Interlayer shifting between plies of woven fabrics is unavoidable during lay-up in liquid composite molding (LCM) and can induce large scatter in measured in-plane permeability. However, its quantitative effect on local flow remains poorly characterized. This work develops an integrated meso-scale framework combining a transversely isotropic hyperelastic yarn model, finite-element compaction analysis and voxel-resolved StokesDarcy flow simulations for a six-layer E-glass plain-weave EWR600-1000 reinforcement. The constitutive law is calibrated against restricted and unrestricted yarn compression tests and accurately reproduces the observed nonlinear stiffening under transverse compaction (R2 = 0.9881 and 0.9712). For 256 distinct in-plane interlayer shifting vectors, the compacted fiber volume fraction Vf at 0.1 MPa ranges from 42.5 % to 54.7 %, and the associated local in-plane permeabilities vary by more than one order of magnitude. The resulting map K(sx, sy) exhibits pronounced symmetries and diagonal repeatability. The predicted mean permeabilities (Kx = 5.2 x 10-11 m2, Ky = 1.23 x 10-10 m2) and anisotropy ratio (Kx/Ky = 0.42) agree closely with radial-flow experiments, while the simulated scatter forms a lower bound for the experimental variability. The proposed framework provides a quantitatively validated link between interlayer shifting and both local and effective in-plane permeability, offering guidance for robust design and variability management in LCM processes.

Porosity remains a critical and unavoidable defect in carbon fiber reinforced polymer (CFRP) composites, significantly impairing mechanical performance and structural integrity, especially in complex geometries. Classical prediction models, relying on oversimplified assumptions like spherical voids in an infinite resin matrix, often fail to provide accurate predictions across diverse manufacturing scenarios. This paper presents a high-fidelity, multi-physics framework to overcome these limitations. The model introduces key physical enhancements: (i) adoption of realistic elongated cylindrical void geometry consistent with micro-computed tomography; (ii) incorporation of a finite resin shell to constrain void growth; and (iii) explicit accounting of mechanical and diffusive interactions between voids and surrounding fibers. Following single-point calibration, the model showed strong quantitative agreement with experimental porosity data for flat laminates manufactured under various curing pressures, heating rates, and initial moisture contents. Furthermore, coupled with flow-compaction simulation to capture process-induced pressure gradients, the framework accurately predicted non-uniform porosity distribution in the fillet region of an L-shaped component. The strong correlation between predicted and measured porosity in this complex geometry underscores the model’s robustness and industrial relevance. This work advances reliable, physics-based virtual optimization of composite manufacturing and quality control.

The eigenstrain model is a novel and efficient method for accurately predicting the curing deformation of composite materials, but it currently lacks a straightforward approach for obtaining input parameters. This study addresses this gap by proposing an inverse method to calibrate a two-stage eigenstrain model directly from the macroscopic deformation of standard test specimens. The inverse framework uniquely leverages the distinct deformation characteristics of L-shaped specimens and flat laminates; spring-back, which is sensitive to rubbery state properties, is used to identify the first set of parameters, while warpage, governed primarily by inelastic strains accumulated in the glassy state, is used to determine the remaining parameters. The accuracy and effectiveness of the calibrated model are validated against both analytical solutions for L-shaped components and extensive experimental data from various flat laminates. The discussion reveals that the effects of measurement uncertainty and free-edge relaxation on the inversion are negligible. Furthermore, the model was applied to predict the curing deformation of variable-thickness L-shaped parts and complex-shaped T-stringers, with prediction accuracy confirming the practical value of the proposed method for engineering applications.

Accurate prediction of cure-induced deformation in carbon fiber reinforced polymers is essential for the precision manufacturing of aerospace components. To reduce the difficulty of characterizing resin properties in the rubbery state and the heavy reliance on traditional experiments, this study proposes a hybrid inversion framework combining least squares optimization and Bayesian optimization to identify the equivalent properties of unidirectional composites efficiently. An improved micromechanical model is developed by introducing the thickness fraction of interlaminar resin-rich layers, enabling the distinction between transverse and through-thickness properties. Morris global sensitivity analysis shows that the through-thickness coefficients of chemical shrinkage and thermal expansion dominate the spring-back of L-shaped parts, whereas the transverse thermal expansion coefficient mainly controls flat laminate warpage. Accordingly, carbon fiber anisotropic properties are first identified using least-squares optimization, and resin-related process-equivalent properties are subsequently calibrated by Bayesian optimization using measured spring-back angles and warpage curvatures. The inverted parameters outperform directly measured values, limiting the maximum prediction errors to within 6% for flat laminates, 15% for L-shaped parts, and 12% for complex-curvature T-stringers. The proposed framework provides a mechanically interpretable and experimentally efficient route for parameter identification and deformation prediction in composite curing.

