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

Abstract 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. © 2026 Society of Chemical Industry.