# Multi-Objective Optimization of Mechanical and Geometric Properties of 3D-Printed PLA Porous Scaffolds for Biomedical Applications

## TL;DR

This paper introduces a framework to optimize 3D-printed scaffolds for biomedical use by balancing mechanical strength and geometric accuracy.

## Contribution

The novel framework uses multi-objective optimization and statistical modeling to balance mechanical and geometric properties of 3D-printed scaffolds.

## Key findings

- Quadratic models predicted scaffold properties with R² > 77% and most >90%.
- Multi-objective optimization revealed topology-specific essential objective pairs.
- Pareto fronts quantify trade-offs between mechanical performance and geometric fidelity.

## Abstract

Porous scaffolds fabricated via fused deposition modeling (FDM) are promising for bone tissue engineering, but their mechanical performance and geometric fidelity are governed by complex interactions between process parameters and architectural design. This study presents a multi-objective optimization framework for poly (lactic acid) (PLA) scaffolds based on three triply periodic minimal surface (TPMS) topologies—Gyroid, Primitive, and Diamond. A Box–Behnken design combined with response surface methodology was used to model compressive strength, elastic modulus, yield strength, energy absorption density, and discrepancies in volume and porosity as functions of layer thickness (0.05–0.15 mm), extrusion temperature (210–220 °C), and target porosity (50–70%). The resulting quadratic models exhibited strong predictive capability (R2 > 77%, with most >90%) and were validated experimentally at extreme parameter combinations, yielding relative errors below 10% for 83% of measurements. Multi-objective optimization using NSGA-II, coupled with principal component analysis and correlation-based objective reduction, revealed that the six original objectives collapse to topology-specific essential pairs: absorbed energy density and porosity discrepancy for Gyroid; Young’s modulus and volume discrepancy for Primitive; and Young’s modulus and porosity discrepancy for Diamond. The generated Pareto fronts quantify the inherent trade-off between mechanical performance and geometric fidelity for each topology, providing designers with explicit decision maps. This framework enables rational, application-driven selection of printing parameters and scaffold architecture, advancing the clinical translation of patient-specific FDM-printed bone scaffolds.