Multi-Objective Optimization of Low-Velocity Impact and Compression Behavior of 3D-Printed PLA Cubic Samples.

This study investigates how various 3D printing parameters influence mechanical properties, specifically strength in compression and low-velocity impact (LVI) tests, and identifies the best printing parameters (layer thickness, nozzle diameter, and infill density) that lead to durable samples. Utilizing a Taguchi L(9) orthogonal array, the study systematically examined the effects of three critical 3D printing parameters on the mechanical strength of cubic test samples. Nine experimental configurations were tested, each subjected to compression and LVI tests according to ASTM standards. Statistical analyses, including analysis of variance (ANOVA) and grey relational analysis (GRA), were employed to evaluate parameter significance and optimize results. Infill density significantly influenced the compression tests, while nozzle diameter was the most impactful parameter in LVI tests. Layer thickness had a minimal influence on both outcomes. Additionally, applying GRA revealed that optimal 3D printing parameters differ when considering the two mechanical properties simultaneously, highlighting the complexity of achieving balanced performance in 3D-printed structures. The application of the Taguchi method to optimize 3D printing parameters improved the mechanical properties of printed materials while significantly reducing the number of required experiments. By employing an efficient experimental design, this research demonstrates how to achieve high-quality results in compression and LVI tests with minimal resource use and time investment. Additionally, integrating GRA for the simultaneous optimization of multiple performance characteristics further enhances the practical applicability of the findings in additive manufacturing.