Fused filament fabrication is a popular extrusion 3D printing technology because of its affordability and accessibility. However, the approach often suffers from printing errors that result in wasted time, materials and energy. Convolutional neural networks can be trained to recognise a wide spectrum of printing anomalies from image data in real time, but past work has been limited to a few defect classifications at a time. Here, we introduce a fault detection system, designed to identify a range of errors without interrupting the printing process. Real-time detection is achieved using a pre-trained image recognition and pattern recognition convolutional neural network (CNN) with two mounted cameras on the print bed and a nozzle camera. Two CNN models are developed to classify images into common 3D printing errors for the two camera systems. The nozzle camera model achieves a high validation accuracy of 97.7%. The side camera model achieves comparable performance with a validation accuracy of 97.6%. To integrate the two CNNs into one unified system, a logic-based priority framework was used to improve reliability beyond individual model accuracies by resolving conflicting predictions and leveraging complementary viewing angles from both camera types to detect a broader range of defects. The data fusion framework identifies 12 common errors and has significantly improved the robustness of error classification, in-situ and in real-time, with inference times as small as 220 milliseconds. The results demonstrate the feasibility of a robust multi-input fault detection system to advance the reliability of extrusion 3D printing.

Driven by the needs of modern transportation and the clean energy transition, the demand for sustainable and lightweight materials is increasing. Composite materials incorporating natural fibers such as flax fibers have gained attention due to their carbon-capturing potential and good specific mechanical properties. However, when embedded in hydrophobic polymer matrices, flax fibers exhibit inferior mechanical performance primarily due to their hydrophilic composition and discontinuous fiber architecture. Biological materials such as nacre have developed useful strategies through mineralization to distribute localized stresses and develop extrinsic toughness that could inspire a solution to enhance stress transfer in natural fiber composites. Here, we report a biomineralization strategy to introduce an additional hierarchy to flax composites. By tuning salt concentrations in the process, we achieve controlled deposition of microbe-mediated mineral particles on flax yarns. With controlled biomineralization, we show that the minerals can enhance the compressive toughness by 178% and compressive strength by 30%. The findings highlight a novel bio-inspired pathway for tailoring composite performance through sustainable processing, offering a scalable and environmentally friendly approach to enhance natural fiber composites for structural applications.

The development of sustainable and high-performance composite materials necessitates effective strategies to improve compatibility between natural fibres (NFs) and circular thermoplastic matrices. We report atmospheric pressure (AP) plasma treatment as a scalable, interface-engineered approach to enhance the mechanical performance of unidirectional flax fibre (FF)-reinforced polyoxymethylene copolymer (coPOM) composites. FF fabrics were treated using diffuse coplanar surface barrier discharge plasma and processed into high fibre volume fraction (FVF ∼ 61%) composites via film stacking. Post-treatment surface analysis revealed removal of waxy species and increased oxygen-containing functional groups, reflected by an increased O/C ratio (from 0.48 to 0.74) and elevated total surface energy (from 29.4 to 41.8 mJ/m²). The wetting improved significantly, with spreading coefficient changing from − 3.9 mJ/m² to positive values. At the coupon level, transverse tensile modulus and strength increased by ∼ 67 % and ∼ 23 %, and transverse compressive modulus and strength by ∼ 46 % and ∼ 43 %, respectively. Shear strength (∼ 23%) and in-plane shear moduli (∼ 31 %) were also enhanced. In contrast, longitudinal tensile strength decreased by ∼ 12% despite a 12% increase in longitudinal modulus, while failure-envelope analysis indicated an expanded inter-fibre failure domain. Together, the results highlight AP plasma treatment as a roll-to-roll-compatible route to significantly improve off-axis mechanical performance of NF/thermoplastic composites.

The pursuit of high-performance structures requires innovative manufacturing approaches that balance multiple competing factors. This work focuses on one such novel approach, named Advanced Tape Laying Additive Manufacturing (ATLAM), that integrates the characteristics of automated tape laying and additive manufacturing. ATLAM supported by a dedicated complementary design tool can provide a unique ability to balance mechanical performance, manufacturing flexibility, and eco-efficiency. To exploit the full potential of ATLAM in manufacturing lightweight sustainable structures, we aim to develop, verify and experimentally validate a computational design tool rooted in topology optimization. We use geometric projection topology optimization framework, and tailor it to accommodate ATLAM’s unique features and constraints. We customize the geometric primitive, the projection & material interpolation schemes and design/manufacturing constraints. Then, based on numerical investigation, we showcase excellent effectiveness and robustness of the proposed approach in optimizing geometry and reinforcement placements and designing stiffer yet manufacturable structures. An environmental impact constraint is incorporated to enable a design choice on sustainability. Finally, we experimentally validate our proposed approach, paving the way for reliable use in real-world applications.