Dry carbon-fiber preforms may contain fabric-level defects whose structural significance is difficult to evaluate before liquid molding. This work proposes a quality assessment framework for dry carbon fiber preforms based on defect classification and residual tensile strength prediction. The present study focuses on three representative defect types, namely floating yarn, missing yarn, and hole defects, together with the defect-free condition, under unidirectional tensile loading. A dual-scale ResNet18 feature extraction strategy combined with a support vector machine classifier was developed for fabric image classification. Features extracted from two Gaussian pyramid scales were fused and classified using a support vector machine with a radial basis function kernel. The proposed method achieved a test accuracy of 95.36%, outperforming the original ResNet18 model and the single scale ResNet18 with support vector machine baseline. To quantify the mechanical consequence of detected defects, microscale and mesoscale representative volume element simulations were used to obtain homogenized properties for defect-free and defective satin weave composites, which were then transferred to macroscale tensile models. The finite-element predictions demonstrated good agreement with the experimental tensile test results. Parametric simulations showed that missing-yarn and hole defects dominated tensile-strength loss in the investigated fiber-direction loading condition, whereas floating-yarn defects had a limited influence. The Gaussian process regression model was established to rapidly estimate residual tensile strength from defect type, defect proportion, and ply number. The proposed framework translates visual defect information into a strength-based quality indicator for dry preform assembly and provides a basis for extending performance-informed inspection to complex composite structures.

To systematically investigate the influence of heat treatment temperature on the cavitation erosion and sliding wear behavior of nanostructured WC-10Co-4Cr coatings-a topic that has received limited attention in prior research. The coatings were deposited on TC6 substrates via an improved high-velocity oxygen-fuel (HVOF) process and subsequently heat-treated at 500, 700, 900, and 1100 degrees C under argon protection. Cavitation tests were conducted in deionized water using an ultrasonic vibratory apparatus, while dry reciprocating sliding wear tests were performed to evaluate tribological performance. Results indicate that increasing the heat treatment temperature markedly improves cavitation erosion resistance. The coating treated at 1100 degrees C (H1100) showed a porosity of only 0.23 % and a 77.3 % reduction in mass loss relative to the as-sprayed coating, attributed to microstructural densification that suppressed crack initiation and propagation. In wear tests, heat-treated coatings exhibited lower friction coefficients, with the H900 condition achieving the lowest COF value (0.291) and wear rate (0.329 mm3/N & sdot;m). The dominant wear mechanisms were identified as oxidative wear and micro-cutting. Heat treatment also enhanced the coating's toughness and load-bearing capacity, leading to reduced spalling. These findings demonstrate that post-deposition heat treatment is an effective strategy for enhancing both cavitation and wear resistance of nanostructured WC-10Co-4Cr coatings.

This study investigates the impact of multi-pass laser shock peening (LSP) on the microstructural evolution and tribological performance of laser melting deposited CoCrFeNiMn high-entropy alloy (HEA). Characterization results confirm a stable FCC phase, with LSP inducing significant grain refinement. A hierarchical gradient nanostructure was established, comprising equiaxed nanograins, dense nanotwins, and transitional twin zones. Mechanical testing revealed significant hardness enhancement and a depth-dependent gradient; notably, the improvement in mechanical properties and H/E ratios tends toward saturation after three passes, with the increment from LSP-3 to LSP-4 being markedly reduced. Tribological evaluations (10~100 N) demonstrated that 4-pass LSP (LSP-4) provides the most superior wear resistance, achieving a 45.5% reduction in wear rate at 100 N compared to the initial HEA. However, 1-pass LSP (LSP-1) exhibited inferior performance at low loads due to the competition between limited hardening and increased surface roughness. Microscopic analysis confirms that the gradient nanostructure suppresses catastrophic delamination and severe adhesive wear. This architecture acts as a robust shield that deflects subsurface fatigue cracks and facilitates the formation of a stable tribo-oxide layer, shifting the mechanism toward mild abrasive and oxidative wear. These findings demonstrate that multi-pass LSP is a critical strategy for enhancing the surface integrity of additive-manufactured HEAs.

