomic ratio and the relative fractions of oxygen-containing functional groups, and induced surface roughening. These changes were associated with an increase in the interlaminar shear strength of the laminates. Within the range of treatment conditions examined in this study, nominal 185 nm irradiation produced the most pronounced surface activation and ILSS improvement. Under the 185 nm, 80 mW/cm2, 20 min condition, the ILSS increased from 89.68 ± 2.30 MPa for the untreated laminate to 104.74 ± 1.82 MPa. During storage, the contact angle increased, whereas the polar surface-energy component and O/C atomic ratio decreased, indicating hydrophobic recovery and attenuation of surface oxidation. In contrast, the UV-induced roughness changed only slightly. After 10 d of pre-consolidation storage, laminates prepared under this treatment condition still exhibited a mean ILSS that was 5.47 MPa higher than that of the untreated storage control. Threshold-based SEM analysis further showed that the fracture-surface resin-covered fraction was higher for the laminate prepared from UV-treated prepregs than for the untreated laminate and partially decreased when consolidation was delayed by storage. These results demonstrate that, under the tested conditions, UV treatment was associated with improved interlaminar shear performance of CF/PEEK laminates, but both the treatment condition and the interval between treatment and consolidation should be considered.

Hydrogen pores are critical microscale defects in laser-deposited aluminum alloys, causing local strain concentration and reducing ductility. To spatially predict this structure-property relationship, this study proposes a neural network model based on structural feature extraction and fusion. Al-Mg-Sc specimens were fabricated by coaxial laser wire directed energy deposition, and micro-computed tomography (mu-CT) and Digital Image Correlation (DIC) experiments were conducted sequentially. Pore structure point clouds and normalized strain maps obtained from the experiments were uniformly segmented into spatially aligned blocks and used as training data. The model integrates PointNet and Convolutional Neural Network (CNN) modules to extract structural features and learn spatial correlations. A composite loss was introduced to capture both the continuous strain distribution and discrete high-strain regions. With limited data, the model achieved a pixel-level Area Under the Curve (AUC) of 0.69 and a custom distance-weighted AUC (dw-AUC) of 0.74, which is weighted by spatial proximity. On a full-scale specimen, the model accurately predicted the high-strain regions, one of which coincided with the actual fracture site. Sensitivity analysis shows that using a segmentation block size of around 300 mu m and applying random point cloud dropout helps maintain spatial resolution and improves training performance. This work provides a structure-informed modeling approach for predicting damage-prone regions in defect-containing alloys.

Establishing a mapping between process parameters and structural characteristics in 3D-printed continuous fiber-reinforced cementitious composites (CFRCCs) is challenging due to complex rheological behaviors and the mutual interaction between the matrix and the embedded fibers. To address this, this paper proposes a hierarchical physics-informed deep learning framework (HPC-Net) to predict the cross-sectional matrix contour and internal fiber distribution of single printed laces from process parameters. The framework utilizes a decoupled two-module architecture. Module I separates dimensional size and morphological shape predictions, incorporating volume conservation and monotonicity constraints to improve training stability and physical consistency across sparse parameter spaces. Module II employs a centroid-normalization mechanism to learn the relative fiber-matrix spatial mapping, establishing translation equivariance and preventing overfitting to absolute spatial coordinates. Validated on an orthogonal experimental dataset, HPC-Net demonstrates superior geometric fidelity and generalization capabilities compared to purely data-driven baseline models. Furthermore, the predicted 2D geometries are applied to an area-matching toolpath spacing optimization strategy for void-reduced deposition and the generation of 3D geometrical digital twins. This work establishes a geometric foundation for the computer-aided manufacturing and finite element analysis of CFRCCs structures.

This study proposes a fatigue life prediction model combining WCM and FE-safe for 35 MPa Type III high-pressure hydrogen storage vessels to investigate how alternated versus separated winding stacking sequences influence fatigue performance. The preferred autofrettage pressure was obtained based on DOT-CFFC and CGH2R standards, and static finite element and dynamic cyclic analyses were conducted on four distinct ply designs under operational conditions. The maximum error of proposed model was 5.01%, showing good agreement with experimental data. Results reveal that alternated ply sequences achieve a fatigue life of 17,298 cycles, significantly outperforming the 3468 cycles of separated sequences. The stress analysis suggests that the intrinsic “stiffness gradient” structure of alternated winding provides superior load transfer and bearing capacity compared to the “stiffness mutation” in separated winding. Notably, the stiffness gradient structures reduced the maximum equivalent stress amplitude on the liner's outer surface by 24.8% and 19.6% relative to the inner surface, indicating the superior fatigue resistance of alternated stacking sequences in the investigated vessel.

