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Related Experiment Video

Updated: Jul 2, 2025

Fabrication and Design of Wood-Based High-Performance Composites

Fabrication and Design of Wood-Based High-Performance Composites

Published on: November 9, 2019

Multi-Attribute Decision Making: Parametric Optimization and Modeling of the FDM Manufacturing Process Using PLA/Wood

Alexandra Morvayová¹, Nicola Contuzzi¹, Laura Fabbiano¹

¹Dipartimento di Meccanica, Matematica e Management, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy.

Materials (Basel, Switzerland)

|February 24, 2024

Summary

Optimizing fused deposition modeling (FDM) for wood-filled polylactic acid (PLA) biocomposites requires advanced methods. A multiparametric approach using Grey Relational Analysis and Taguchi arrays achieved superior print quality and dimensional accuracy.

Keywords:

FDM Grey Relational Analysis PLA/wood Taguchi orthogonal array biocomposite optimization process

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Area of Science:

Materials Science
Additive Manufacturing
Polymer Engineering

Background:

Polylactic acid (PLA)-based filaments with natural fillers are gaining interest due to their eco-friendly attributes and properties.
Incorporating natural fillers into PLA significantly affects printability, leading to challenges in fused deposition modeling (FDM) biocomposites.
The complex interplay between processing, structure, and properties in FDM biocomposites remains poorly understood, impacting reliability and accuracy.

Purpose of the Study:

To identify optimal processing parameters for FDM manufacturing of wood-filled PLA biocomposites.
To enhance the dimensional accuracy and reduce defects in FDM-printed biocomposite samples.
To compare the effectiveness of multiparametric versus monoparametric optimization strategies.

Main Methods:

Utilized Grey Relational Analysis combined with the Taguchi orthogonal array for process optimization.
Investigated the impact of scanning speed, layer height, and printing temperature on FDM biocomposite properties.
Compared the integrated multiparametric optimization method with conventional monoparametric strategies.

Main Results:

Identified optimal parameters: 70 mm/s scanning speed, 0.1 mm layer height, and 220 °C printing temperature.
Achieved high dimensional accuracy (e.g., Dx = 20.115 mm) and low void content (1.673%).
The multiparametric optimization method demonstrated superior performance over monoparametric approaches.

Conclusions:

The Grey Relational Analysis and Taguchi method effectively optimize FDM parameters for wood-filled PLA biocomposites.
Optimal parameters significantly improve dimensional accuracy and reduce defects, enhancing biocomposite reliability.
Multiparametric optimization is crucial for achieving balanced improvements across multiple properties in FDM biocomposites.