Dual-Response Optimization of Fused Filament Fabrication for Controlling Crystallinity and Tensile Strength in PLA Scaffolds
Published 2025-12-18
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Keywords
- Fused Filament Fabrication,
- dual response optimization,
- crystallinity variance,
- process robustness
How to Cite
Copyright (c) 2025 Advanced Technologies and Materials

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Fused filament fabrication (FFF) is widely applied in scaffold manufacturing due to its ability to produce porous polymer structures with controlled architecture and reproducible process conditions. In biomedical applications, optimization of mechanical performance must be accompanied by reliable regulation of polymer crystallinity, as excessive crystalline content can adversely affect degradation behaviour and biological response. While previous studies have demonstrated that in-process crystallinity can be manipulated through adjustment of FFF processing parameters, limited attention has been given to crystallinity variance and its implications for process robustness.
In this study, a statistically designed experiment was employed to examine the combined influence of extrusion speed, extrusion temperature, layer thickness, and build plate temperature on tensile strength and in-process crystallinity of polylactic acid specimens fabricated by FFF. A varianceaware dual-response optimization framework was implemented to identify parameter combinations that maximize tensile strength while achieving low and stable crystallinity. In addition to mean crystallinity values, crystallinity variance was explicitly analysed to enhance process reproducibility. The results indicate that extrusion speed is the dominant contributor to crystallinity variance, while appropriate parameter selection enables simultaneous attainment of high tensile strength and controlled crystallinity within a narrow and reproducible range. Experimental verification confirmed the predictive capability of the developed models. These findings demonstrate that incorporating variance minimization into multi-objective optimization provides a robust approach to process control, supporting the fabrication of polymer scaffolds suitable for biomedical applications.
