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Abstract. The Vacuum-Assisted Resin Infusion Molding (VARIM) process is widely used in wind turbine blade manufacturing due to its cost-effectiveness and reliability. However, challenges such as prolonged curing cycles and defects caused by non-uniform cure remain persistent. To address these issues, multizone heating systems have been developed to enable independent temperature control across blade sections. Yet, optimizing the temperature profile in each zone is computationally intensive, requiring detailed modelling of curing kinetics and heat transfer mechanisms. To overcome these challenges, in this work, a machine learning (ML) based digital twin of the VARIM process was developed using a time-distributed long short-term memory (LSTM) network trained on data generated by a high-fidelity multiphysics solver. The model achieved a predictive accuracy of 96.7 % in replicating the resin curing behavior. Its time-distributed architecture effectively captures the spatial – temporal dependencies across multiple zones, allowing precise prediction of the degree-of-cure evolution. Paired with a gradient-free optimization algorithm, the digital twin reduced curing time by 12.5 % while improving cure uniformity. This AI-driven framework eliminates costly trial-and-error experimentation, and provides a scalable, adaptive solution for improving both quality and productivity in wind turbine blade manufacturing, with strong potential for extension to other composite manufacturing processes. 

Abstract The vacuum-assisted resin infusion mold (VARIM) process is widely used in wind blade manufacturing for its cost-effectiveness and reliability. However, the current method faces challenges such as long curing times and defects due to nonuniform heating across the blade structure. To address this, a multi-zone heated bed setup tailored to blade thickness has been considered. However, determining an optimal temperature for each zone poses a computational challenge, which can be tackled with a novel machine-learning approach. Using a digital twin based on a high-fidelity multiphysics solver, a time-distributed LSTM model was trained to understand complex resin curing dynamics. This eliminates the need for costly lab experiments, as the model learns heating patterns and curing behavior efficiently. Once trained, the ML model acts as a digital twin by predicting the degree of cure for a given temperature setpoint with 96.73% accuracy. This model, when used as a surrogate for a Nelder-mead optimization workflow, improves the curing time by roughly 12.5% and presents a more uniform curing rate throughout the part. 

In this article, the use of additive-manufactured thermoplastics, specifically polylactic acid (PLA), to fabricate segments of wind turbine blades with core sandwich composites was verified through their compressive bucking performance, demonstrating their costeffectivness in manufacturing and transportation. A small wind blade was constructed by joining these segments to demonstrate their application potential in renewable energy technologies. The study’s focus was on the compressive buckling behavior of these fusion joined blades, particularly on the heterogeneity at the resistance welding bond line. An approach was adopted to integrate a hybrid of solid and cohesive elements within the cohesive zone modeling (CZM) framework using the Abaqus–Riks method. This allowed us to insert a thin layer of solid–cohesive elements at the bond line, enhancing the fidelity of our simulations. The validity of our numerical results was examined by comparing them with the surface strain field measured by digital image correlation (DIC) and assessing the compressive response. Furthermore, the applicability of classical Euler and Johnson formulas was evaluated in predicting buckling loads and modes. The Euler formula was found adequate for the first flexural buckling mode in beams with high slenderness ratios (≥12). Our findings demonstrate that the hybrid CZM approach effectively models the buckling behavior of fusion-joined beams, accommodating a range of slenderness ratios (6 to 18) and various buckling modes. This study provides insights into the structural analysis of fusion-joined components for potential applications of additive manufacturing in wind energy. 

Additively manufactured thermoplastic polymers, such as polylactic acid (PLA), hold significant promise for sustainable engineering structures, including wind turbine blades. Upscaling these structures beyond the limitations of 3D printer build volumes is a challenge; fusion joining presents a potential solution. This paper introduces a displacement-controlled resistance welding process for PLA, as an alternative to the typical force controlled methods. We investigated the bonding quality of resistance-welded and adhesive-bonded PLA beams through three-point bending and measured the surface deformations using digital image correlation. Different metal meshes (30 %/0.11 mm Ni–Cu, 34 %/0.07 mm Ni–Cu, and 36 %/0.25 mm Co–Ni) served as heating elements. The process parameters were varied for the 34 %/0.07 mm Ni–Cu mesh to identify an optimum set of parameters. Results showed that this optimized displacement-controlled welding achieved 94 % of the original strength of monolithic samples. This indicates that the new welding process not only ensures high quality bonding and fine surface finishing but also promotes sustainability, recyclability, and economic efficiency in various polymer and composite structural applications. 

