This research investigated and optimized the parameters of the FDM process by employing bio-inspired algorithms for determining the optimal parameter settings in terms of surface quality and mechanical performance. Four important process parameters including layer thickness (0.11–0.33$$mm), part orientation (0–90^∘), raster width (0.2–0.56$$mm), and the raster angle (0–60^∘) at three variation levels were selected for fabricating the specimens (ABS material P430) using the statistical Box–Behnken design. ANOVA analysis and multiple regression analysis were used to fit the experimental data to a second-order polynomial equation. Through, the RSM analysis, the layer thickness is the key important factor that accounts for all of the responses. The fracture behavior of specimens was examined using a scanning electron microscope (SEM). From the SEM analysis, a substantial amount of plastic deformation on the fracture surface indicative of craze cracking is visible from a 0^∘ orientation, indicating a totally ductile fracture mechanism. Then, three swarm intelligence algorithms such as Tasmanian Devil Optimization (TDO), Remora Optimization Algorithm (ROA), Tuna Swarm Optimization (TSO) were implemented to optimize the input parameters that would lead to minimum surface roughness and maximum tensile strength. Experimental data and predicted values varied between 1.64% and 1.84%, as shown by verification experiments.

这项研究通过采用仿生算法来研究和优化 FDM 工艺的参数，以确定表面质量和机械性能方面的最佳参数设置。四个重要的工艺参数，包括层厚度 (0.11–0.33$$mm)、零件方向 (0–90 ^∘ )、光栅宽度 (0.2–0.56$$mm) 和三个变化水平的光栅角度 (0–60 ^∘ ) 被选择用于使用统计 Box-Behnken 设计制造样本（ABS 材料 P430）。使用方差分析和多元回归分析将实验数据拟合到二阶多项式方程。通过 RSM 分析，层厚度是解释所有响应的关键重要因素。使用扫描电子显微镜（SEM）检查样品的断裂行为。根据 SEM 分析，从 0 ^∘方向可以看到断裂表面上存在大量塑性变形，表明存在银纹裂纹，这表明完全是延性断裂机制。然后，实施塔斯马尼亚恶魔优化（TDO）、雷莫拉优化算法（ROA）、金枪鱼群优化（TSO）等三种群体智能算法来优化输入参数，从而获得最小的表面粗糙度和最大的拉伸强度。验证实验表明，实验数据与预测值的偏差在1.64%和1.84%之间。