Low-temperature plasma nitriding (PN) of selective laser melted (SLM) Ti-6Al-4V alloy was achieved through surface nanocrystallization (SNC) pretreatment, and its effects on the microstructure and wear properties of the nitrided layers were systematically investigated. Results demonstrated that at 650 degrees C, the SNC-treated specimen developed a nitrided layer approximately 1.94 mu m thick, exhibiting a wear depth of 1.58 mu m and an ultralow wear rate of 2.5 x 10-6 mm3 /(N & sdot;m), demonstrating wear resistance comparable to that of the 800 degrees C PN-treated specimen. In contrast, the specimen without SNC pretreatment attained only a 0.91 mu m thick nitrided layer, which was completely worn through during wear, resulting in a substantial wear depth of 22.75 mu m. Although SNC pretreatment enhanced the layer thickness to 1.01 mu m at 550 degrees C, substantially exceeding that of the directly nitrided sample, the resulting wear resistance remained inferior to the 800 degrees C treated level. While SNC effectively promotes the plasma nitriding process, its capability to reduce processing temperature was limited, with 650 degrees C identified as the feasible lower-bound temperature window for producing high-quality nitrided surfaces on SLMfabricated Ti-6Al-4V alloy.

In this study, shot peening (SP) was employed to induce surface nanocrystallization (SNC) on the Ti-6Al-4V alloy prior to plasma nitriding (PN). We systematically investigated the impact of this SNC pre-treatment on the microstructure, microhardness, and wear resistance of the subsequently formed nitrided layer. The results indicate that SP successfully induces SNC, yielding an average grain size of 34.96 nm in the subsurface layer. The nano-gradient structure notably enhances PN, evidenced by the SP-PN sample exhibiting a maximum nitrided layer thickness of 3.82 μm, marking a 39% increase compared to the PN (without SP pre-treatment) sample. Furthermore, the maximum surface hardness measures 1093.2 HV, representing a 20% enhancement over the PN sample. The SP pre-treatment substantially enhanced the wear resistance of the nitrided layer, evidenced by significantly reduced wear depths and wear rates under various applied loads. As the worn area transitions from the nitride layer to the matrix, the wear mechanism of the PN sample shifts gradually from micro-fracture wear in abrasive scenarios to adhesive wear and abrasive mechanisms. In conclusion, SP treatment has the potential to augment the nitrided layer thickness and promote the wear resistance of PN.

An internal diameter thermal barrier coating (TBC) system was developed using ID-HVOF for the bond coat and APS for the top coat to enable coating deposition within confined combustor and gas-duct geometries. Microstructural observations show that both layers exhibit uniform morphology, low porosity, and stable phase composition. The coatings demonstrate strong interfacial adhesion, high hardness, and excellent thermal-shock resistance, confirming their mechanical and thermal reliability. Furthermore, an engineering verification validated the system's capability to produce continuous, defect-free coatings in restricted spaces. These results indicate that the proposed internal-diameter TBC process provides an effective solution for aero-engine components with narrow, complex inner surfaces.

The effects of plasma nitriding (PN) on the tribological and corrosion properties of TC6 titanium alloy were studied using X-ray diffraction, microhardness testing, scanning electron microscopy, a friction wear testing machine, and an electrochemical workstation. The results reveal that following PN treatment, a 5-μm-thick nitrided layer and diffusion layer formed on the surface of the TC6 titanium alloy. The surface hardness of the PN sample reached 1321.1 HV0.5, representing a 2.77-fold increase, while the wear scar volume decreased by approximately 99