Strong, tough, and lightweight composites are increasingly needed for diverse applications, from wind turbines to cars and aircraft. These composites typically contain sheets of strong and high-modulus fibers in a matrix that are joined with other materials to resist fracture. Coupling these dissimilar materials together is challenging to enhance delamination properties at their interface. We herein investigate using a trace amount of carbon nanotube sheets to improve the coupling between composite skins and core in a composite sandwich. Ultra-thin (~100 nm) forest-drawn multi-walled carbon nanotube (MWNT) sheets are interleaved within the skin/core interphase, with MWNTs aligned in the longitudinal direction. The mechanical behavior is characterized by end notched flexural testing (ENF). With two MWNT sheets placed in the skin/core interphase, the following performance enhancements are achieved: 36.8 % increase in flexural strength; 127.3 % and 125.7 % increases in mode I & II fracture toughness values, respectively; and 152.8 % increase in interfacial shear strength (IFSS). These are achieved with negligible weight gain of the composite sandwich (0.084 wt% increase over the baseline sandwich without MWNT sheets). The finite element simulation results show that MWNT sheets enhance the skin/core coupling by reducing stress concentration, enabling the transition from catastrophic brittle failure to a stable ductile failure mode. The MWNT sheets interleaved sandwich composites are thus demonstrated to be stronger and tougher while providing electrical conductivity (4.3 × 104 S/m) at the skin/core interface for potential de-icing, electromagnetic interference shielding, and structural health monitoring. 

Spread tow carbon fiber composites are receiving increased attention for diverse applications in space and sports gear due to their thin form, which is suitable for deployable structures, and high tensile strength. Their compressive strength, however, is much lower than their tensile strength due to low interlaminar strength. Herein we report a facile technique to enhance their performance through interlaminar insertion of aligned carbon nanotube (CNT) sheets. The inserted CNT sheets also provide electrical conductivity in the composites even at a low CNT loading below the electrical percolation threshold established for CNT-filled composites. Mechanical and electrical characterization was conducted on the CNT sheet-inserted composites and the baseline composites. Results show that the CNT sheets increase the compressive strength by 14.7% compared with the baseline. Such an increase is attributed to the increased adhesion provided by the inserted CNT sheets at the interface between neighboring plies, which also increases the interlaminar shear strength by 33.0% and the interfacial mode-II fracture toughness by 34.6% compared with the baseline composites without inserting CNT sheets. The well-aligned CNT sheet structure maintained between the neighboring plies contributed to a 64.7% increase in electrical conductivity compared with the baseline composites. The findings indicate that the insertion of well-aligned ultrathin CNT sheets in the interlaminar region of a spread tow carbon fiber composite provides significant enhancement in mechanical and electrical performance, paving the path toward applications where both mechanical and electrical performances are crucial, such as for structural health monitoring, lightning protection, and de-icing in aircraft and wind blades. 

The additive manufacturing (AM) industry increasingly looks to differentiate itself by utilizing materials and processes that are green, clean, and sustainable. Biopolymers, bio‐sourced raw materials and light weighting of parts 3D printed with photopolymer resins each represent critical directions for the future of AM. Here, we report a series of bio‐based composite resins with soybean oil derivatives, up to 20% by weight of surface‐methacrylated micro‐crystalline cellulose (MCC) and 60% total bio‐based content for vat photopolymerization based additive manufacturing. The ultimate tensile strengths of the materials were found to increase up to 3X, the Young's moduli increased up to 10X, and the glass transition temperature increased by 11.3°C when compared to the neat resin without surface‐methacrylated MCC as a filler. Working curves and shrinkage factors were used to demonstrate how the surface‐methacrylated MCC causes changes in the dimensions of printed parts, to facilitate development of optimized print parameters based on the UV intensity of the 3D printer being used. These results will allow further development of commercial 3D printable resins with a high concentration of bio‐based fillers that print well and perform on par with conventional resins. 

Wire arc additive manufacturing (WAAM) has received increasing use in 3D printing because of its high deposition rates suitable for components with large and complex geometries. However, the lower forming accuracy of WAAM than other metal additive manufacturing methods has imposed limitations on manufacturing components with high precision. To resolve this issue, we herein implemented the hybrid manufacturing (HM) technique, which integrated WAAM and subtractive manufacturing (via a milling process), to attain high forming accuracy while taking advantage of both WAAM and the milling process. We describe in this paper the design of a robot-based HM platform in which the WAAM and CNC milling are integrated using two robotic arms: one for WAAM and the other for milling immediately following WAAM. The HM was demonstrated with a thin-walled aluminum 5356 component, which was inspected by X-ray micro-computed tomography (μCT) for porosity visualization. The temperature and cutting forces in the component under milling were acquired for analysis. The surface roughness of the aluminum component was measured to assess the surface quality. In addition, tensile specimens were cut from the components using wire electrical discharge machining (WEDM) for mechanical testing. Both machining quality and mechanical properties were found satisfactory; thus the robot-based HM platform was shown to be suitable for manufacturing high-quality aluminum parts.