Filament winding trajectories govern component manufacturability and dictate mechanical properties via fiber distribution modulation. This study aims to develop more flexible and versatile trajectories by establishing a co-layer hybrid winding theory for multiple payout eyes and proposing a physics-guided deep reinforcement learning (DRL)-based optimization method. A physics-based custom filament winding simulation environment was built to evaluate various algorithms. Additionally, a comparative dome training-difficulty score was introduced to assess morphology effects under the tested geometry and parameters. Finally, filament winding trajectory experiments under different objectives were conducted using a custom-built multi-payout-eye filament winding machine, thereby verifying the feasibility of the reinforcement learning-based trajectory design. The results indicate that, compared to Twin Delayed Deep Deterministic Policy Gradient (TD3) and Proximal Policy Optimization (PPO) algorithms, the Soft Actor-Critic (SAC) algorithm exhibits the highest stability while maintaining high precision in the winding environment. DRL-based winding trajectories can achieve flexible and stable winding across different modes by employing a variable slippage coefficient. Within the tested geometry and parameter range, the score decreased with increasing aspect ratio, permitting greater offsets and design flexibility. Simultaneously, the winding of two distinct trajectories on the same helical layer is realized through the coordinated motion of dual payout eyes. This hybrid trajectory winding effectively mitigates fiber accumulation at the polar opening of the dome. Compared with the tangential path, the mean winding thicknesses of the adjacent and interlacing paths within the inner evaluation band were reduced by 43.9% and 50.6%, respectively.

To address the increasing demands for integrated stealth and load-bearing functions in modern aircraft and communication systems, this study develops a novel multilayered absorber (MA) utilizing a continuous fiber 3D printing process. This structure features a three-tiered graded metamaterial structure (3T-GMMS) as the microwave absorbing core layer. Benefiting from the concentric square spiral distribution of carbon fiber (CF) and a dual-gradient design (both intra-layer and through-thickness), the MA exhibits excellent TE/TM polarization insensitivity and oblique incidence stability. It achieves an average absorptivity exceeding 0.9 within the 4-18 GHz frequency band, with an effective absorption bandwidth (EAB) surpassing 13 GHz. Regarding mechanical performance, in-plane compression tests demonstrate that continuous fiber-reinforced (FRP) specimens achieve an ultimate edgewise compressive strength of 128.78 MPa, representing a 27.06% increase over pure polylactic acid (PLA) specimens. The design and fabrication of these 3D-printed graded metamaterial structures offer a promising strategy for developing integrated structures that balance superior microwave absorption performance with robust load-bearing capacity.

Traditional continuous fiber 3D printing technology has primarily focused on the fabrication of planar structures, with relatively insufficient research on curved composite components. To address the key technical challenges in the continuous fiber-reinforced 3D printing of rotationally curved components, this study conducted systematic research on printing equipment design, path planning methodologies, and interlaminar performance optimization using carbon fiber-reinforced polyether-ether-ketone (CCF/PEEK) prepreg filament. A multi-degree-of-freedom curved-surface printing system based on a six-axis robotic arm was developed to overcome the geometric limitations of traditional three-axis equipment. A cylindrical surface path layering algorithm based on STL models was proposed, achieving full-process digital manufacturing of curved components. Through orthogonal experiments and response heatmap analysis, it was clarified that printing speed and printing temperature are the key process parameters affecting interlaminar shear strength, with printing speed being the most influential. The optimal parameter combination (printing speed 0.8 mm/s, printing temperature 380 degrees C, mold temperature 160 degrees C, layer thickness 0.2 mm) improved the interlaminar shear strength to 55.36 MPa. Microscopic morphology analysis demonstrated that optimized thermal input conditions effectively enhance the fiber-resin interfacial bonding quality. This research provides valuable process references for the additive manufacturing of lightweight and high-strength curved components.

For the fiber composite rotating drums used in specialized equipment in the nuclear industry, they are in a state of long-term high stress during operation. If their damage behavior is studied only from a macro perspective, the practical guiding significance for engineering problems is not significant. In this paper, the cell element method (a meso-scale approach) is embedded into the finite element model, and the meso-scale stress field of the material is calculated using the cell element method. A macro-meso dual-scale damage analysis model is constructed from two aspects: interlayer damage research and intralayer damage research, and the correctness of the model is verified through experiments. By applying this model, the failure mechanisms and damage evolution laws of composite cylindrical shells under two dominant loading conditions (when the ends are subjected to winding-induced prestress loads) are studied, namely compressive instability failure (when compressive loads are dominant) and bending failure (when bending loads are dominant). The results show that: the transverse stress of the matrix in the 31° angled layer leads to compressive instability failure at the end of the cylindrical shell; the bending failure of the 50° angled layer is caused by the axial bending stress concentration at the end of the composite material under winding tension, which induces radial cracks in the circumferential layer and ultimately results in bending failure at the end of the cylindrical shell.

