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

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.