Multi-Objective Optimization of Fatigue Performance in FDM-Printed PLA Biopolymer Using Grey Relational Method

This study focuses on improving the fatigue strength and overall performance of sustainable biopolymer polylactic acid (PLA) components manufactured via Fused Deposition Modelling (FDM) additive manufacturing process. PLA, as a biodegradable and renewable polymer derived from natural resources, represents a promising alternative to conventional petroleum-based plastics in engineering and research applications. The influence of key FDM process parameters—layer height, infill density, and number of perimeters—on critical performance indicators such as filament consumption, printing time, and fatigue strength (number of cycles to failure) was systematically analyzed using the Taguchi L9 orthogonal array. Subsequently, Grey Relational Analysis (GRA) was applied as a multi-objective optimization technique to identify the parameter settings that achieve an optimal balance between mechanical durability and resource efficiency. The obtained results demonstrate that a proper combination of process parameters can significantly enhance the mechanical reliability and sustainability profile of FDM-printed PLA parts, contributing to the broader adoption of eco-friendly materials in additive manufacturing.