In this study, (CoCrFeNiMn)1-x/(WC)x composite coatings with WC contents of 0, 20, 30, and 40 wt% were fabricated using directed energy deposition to enhance wear resistance and mechanical properties. The coatings exhibited excellent metallurgical bonding with the substrate and strong interfacial adhesion between WC particles and the HEA matrix, with no cracks observed. The incorporation of WC significantly increased the hardness of the coatings, with WC particles being harder than the HEA matrix and reinforcing the matrix's mechanical properties. Wear tests, using Si3N4 as the counterface material, demonstrated that wear resistance improved with increasing WC content, as evidenced by smoother wear tracks, reduced wear rates, and lower friction coefficients. The wear mechanism shifted from abrasive wear and oxidation in pure HEA to more stable wear behavior with WC reinforcement. These results suggest that WC-reinforced CoCrFeNiMn high-entropy alloy coatings are promising candidates for high-wear applications in demanding environments.

High-quality CoNiCrAlY bond coats are essential for the durability of thermal barrier coating (TBC) systems in aero-engine combustors, yet their deposition on small-diameter liners remains challenging. In this study, a dual-fuel internal diameter high-velocity oxy-fuel (HVOF) system was designed and optimized to overcome issues of limited accessibility and unstable powder delivery. Orthogonal experiments identified optimal spraying parameters, yielding bond coats with low porosity (similar to 1.2 %) and high adhesion strength (similar to 85 MPa). Microstructural analyses revealed dense coatings with uniformly distributed elements and localized O and Al enrichment, indicative of a thin thermally grown oxide (TGO). X-ray diffraction and transmission electron microscopy confirmed the gamma-(Co, Ni, Cr) phase, with nanoscale grains and lattice strain contributing to peak broadening. Hardness values of 250-300 HV further demonstrated the coating's mechanical integrity. Engineering validation on an actual combustor liner confirmed uniform thickness (average 0.091 mm) and reliable deposition quality, establishing the developed system as a promising solution for producing durable bond coats in confined geometries.

This research investigated the influences of some key factors in the prestressed laser peen forming (PLPF) process, namely, the plate thickness, the coverage ratio, and the prestress, on the deformation of 2024-T351 rectangular plates through experiments and numerical simulations. In the experiments, laser parameters, such as the laser energy and spot size, were kept unchanged, and prestress was applied through a piece of self-developed, four-point-bending equipment. The curvature radius of the samples was measured through a digital radius gauge. A corresponding finite element analysis (FEA) model of PLPF was also established to simulate the full procedure of the PLPF, including prebending, laser shock peening, and spring back. Based on the PLPF experimental results, an artificial neural network (ANN) was trained to help to design the process parameters, including the coverage ratio and the amount of prebending, according to the plate thickness and the target curvature radius. By adding a penalty term to the loss function, the amount of prebending (AOP) can be reduced as much as possible. The validation of the ANN was confirmed by three other PLPF experiments.

Accurate prediction of the mechanical behavior of composite laminates is critical for their reliable application in engineering structures. While significant progress has been made in understanding the influence of micro-defects on the mechanical properties of unidirectional composites, quantifying the propagation of uncertainty from the microscale to the laminate level remains a challenge. Traditional deterministic approaches often oversimplify the complex interplay between micro-defects, material variability, and laminate performance. To address these limitations, this study presents a comprehensive framework for uncertainty quantification in composite laminates. By combining experimental characterization, computational modeling, and statistical analysis, this study quantified the impact of micro-defects and delamination on laminate properties. A Polynomial Chaos Expansion (PCE) method was employed to propagate uncertainty from the microscale to the laminate level. The results demonstrated that micro-defects significantly influence the mechanical properties of laminae, leading to normally distributed strength and stiffness values. Delamination was found to primarily affect the mean value of compressive strength without altering the distribution shape. Finally, experimental validation confirmed the accuracy of the proposed uncertainty analysis framework. This research offers a valuable tool for improving the design and reliability of composite structures by providing a quantitative understanding of the uncertainties associated with their mechanical behavior.

