Continuous fibers with outstanding mechanical performance due to the continuous enhancement effect, show wide application in aerospace, automobile, and construction. There has been great success in developing continuous synthetic fiber-reinforced composites, such as carbon fibers or glass fibers; however, most of which are nonrenewable, have a high processing cost, and energy consumption. Bio-sourced materials with high reinforced effects are attractive alternatives to achieve a low-carbon footprint. In this study, continuous bamboo fiber-reinforced polyethylene (CBF/PE) composites were prepared via a facile two-step method featuring alkali treatment followed by 3D printing. Alkali treatment as a key processing step increases surface area and surface wetting, which promote the formation of mechanical riveting among bamboo fibers and matrix. The obtained treated CBF (T-CBF) also shows improved mechanical properties, which enables a superior reinforcement effect. 3D printing, as a fast and local heating method, could melt the outer layer PE tube and impregnate molten plastics into fibers under pressure and heating. The resulting T-CBF/PE composite fibers can achieve a tensile strength of up to 15.6 MPa, while the matrix PE itself has a tensile strength of around 7.7 MPa. Additionally, the fracture morphology of printed bulks from composite fibers shows the alkali-treated fibers–PE interface is denser and could transfer more load. The printed bulks using T-CBF/PE shows increased tensile strength and Young’s modulus, with 77%- and 1.76-times improvement compared to pure PE. Finally, the effect of printing paraments on mechanical properties were analyzed. Therefore, this research presents a potential avenue for fabricating continuous natural fiber-reinforced composites.

All-cellulose composite (ACC) films, with their excellent tunable optical and mechanical properties, combined with biodegradability, represent a highly promising material for applications in the packaging and flexible electronics sectors. The optical properties of ACC films are critically governed by their microstructure, which is determined by drying conditions. In this study, the effects of drying conditions on the structure–property relationships of ACC films were systematically investigated. First, ACC films were fabricated via the partial dissolution of microcrystalline cellulose powder in ionic liquids, followed by a film-casting process. Subsequently, various drying conditions under different temperatures and pressures were applied to finalize the films. XRD characterization demonstrated the coexistence of cellulose I and cellulose II structures. Optical and morphological tests revealed that (1) drying without pressure resulted in obvious shrinkage and deformation, with the diameter reduced by 70%; (2) the high-temperature/high-pressure drying method promoted a dense structure, resulting in ACC films with high transmittance (>90%) and low haze (<10%); and (3) ACC films dried under different hot-press temperature conditions showed similar transmittance and a large difference in haze, which could be related to the micro-pores formed within films. The systematic correlation between structure and optical properties established in this work provides a clear pathway for the tailoring of the optical performance of cellulose films through controlled drying conditions.

Determining optimal process parameters for laser transmission welding (LTW) of solid/porous materials remains challenging due to the complexity of influencing factors. In this study, the welding of solid polycarbonate (PC) and porous polyethylene terephthalate (porous-PET) was chosen as an exemplary case and the relationship between parameters and welding quality was established using a Gaussian process regression (GPR) model. First, the experimental dataset, comprising welding power, welding speed, PC thickness, and porous-PET density, is established based on a flexible factor-level design. Then, the optimized GPR model trained based on the full experimental dataset achieved high predictive performance, significantly outperforming that trained with the averaged experimental dataset. Next, using the optimal prediction model as the objective function, three different optimization methods, genetic algorithm (GA), Bayesian optimization (BO), and covariance matrix adaptation evolution strategy (CMA-ES), are employed to optimize the process parameters, and the performance of the different optimization algorithms shows that CMA-ES has demonstrated the fastest convergence and the shortest runtime, while still converging to the same recommended parameters as GA and BO. Experimental validation confirms the accuracy of the recommended parameters, with a low relative error. Morphological analysis confirms that the weld seam is uniformly formed at recommended parameters. The proposed strategy provides an efficient route for achieving high-performance LTW joints and shows strong potential for improving process efficiency and reducing manufacturing cost in solid/porous materials joining.