Design, fabrication, and multi-objective optimization of 3D-printed PLA screws

Purpose In recent years, 3D printing technologies have grown significantly due to their versatility in producing components with complex geometries. In line with this trend, polylactic acid (PLA) has emerged as a highly applicable material due to its biocompatibility and biodegradability, along with adequate mechanical strength, making it suitable for the development of fastening and load-bearing components. However, the fabrication of such mechanical parts requires the consideration of multiple factors that ultimately influence both surface quality and mechanical properties of the final products. Design/methodology/approach In this study, a Design of Experiments (DOE) approach is implemented to vary the most critical parameters of the Fused Filament Fabrication (FFF) process, namely, nozzle diameter (ND), layer height (LH), extrusion temperature (TE), printing speed (PS) and extrusion multiplier (EM). Standardized specimens were used to measure the response variables. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed to predict surface roughness, density and tensile strength, consistently outperforming regression models across all error metrics. A multi-objective optimization (MOO) based on particle swarm optimization was then applied to determine optimal processing conditions for PLA screw prototypes, which were subsequently manufactured (single- and double-thread) and validated through tensile and pull-out tests. Findings From the results, when all response variables had the same weight in the trade-off, the optimization indicated that the optimal parameter values within the selected range were ND = 0.25 mm, TE = 210 ºC, EM = 90%, LH = 0.1 mm and PS = 30 mm/s, achieving in the experimental validation a surface roughness (Ra) of 6.38 µm, a density of 1.2277 g/cm³ and an ultimate tensile strength of 63.20 MPa. Originality/value By combining ANFIS modeling with particle swarm-based MOO, this study provides a systematic strategy to identify optimal FFF processing parameters for PLA components. The integrated approach of modeling, optimization and experimental validation and experimental validation on PLA screw prototypes establishes a robust pathway for designing high-performance screws outperforming regression models. These findings underline the critical role of parameter tuning, where the optimization allows for trade-offs between mechanical performance and surface quality, which is very important in balancing strength, density and surface roughness, particularly in applications where multi-criteria optimization is essential.