Preheating in selective laser sintering plays a critical role in enhancing mechanical properties and dimensional accuracy. Due to equipment of image shape laser sintering limitations that prevent direct preheating of the build chamber, a thermal conditioning strategy was implemented in the powder supply chamber instead. To assess the impact of supply chamber preheating for image shape laser sintering on part strength and dimensional accuracy, this study systematically examined the effects of laser power (16–22 W), scanning speed (6–12 mm/s), layer thickness (0.1–0.25 mm), and preheating temperature (70°C–85°C) of carbon black/ pinewood/ polyethersulfone composites. Temperature calibration confirmed that the powder could be preheated to 85°C, maintaining a powder bed surface temperature of approximately 60°C during spreading. Orthogonal experiments, combined with a weighted performance analysis, showed that scanning speed had the most significant influence on mechanical and geometric outcomes, while layer thickness had the least. The optimal process parameters—20 W laser power, 85°C preheating temperature, 0.15 mm layer thickness, and 6 mm/s scanning speed—resulted in a twofold increase in tensile strength and a 1.5-fold increase in flexural strength compared to samples processed without preheating. These results highlight the effectiveness of indirect preheating and offer a practical solution for cases in which direct build chamber heating is not feasible.

Polylactic acid (PLA) is a biodegradable polymer suitable for selective laser sintering, yet the atomic-level sintering mechanisms of plant-fiber-reinforced PLA remain largely unclear. Here, all-atom molecular dynamics simulations are used to investigate the sintering behavior and interfacial interactions of cotton stalk cellulose/PLA nanocomposites with cellulose contents of 0–20 wt%. The nanoparticle coalescence proceeds through three sequential stages: initial van der Waals-driven contact, thermally activated neck formation, and isothermal interfacial densification. Cellulose content exerts a non-monotonic effect on interfacial bonding. At 15 wt% cellulose, an optimal, continuous hydrogen-bond network forms between cellulose hydroxyls and PLA carbonyls, maximizing interfacial adhesion while simultaneously suppressing unfavorable dipole stacking among PLA chains. At this critical loading, cellulose acts as a rigid scaffold that directs PLA chain extension and directional migration across the sintering neck, as evidenced by atomic displacement and radius-of-gyration analyses. In contrast, excess cellulose (20 wt%) induces self-aggregation, which disrupts interfacial continuity and hinders long-range chain diffusion. These results identify 15 wt% as the optimal cellulose content for achieving enhanced interfacial bonding and chain mobility, providing atomistic criteria for the formulation design of cellulose/PLA composite powders for selective laser sintering processing.

Laser sintering (LS) technology is one of the most widely commercialized additive manufacturing technologies. However, the popularization of LS technology in civilian applications has long been constrained by accuracy-related issues. Polyamide (PA), as the most mature LS material, still faces challenges in controlling part dimensional errors. Biomass materials, when used as fillers, can improve the printing accuracy of fabricated parts, demonstrating a technically feasible synergy between PA and biomass materials. Therefore, this study analyzes the fundamental material properties of PA/pine biomass composites and investigates error control methods for LS-fabricated parts using PA/biomass materials as feedstock. This study investigates the error modeling of LS-fabricated parts from two perspectives. First, a theoretical mathematical model is established to predict part errors by incorporating material properties, process parameters, and equipment factors. Second, a data-driven model is developed using BP neural network technology based on experimental data to correlate LS process parameters with part dimensional errors. Additionally, the predictive capabilities and compensation effects of both models are examined. The experimental results indicate that the nylon/pine wood biomass composite with a pine wood content of 3 wt% can produce molded parts with a tensile strength of 20 MPa. Additionally, this material exhibits a sintering preheating window range of 10 °C, which facilitates the production of parts with both favorable mechanical properties and dimensional accuracy. Both error prediction models are capable of predicting the dimensional deviations of the parts. The data-driven model demonstrates superior deviation prediction accuracy (approximately 81–91%) for LS parts compared to the theoretical mathematical model (approximately 62–73%). By applying compensation based on the error prediction models, the overall dimensional deviation can be reduced from 1.61–3.49% to 0.41–0.50%. Consequently, the part’s precision grade (according to ISO 2768) is improved from below Grade V to Grade C.