Biomimetic design has been used to reduce the high operating resistance of agricultural soil-engaging components, thereby lowering energy consumption. However, most existing contour-based structural biomimetic designs rely on a single or a few biological samples, making the resulting designs susceptible to individual variation and randomness in sample selection. To address this issue, this study used the abdomen of antlion larvae as a biological prototype. Abdominal contours of 85 antlion larvae were extracted from the front, top, and side views, and elliptic Fourier descriptors (EFDs) were used for contour normalization, averaging, and reconstruction to obtain population mean contours. Seven biomimetic wedge specimens were designed based on the population mean contours, and vertical penetration and horizontal cutting tests were conducted in two different media. The results showed that in the vertical penetration tests, the B-FT specimen, which integrated contour features from the front and top views, exhibited the best drag-reduction performance. Its average penetration resistance decreased by 44.26% and 32.81% in quartz sand and loam soil, respectively. In the horizontal cutting tests, the B-FTS specimen, which integrated contour features from all three views, showed the lowest average cutting resistance, with reductions of 17.62% and 36.47%, respectively. The FTS contour features were further applied to the biomimetic design of a subsoiler tine and validated by discrete element method (DEM) simulation and soil bin tests. Compared with the standard subsoiler tine, the biomimetic subsoiler tine reduced draft force by 11.57% in the simulation and by 12.61% in the soil bin test. These results demonstrate the drag-reduction effectiveness of population mean contours and provide a statistically grounded geometric reference for the biomimetic low-resistance design of agricultural soil-engaging components.

Perennial alfalfa roots form a composite with the soil, contributing to intensified grassland degradation and reduced yields. Soil-loosening and root-cutting tools are effective in disrupting root–soil composites and reducing soil compaction. However, loosening and root-cutting operations commonly face challenges, such as high tillage resistance and disturbance. This study developed a simulation model of the alfalfa root–soil composite based on the coupled Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH) method when considering the biomechanical properties of roots. The validity of the model was verified using direct shear and cutting tests. The errors in both simulation and test results were less than 8%. Additionally, a vibration root-cutting shovel was designed. The factors of tillage speed, vibration frequency, amplitude, and direction were analyzed for their impact on tillage resistance and root shear displacement. Results indicated that the incorporation of vibration enhanced soil breaking and reduced root-cutting displacement. The optimal combination of parameters determined using the Response Surface Method (RSM) for minimizing tillage resistance and shear displacement were a tillage speed of 0.86 m·s⁻¹, vibration amplitude of 3.79 mm, vibration frequency of 45.05 Hz, and vibration parallel to the tillage direction. Field tests confirmed the effectiveness of the vibratory root-cutting shovel. The addition of vibration parallel to the tillage direction can reduce tillage resistance by 16.68% and penetration resistance by 26.80%. This study provides a methodology for modeling root–soil composite and improving the root-cutting shovel for grassland degradation restoration.

Fused deposition modeling (FDM) of polylactic acid (PLA) is plagued by dimensional inaccuracies—thermal shrinkage, warpage, and geometric distortion—that restrict its application in high-precision manufacturing. Blending thermoplastic polyurethane (TPU) with PLA enhances toughness, yet the coupled effects of blend ratio and printing parameters on dimensional accuracy remain unclear. This study establishes a multi-objective optimization framework integrating single-factor experiments, orthogonal design, ANOVA, and entropy weight–TOPSIS. Single-factor experiments combined with TOPSIS first identify the optimal PLA/TPU blend ratio, and orthogonal experiments are subsequently conducted on this optimal ratio to determine the best parameter combination. The 70:30 PLA/TPU blend delivers the optimal comprehensive performance (TOPSIS closeness: 0.680), attributed to favorable phase compatibility and robust interfacial adhesion as verified by XRD and SEM. Range analysis and ANOVA on the orthogonal results identify infill rate and layer height as the dominant factors governing dimensional accuracy (p < 0.05). Under the optimized parameter set (60 mm/s, 210 °C, 90% infill, 0.1 mm layer), the total dimensional deviation reaches only 0.256 mm, substantially lower than single-objective counterparts (0.321–0.418 mm). This work offers a validated strategy for precision control in FDM through synergistic optimization of blend formulation and processing parameters.

Polylactic acid (PLA) is widely used in fused deposition modeling (FDM) due to its excellent mechanical properties and processability. However, its inherent brittleness significantly restricts its application in load-bearing and high-toughness scenarios. To address this limitation, this study establishes a multi-objective optimization framework that integrates single-factor experiments, Box–Behnken design (BBD), response surface methodology (RSM), and entropy weight-based objective weighting to simultaneously enhance the tensile strength, elongation at break, and flexural strength of FDM-printed PLA/thermoplastic polyurethane (TPU) blends. Through single-factor experiments, the optimal PLA/TPU blend ratio was determined as 80:20, achieving a tensile strength of 38.93 MPa, an elongation at break of 14.12%, and a flexural strength of 42.33 MPa—representing improvements of 39.4%, 15.7%, and 31.6%, respectively, over the 70:30 blend. Multi-scale characterization via FTIR, XRD, and SEM reveals that this enhanced performance arises from strong interfacial hydrogen bonding, a well-retained crystalline PLA framework, and the uniform dispersion of fine TPU domains. Subsequently, a four-factor, three-level BBD was employed to investigate the effects of printing speed, nozzle temperature, raster angle, and layer height on the mechanical properties. The entropy weight method assigned objective weights of 0.4924, 0.0901, and 0.4805 to the three properties, yielding a comprehensive score as the evaluation index. Unlike conventional approaches that rely solely on software-recommended optima from RSM models, this study critically compares two decision routes: secondary RSM modeling followed by continuous optimization versus direct ranking of BBD experimental data. Direct ranking identified the optimal parameter set (90 mm/s, 200 °C, 0° raster angle, 0.2 mm layer height) with a comprehensive score of 0.9614, surpassing the RSM-recommended route (70 mm/s, 200 °C, 0° raster angle, 0.2 mm layer height; score 0.7366) by 30.5%. This comparison demonstrates that, in strongly nonlinear FDM processes, software-based continuous optimization may converge to mathematically conservative suboptimal regions, whereas direct ranking of discrete experimental data preserves full physical responses and captures higher-order synergistic effects that the RSM model fails to account for. ANOVA results further confirm that raster angle is the most influential factor affecting the comprehensive score. Overall, this work bridges material formulation and printing parameter optimization for PLA/TPU blends, offering a validated strategy for high-performance FDM manufacturing.