WC-reinforced CoCrFeNiMn high entropy alloy (HEA) coatings were successfully fabricated using laser metal deposition (LMD) to explore the interfacial phase evolution and its influence on mechanical behavior. Microstructural analysis revealed that WC particles were well bonded with the HEA matrix through a diffusionmediated transition zone. The matrix retained an FCC structure composed of Co, Cr, Fe, Ni, and Mn (denoted as M), while the WC reinforcement primarily transformed into an HCP-structured W2C phase. At the interface, a diffusion zone with an FCC-structured M3W3C phase formed, accompanied by secondary carbide precipitates such as HCP-structured W12C5.08, FCC-structured WC0.82, and Cr-rich HCP-structured Cr7C3. These interfacial phase transformations facilitated a stable metallurgical bond between the reinforcement and the matrix. Nanoindentation results showed that the reinforcements exhibited significantly higher hardness (26.69 GPa) and elastic modulus (297.11 GPa) compared to the HEA matrix (6.36 GPa, 174.19 GPa), and also contributed to matrix strengthening relative to the pure HEA, which exhibited a hardness of only 3.35 GPa and an elastic modulus of 123.90 GPa. These findings provide valuable insights into the interfacial phase evolution and its role in enhancing the microstructural stability and mechanical performance of WC-reinforced HEA composites.

Titanium alloys are widely used in aerospace and biomedical fields due to their excellent strength-to-weight ratio, yet their poor surface wear resistance remains a critical limitation. While plasma nitriding (PN) has proven effective in enhancing surface hardness of Ti-6Al-4V, conventional PN-treated specimens still exhibit insufficient nitride layer thickness and inadequate hardness gradients under severe fretting conditions. The potential of surface nanocrystallization (SNC) as an innovative pretreatment to synergistically enhance nitriding efficiency and wear resistance has not been systematically investigated. This paper employs shot peening (SP) technology to achieve SNC on Ti-6Al-4V titanium alloy and examines its impact on the microstructural characteristics and fretting wear performance of plasma nitrided layers. The results show that, compared to PN samples without SNC treatment, SNC-pretreated PN samples exhibit a 27 % increase in nitrided layer thickness, reaching 4.123 mu m, a 26 % enhancement in nano-hardness, reaching 13.02 GPa, and smaller TiN nanograin sizes. SNC-treated PN samples penetrate the nitrided layer at a 50 N load, whereas untreated PN samples reach the substrate at a 30 N load. Additionally, SNC-treated PN samples demonstrate lower wear scar depth and wear rates across all loads. The enhanced wear resistance of the nitrided layer with SNC is primarily due to the increased thickness of the nitrided layer and the hardness enhancement from the reduced TiN grain size.

This study seeks to explain the influence of the main geometric parameters on the asymmetric C-shaped composites' curing deformation. A numerical model appropriate for cure simulation was described and verified through experiments. Then, the model was used to evaluate the influence of different parameters instead of experiments. It was found that the mold's radius has the most significant effect on the deformation. Apart from the central angle, all other parameters have a similar impact on the twist angles. While the in-plane twist angle constantly increases in relation to the center angle, the out-of-plane twist angle initially increases and then drops.

This work involved the precise prediction and in-depth analysis of cure-induced warping of T-shaped parts with inconsistent layup. First, the warping mechanism of this kind of part was studied. Then, a finite element analysis (FEA) that considered the parts' structural characteristics and inconsistent layup using the curing mechanical constitutive model was developed to predict the warping. Following verification of the model's accuracy, the effects of material and structural characteristics on warping were investigated. When designing T-shaped parts, selecting materials with lower thermal expansion coefficients can help minimize warping. Additionally, increasing the radius of the chamfer at the rib and flange connections is also effective. By taking into account the effects of structural parameters and inconsistent layup by using both classical laminated plate theory and artificial neural networks (ANN), a rapid and accurate surrogate model for warping prediction was finally developed. It was discovered that the two-layer ANN model performed better than the other two models and could predict warping with accuracy and speed. For the parts analyzed in this work, this model is able to identify the fluctuations of warping captured by FEA. Moreover, by defining deviation as the difference between the predicted values of the surrogate model and the FEA, it can be observed that the majority of deviations for the validation set are within +/- 1.5 mm. This demonstrates that the two-layer ANN possesses strong generalization ability and high accuracy in predicting warping.Highlights The inconsistent layup could significantly affect the T-shaped part's warping. Cutting off a part's rib may significantly reduce its warping. A rapid and accurate model for warping prediction was developed. The layup and structure's influence could be identified by the two-layer ANN. Developing an efficient and accurate method for evaluating the cure-induced deformation of T-shaped composite parts. image