Definitive screening design coupled with entropy‐weighted grey relational analysis for optimizing wear and strength of FDM PLA

Poly(lactic acid) (PLA) is commonly employed in fused deposition modeling (FDM) because of its biodegradability, ease of processing and low cost; however, its structural applications are hindered by moderate mechanical strength and poor wear resistance. This research presents an integrated experimental and multi‐objective optimization framework for tribo‐mechanical improvement of FDM‐printed PLA. A definitive screening design (DSD) was used to examine the effects of layer thickness (0.2–0.4 mm), nozzle temperature (210–230 °C) and infill density (50–100%) over 39 experimental runs, thus facilitating the efficient estimation of main, quadratic and interaction effects. The developed regression models showed strong predictive accuracy, with coefficients of determination reaching 98.85% for flexural strength. Results revealed that infill density and thermal conditions exert a dominant influence on both mechanical and tribological responses. To resolve trade‐offs among Shore D hardness, flexural strength and wear behavior, grey relational analysis (GRA) combined with Shannon entropy weighting was applied. The optimal parameter set – 0.2 mm layer thickness, 210 °C nozzle temperature and 50% infill density – yielded the highest grey relational grade, indicating balanced improvement in tribo‐mechanical performance. The proposed DSD–entropy–GRA framework offers a robust and transferable approach for simultaneous optimization of mechanical and tribological properties of FDM‐fabricated PLA components.