Electrification, including emerging technologies such as structural supercapacitors, is critical in realizing carbon-neutral transportation. A fundamental challenge is the trade-off between mechanical properties and energy storage capabilities. We report the fabrication of structural supercapacitors with a novel fibre-fibre interface to improve the interlaminar strength and encapsulation while considering the effect of structural resin on energy storage performance. The synthesized graphene nanoplatelets-modified electrodes attain a high specific surface area of ∼231 m² g⁻¹ - outperforming comparable carbon-based electrodes. We learned that the use of a gel-polymer electrolyte (GPE) separator containing 60 wt% Li-salt eliminates the requirement of electrolyte infusion and showed the highest values for conductivity for the cell produced using GPE. The implementation of glass fabrics (GFs) into the GPE improved the flexural modulus by ∼22%, while retaining the mechanical strength of the cells. The multifunctional performance of the produced SSCs were on par or even outperformed the performances of SSCs reported in literature. A proof-of-concept prototype demonstrates that gel-polymer electrolyte cells can retain charges for longer than those with a glass fibre separator. Cumulatively, these offer the possibility of conventional composite manufacturing techniques to scale-up and eliminate delamination issues arising from different thermal expansion coefficients which also addresses the balance between mechanical stability and electrochemical performance. Our findings support the advancement of durable, lightweight energy storage and delivery systems for sustainable transportation, with potential applications in robotics and wearable technologies.

Rain-induced erosion of wind blades is a challenge to wind energy growth. As blade lengths and tip speeds increase, droplet-impact kinetic energy increases, accelerating surface degradation and reducing aerodynamic efficiency. Conventional polyurethane coatings require maintenance and are unable to withstand prolonged exposure to high-frequency impact stresses. Recent approaches have investigated impedance-matched multilayer and particle-reinforced coatings, but these often suffer from abrupt impedance transitions and weak interfacial adhesion. Here, we demonstrate that a bio-inspired, platelet-reinforced polyurethane coating with a graded through-thickness architecture enhances erosion resistance. We reason that minimising the acoustic impedance mismatch between the coating and substrate while maintaining a compliant outer layer reduces interfacial stresses. Compared to monolayer coatings, our system doubles the incubation time under erosion testing, confirming increased durability. Dynamic Mechanical Analysis shows that platelet volume fraction governs the viscoelastic and acoustic impedance behavior, while orientation has negligible influence on viscoelasticity but is critical for wave propagation and damage evolution. We demonstrate that these graded architectures inspired by natural impact-resistant structures offer superior protection. By providing a deeper understanding of the interplay between acoustic impedance, viscoelasticity, and wave propagation, our study lays the groundwork for designing bio-inspired graded coatings that actively mitigate impact damage in renewable energy applications.

Carbon fibre reinforced plastic (CFRP) exhibits complex optical behaviour due to its anisotropy and highly scattering surface. These optical characteristics pose significant challenges for the automated laser-based inspection systems used in CFRP manufacturing, as they lead to variations in light interaction with the material, affecting the accuracy and reliability of inspections. To investigate this complex optical behaviour, an inverse optical model based on the Multi-Gaussian method has been developed. Laser speckle patterns from the CFRP surface are decomposed into multiple Gaussian components to model the material's optical properties. A greedy optimisation algorithm is employed to estimate the optimal coefficients for the Gaussian sets, which are further refined by introducing negative amplitude Gaussian components. These enhancements improve the optimisation, resulting in a better correlation between the Multi-Gaussian model and actual laser speckle measurements.

In this study, a 3D-printed biomimetic overlapping curl structure inspired by spider silk molecular structure, containing sacrificial bonds and hidden lengths, is studied as a toughening mechanism for a bio-based epoxy. Experimental results of the fracture phenomena of the overlapping curl-reinforced bio-based epoxy identify three toughening mechanisms triggered by the overlapping curl: (1) crack re-initiation, (2) overlapping curl bridging, and (3) epoxy ligament. First, the integrated overlapping curl creates a void within the epoxy matrix. As the crack tip reaches the end of this void, the crack re-initiates. Then, as the hidden length of overlapping curl unfolds, it leads to a bridging effect in resisting crack growth. In addition, for the smallest hidden length, an epoxy ligament is formed due to crack branching, significantly improving the energy release rate. The epoxy fracture energy release rate increased by 13 %. The overall modest improvement is attributed to the large plastic dissipation energy of the epoxy and the relatively low overlapping curl load-capacity. However, when expanding the design space numerically, it was shown that as the failure load of the overlapping curl increases, the bridging effect increases progressively. The introduction of the bio-inspired overlapping curl structure into bio-based epoxy proves the concept of a toughening strategy for developing high-performance sustainable composite materials.

