This study explores the shape-morphing behavior of 4D-printed structures made from Polylactic Acid (PLA), a prominent bio-sourced shape-memory polymer. Focusing on the response of these structures to thermal stimuli, this research investigates how various printing parameters influence their morphing capabilities. The experimental approach integrates design and slicing, printing using fused deposition modeling (FDM), and a post-printing activation phase in a controlled laboratory environment. This process aims to replicate the external stimuli that induce shape morphing, highlighting the dynamic potential of 4D printing. Utilizing Taguchi’s Design of Experiments (DoE), this study examines the effects of printing speed, layer height, layer width, nozzle temperature, bed temperature, and activation temperature on the morphing behavior. The analysis includes precise measurements of deformation parameters, providing a comprehensive understanding of the morphing process. Regression models demonstrate strong correlations with observed data, suggesting their effectiveness in predicting responses based on control parameters. Additionally, finite element analysis (FEA) modeling successfully predicts the performance of these structures, validating its application as a design tool in 4D printing. This research contributes to the understanding of 4D printing dynamics and offers insights for optimizing printing processes to harness the full potential of shape-morphing materials. It sets a foundation for future research, particularly in exploring the relationship between printing parameters and the functional capabilities of 4D-printed structures.

Dimensional accuracy in material-extrusion polymer additive manufacturing depends on feature geometry and process settings. This study evaluates a replicated, feature-resolved framework for polylactic acid (PLA) benchmark parts. A Taguchi L9 design varied layer height, print speed, and nozzle temperature across 45 independent builds, yielding 1575 measurements of 35 components across 16 features. Analyses combined build-level uncertainty, additive models, bootstrap intervals, exhaustive residual-permutation sensitivity, and clustered tolerance models. Inclined-surface roughness was summarized from five specimen means per condition. Mean absolute error (MAE) ranged from 0.0773 to 0.1263 mm. Layer height accounted for 91.8% of the decomposed condition-mean MAE variation (95% bootstrap interval: 90.0–93.2%). The additive-model effect had a Benjamini–Hochberg-adjusted p < 0.001; the nine-condition sensitivity analysis gave p = 0.093. Compliance odds ratios at ±0.10 mm were 0.51 and 0.32 for 0.2 and 0.3 mm layers relative to 0.1 mm. Nominally dominant factors persisted after dimension normalization. Dimensional utility, including slicer-estimated time, selected R2 (0.1 mm, 50 mm/s, 210 °C), while roughness-inclusive preferences made R1 competitive. Feature-specific tolerances and build-level uncertainty support process selection within the investigated PLA window.