The low carbon footprint, biodegradability, interesting mechanical properties, and relatively low price are considered some of the reasons for the increased interest in polylactic acid-based (PLA-based) filaments supplied with natural fillers. However, it is essential to recognize that incorporating natural fillers into virgin PLA significantly impacts the printability of the resulting blends. The complex inter-relationship between process, structure, and properties in the context of fused deposition modeling (FDM)-manufactured biocomposites is still not fully understood, which thus often results in decreased reliability of this technology in the context of biocomposites, decreased accuracy, and the increased presence of defects in the manufactured biocomposite samples. In light of these considerations, this study aims to identify the optimal processing parameters for the FDM manufacturing process involving wood-filled PLA biocomposites. This study presents an optimization approach consisting of Grey Relational Analysis in conjunction with the Taguchi orthogonal array. The optimization process has identified the combination of a scanning speed of 70 mm/s, a layer height of 0.1 mm, and a printing temperature of 220 °C as the most optimal, resulting in the highly satisfactory combination of good dimensional accuracy (Dx = 20.115 mm, Dy = 20.556 mm, and Dz = 20.220 mm) and low presence of voids (1.673%). The experimentally determined Grey Relational Grade of the specimen manufactured with the optimized set of process parameters (0.782) was in good agreement with the predicted value (0. 754), substantiating the validity of the optimization process. Additionally, the research compared the efficacy of optimization between the integrated multiparametric method and the conventional monoparametric strategy. The multiparametric method, which combines Grey Relational Analysis with the Taguchi orthogonal array, exhibited superior performance. Although the monoparametric optimization strategy yielded specimens with favorable values for the targeted properties, the analysis of the remaining characteristics uncovered unsatisfactory results. This highlights the potential drawbacks of relying on a singular optimization approach.

: The exploited enthusiasm within the research community for harnessing PLA-based biocomposites in fused deposition modeling (FDM) is spurred by the surging demand for environmentally sustainable and economically viable materials across diverse applications. While substantial strides have been taken in unravelling the intricacies of the process-structure–property relationship, the intricate interdependencies within this context remain only partially elucidated. This current gap in knowledge presents formidable obstacles to achieving the pinnacle of quality and dimensional precision in FDM-fabricated specimens crafted from biocomposites. Despite the existence of numerous numerical models for simulating the FDM process, an unmistakable need exists for models that are custom-tailored to accommodate the distinct characteristics inherent to biocomposites. As a reaction to those pressing needs, this study presents a 3D coupled thermomechanical numerical model designed to predict dimensions, defect formation, residual stresses, and temperature in PLA/wood cubes produced by FDM, considering various process parameters and composite-like nature of wood-filled PLA filaments. The accuracy of the proposed numerical model was validated by comparing its results with experimental measurements of biocomposite cubes manufactured under the same process parameters. Encouragingly, the simulated dimensions showed a maximum relative error of 9.52% when compared to the experimental data, indicating a good agreement. The numerical model also successfully captured the defect formation in the manufactured cubes, demonstrating consistent correspondence with defects observed in the experimental specimens. Therefore, the presented model aims to substantially contribute to the progress in the field of additive manufacturing of PLA-based biocomposites.

Despite the emerging research, it is still troublesome to control the defect formation in selective laser melted parts. In this paper, a mechano-thermo-fluid dynamical FEM model at meso-scale was proposed. The model calculates and predicts the volume and dimensions of lack-of-fusion (LOF) defects according to the applied process parameters. The numerical model was validated with experimentally assessed volume of lack-of-fusion defects and melt pool dimensions. Results shown the lowest volume of LOF defects (0.41%) in specimen produced with 275 W and scanning speed of 400 mm/s. Laser power was determined as a parameter majorly impacting the volume and dimensions of LOF in selective laser melted parts.

This study investigates the performance of thermoacoustic engines by examining the influence of stack position, resonator length, and working fluid on energy conversion efficiency. Numerical simulations reveal that placing the stack at an intermediate location (e.g., 60 mm in a 350 mm resonator) maximises efficiency by promoting stable, single-mode harmonic oscillations and minimising boundary layer interference. Deviations from this optimal position (e.g., 30 mm or 90 mm) induce secondary harmonics, reducing efficiency. Doubling the resonator length while maintaining proportional stack scaling preserves performance, indicating aspect ratio is not a limiting factor. Simulations with helium, as opposed to air, yield a tripled resonance frequency (∼$700 \mathrm{H}\mathrm{z} \mathrm{v}\mathrm{s}. 245 \mathrm{H}\mathrm{z}$) and significantly higher efficiency (∼$0.38 \mathrm{vs}. \sim 0.13$), due to helium’s superior thermal and acoustic properties. These results provide quantitative guidelines for optimising thermoacoustic engine design for sustainable energy applications.