3D quadrangular rotary braiding is one vital technique of high efficiency and flexibility to manufacture preforms for composites. However, the use of this technique is limited to fabricating a few traditional braided fabric structures, which is attributed to the lack of knowledge of its process. Therefore, a model is proposed to simulate the braiding process so as to develop new processes and corresponding fabric structures. First, yarn interlacement patterns under individual propelling of the horn-gear and switch are depicted, which preliminary supplies a reference to the selection of process parameters. Then, the braiding process is digitized based on a novel mixed coordinate system, which constructs the relation between the process and carrier path. Combined with the algorithm transforming carrier path to yarn trajectories, the process-structure model is established. Afterward, the effect of horn-gear and switch movement on the yarn interlacement pattern is illustrated, which further clarifies the utilization principle of braiding process parameters. Several interesting novel braided structures including the surface-core structure are found, which shows the potential of this technique. At last, a new multi-layer interlock structure is created by modifying part of the process parameters of traditional 3D braided structure, which verifies the correctness of above model and effects.

3D printing of continuous fiber-reinforced composites based on material co-extrusion has been a popular research topic in the field of composite manufacturing technology over the past few years. However, its application is limited by poor impregnation. In this paper, an impregnation device for resin-injected fiber tows was designed. It achieved good impregnation for different types of carbon fiber tows, such as 1 k, 3 k, and 6 k. In particular, the prepreg filaments prepared with 6 k fiber tows had a fiber volume fraction of 53.1 % and a tensile strength of 1976.05 MPa. The device was integrated into a 3D printer to enable simultaneous preparation and printing of prepreg filament. The printed samples achieved excellent performance, with tensile strength of 1195.89 MPa (6 k fiber tow samples) and flexural strength of 867.47 MPa (1 k fiber tow samples). The void defects inside the sample were observed using 3D-CT, and the inter-filament and inter-layer voids in the printed sample were found to be important factors affecting the mechanical performance.

To address the challenge of maintaining the internal continuity of carbon fibers in microwave absorption structures produced through conventional methods, this study introduces the interrupted fiber segment array (IFSA) model and the continuous fiber path (CFP) model, which are employed to examine the effect of carbon fiber trajectory continuity within printed layers on microwave absorption performance under different structural parameters. Validation and further insights were achieved through microwave absorption performance tests and surface morphology analyses of the specimens. The findings indicate that the conductive pathways formed by the carbon fibers in the CFP model optimize impedance matching and enhance conductive loss, thereby achieving higher absorptivity than the IFSA model. Absorptivity is maximized when the fiber orientation is 0 degrees. Under these conditions, a fiber spacing of 1.5 mm, a layer thickness of 0.2 mm, and a two-layer or three-layer structure are identified as optimal. This study underscores the advantages of continuous fiber 3D printing technology in the fabrication of microwave absorption structures.Highlights CFP model has higher absorptivity than IFSA model. Continuous carbon fiber conductive pathway enhances conductivity loss. The polarization sensitivity of CF/PLA specimens is strong. 3D printing impacts absorptivity via structural parameters and surface form.

Three-dimensional braided composites have become one kind of critical engineering material for applications in extreme environments. The 3D stepwise rotary braiding process is one vital technique for manufacturing preforms with high efficiency and flexibility. However, the fabric topology is decided by the combination of switch rotation directions, which affects the mechanical properties, and the full carrier configuration results in a loose four-directional structure which is supposed to be improved by adding axial yarns. Therefore, experiments are carried out to illustrate the effect of fabric topology and axial yarn condition on the compressive properties of 3D stepwise rotary braided composites. Samples with three types of fabric topologies named Type A, B, and C are prepared under four axial yarn conditions including no axial yarn addition, 12K axial yarn addition, 24K axial yarn addition, and 36K axial yarn addition, which are fabricated with braiding angles of 20°, 30° and 40°. Longitudinal and transverse compression tests are conducted, and the morphology is observed. It shows that the braiding angle has more influence on the longitudinal compressive properties than transverse compressive properties, and the effect of fabric topology and axial yarn condition depends on the braiding angle. The fabric topology affects a lot on the longitudinal compressive properties when the braiding angle is small, resulting in a gap of up to 40%. The longitudinal compressive properties are improved significantly by adding axial yarns especially for the composites with large braiding angles, making the strength more than double. With the increase in axial yarn size, the strength increment gradually decreases while the modulus declines after a certain size for smaller braiding angles.

Cutting chatter is a major factor that limits machining efficiency and can negatively impact the quality of a cutting surface. Chatter suppression is crucial for improving machining efficiency and maximizing business benefits. However, most chatter suppression techniques are difficult to use on a massive scale in actual production because of their high cost and limited applicability. In the investigation of chatter suppression, particularly in recent years, unique and effective suppression methods have been developed that must be summarized and arranged, and their advantages and disadvantages must be evaluated in depth. Therefore, this paper summarizes and systematically discusses recent research advancements in chatter suppression methods. Furthermore, future research directions for chatter suppression technologies are predicted.