Design for composite material additive manufacturing is governed by multiple process variables that can be computationally expensive to optimize. This is especially true when considering discrete variables, such as the material type to be used, which lead to a lot of possible solutions that have to be evaluated. Here, we propose a workflow for optimizing topology and fiber placement of 3D volumetric structures based on mechanical performance under multiple load cases and environmental impact. An eco-informed material selection from a set fibers and polymers is followed by a methodology to optimize the manufacturing setting. By performing these two steps sequentially, the number of input parameter sets to be tested is reduced in a combinatorial scale, along with the computational cost. The framework can be easily extended by adapting the analyses and holds significant promise for the design of additive manufactured sustainable structures.

Where the trunk of a tree splits into co-dominant branches, wood fibres are highly interlocked. Such an arrangement of fibres has been shown to impart superior strength and toughness to this critical junction. Here, wavy patterns are 3D printed with a liquid crystal polymer (LCP) to evaluate the potential of wood-inspired localized adaptations to improve the robustness of junctions between orthotropic struts. The highly anisotropic, fibrillar microstructure of LCPs is harnessed by Fused Filament Fabrication, yielding Young's modulus and tensile strength reaching 30 GPa and 500 MPa respectively. However, unidirectional 3D-prints subjected to normal tensile stresses show weak interfaces, like in wood. To overcome this weakness, sinusoidal, helix and saw-tooth patterns are 3D-printed to create interlocking between layers. A trade-off is established between uniaxial tension and short-beam shear with increasing interlocking angle of the sinusoidal pattern. We find that the work associated with crack propagation in Mode I is increased three-fold compared to a unidirectional pattern, through extrinsic toughening. When applied to a more complex load case in a curved beam in four-point bending, helix-patterning at the junction zone increases the maximum load by 88 %. By locally controlling anisotropy via waviness, this method opens the possibility of improving toughness and transverse properties where the stress state is multi-axial without adding mass in future recyclable structural materials.

This work explores a spatial printing method to fabricate continuous fiber-reinforced thermoplastic composites (CFRTPCs), which can achieve exceptional mechanical performance. For models giving complex 3D stress distribution under loads, typical planar-layer based fiber placement usually fails to provide sufficient reinforcement due to their orientations being constrained to planes. The effectiveness of fiber reinforcement could be maximized by using multi-axis additive manufacturing (MAAM) to better control the orientation of continuous fibers in 3D-printed composites. Here, we propose a computational approach to generate 3D toolpaths that satisfy two major reinforcement objectives: (1) following the maximal stress directions in critical regions and (2) connecting multiple load-bearing regions by continuous fibers. Principal stress lines are first extracted in an input solid model to identify critical regions. Curved layers aligned with maximal stresses in these critical regions are generated by computing an optimized scalar field and extracting its iso-surfaces. Then, topological analysis and operations are applied to each curved layer to generate a computational domain that preserves fiber continuity between load-bearing regions. Lastly, continuous fiber toolpaths aligned with maximal stresses are generated on each surface layer by computing an optimized scalar field and extracting its iso-curves. A hardware system with dual robotic arms is employed to conduct the physical MAAM tasks depositing polymer or fiber reinforced polymer composite materials by applying a force normal to the extrusion plane to aid consolidation. When comparing to planar-layer based printing results in tension, up to 644% failure load and 240% stiffness are observed on shapes fabricated by our spatial printing method. We demonstrate the versatility of our approach through various complex load cases which demonstrate their successful implementation of continuous fiber printing in 3D.

Carbon-based products are essential to society, yet producing them from fossil fuels is unsustainable. Microorganisms have the ability to take up electrons from solid electrodes and convert carbon dioxide (CO₂) to valuable carbon-based chemicals. However, higher productivities and energy efficiencies are needed to reach a viability that can make the technology transformative. Here, we show how a biofilm-based microbial porous cathode in a directed flow-through electrochemical system can continuously reduce CO₂ to even-chain C2–C6 carboxylic acids over 248 days. We demonstrate a threefold higher biofilm concentration, volumetric current density, and productivity compared with the state of the art. Most notably, the volumetric productivity (VP) resembles those achieved in laboratory-scale and industrial syngas (CO-H₂-CO₂) fermentation and chain elongation fermentation. This work highlights key design parameters for efficient electricity-driven microbial CO₂ reduction. There is need and room to improve the rates of electrode colonization and microbe-specific kinetics to scale up the technology.