The laser-assisted automated fiber placement (LAFP) in-situ consolidation process shows great potential in the industrial sector. However, its practical application in engineering is hindered by the low interlaminar shear strength (ILSS) and warping of components. This study optimized the distribution of laser heat flux using the non-dominated sorting genetic algorithm-II to enhance ILSS and reduce warping of LAFP in-situ consolidation carbon fiber reinforced polyetheretherketone (CF/PEEK) laminates. A laser irradiation model was developed to calculate the laser heat flux distribution during the LAFP process, and a thermal-mechanical coupling model was created to conduct finite element analysis of the LAFP process. Afterwards, the laser heat flux distribution was optimized using NSGA-II based on the computed temperature histories. The selected parameters from optimization findings were used to conduct placement experiments, and then the ILSS and warping of the CF/PEEK laminates were measured. After optimization, the maximum ILSS achieved was 81.46 MPa at a placement speed of 100 mm/s and a consolidation force of 300 N, which represented 90.5% of the hot compression molding (90 MPa) with the same material. In addition, it is feasible to significantly reduce the central axis warping (37.6% reduction) at the cost of reducing ILSS (6.6% reduction).Highlights Propose an inversion design method for laser spatial pose and laser power. Propose a method for coupling optimization of ILSS and warping. Propose a thermal-mechanical model for LAFP considering laser heat flux load.

During the wet filament winding process, voids often remain within the fibers during resin impregnation, directly affecting the overall performance of the fiber-reinforced product. This study investigates the percolation behavior of resin along fiber bundles during the winding process and proposes a random arrangement algorithm based on the circular profile of filaments. By incorporating waviness, a fiber bundle model with longitudinal kinks is established. Using this spatial model, a two-phase flow simulation is conducted via the finite element method to analyze the resin displacement of air. The study examines the formation, migration, content, morphology, and distribution of voids during impregnation. The findings indicate that spatial inhomogeneity in resin flow leads to air entrapment and void formation. Low pressure extends the impregnation duration, while high pressure exacerbates resin accumulation above the fiber bundle. As pressure or volume fraction increases, void morphology transitions from an approximately spherical shape to elongated narrow strips along the pressure direction. Therefore, optimizing the radius of the impregnation roller to achieve moderate impregnation pressure (0.4-0.6 MPa), controlling resin uptake, or increasing the number of impregnation rollers to enable multi-surface, multi-pass impregnation can enhance impregnation effectiveness. In addition, simulations with different material parameters showed that the winding speed should be increased appropriately when the resin viscosity is low in order to shorten the impregnation time under the same pressure, thus reducing the accumulation of resin on the surface of the fiber bundle.

Combined with digital twin technology, data-driven intelligent parameter optimization can be achieved, which is of great significance for realizing the intelligent manufacturing of thin-walled components. This study conducts a digital twin model study on parameter optimization of thin-walled parts milling processes. Firstly, the system architecture is developed based on the digital twin five-dimensional paradigm, enabling the interaction of virtual and real information through effective system connections. A milling parameter optimization method is devised, considering the goals of energy efficiency and material removal rate, as well as the limits imposed by the chatter factor. The other digital twin models, such as the energy consumption model and chatter model, are constructed to achieve the optimization model. Ultimately, the digital twin system is created, and the construction and testing of the system for the milling process are accomplished. The experiment confirms the efficacy of each function of the system.

Carbon nanotubes can be used to enhance interlaminar mechanical performance of polymer composites due to its physicochemical properties. In this article, a novel method for preparing multi-walled carbon nanotubes/polyether ether ketone (MWCNT/PEEK) thermoplastic composite film is proposed. The microstructure and interfacial bonding performance of chemically modified and unmodified composites films are analyzed by molecular dynamics simulation and experiment. The composite films are used in CF/PEEK laminates. The results show that MWCNT significantly improve the crystallinity of PEEK resin. Compared with the MWCNT/PEEK composites without chemical modification, the MWCNT-COOH/PEEK-OH composite film has a denser structure, and the tensile strength and elastic modulus are increased by 94.5% and 15%, respectively. Furthermore, the interlaminar shear strength of the CF/PEEK laminates doped with MWCNT/PEEK and MWCNT-COOH/PEEK-OH composite films increased by 8.3% and 12.7%, respectively.Highlights The interfacial bonding performance between MWCNT and PEEK is obtained by molecular dynamics simulation. A novel method for preparing BP/PEEK composite film is proposed. MWCNT/PEEK composite films can effectively enhance the interlaminar performance of CF/PEEK high performance thermoplastic composites.