A Fuzzy Decision-Making Framework for Optimizing Process Parameters in Laser Powder Directed Energy Deposition Additive Manufacturing

Abstract

This study presents a fuzzy multi-criteria framework for evaluating single-track quality in Laser Powder Directed Energy Deposition (LP-DED). The approach integrates three key geometric descriptors, including symmetry index, wetting angle, and dilution ratio, into a unified Triangular Fuzzy Number (TFN)-based TOPSIS model to capture the nonlinear relationships among process parameters and bead geometry. Twenty-seven single tracks were fabricated full factorial experiment design, varying laser power, scan speed, and powder feed rate. Optical microscopy measurements were used to quantify geometric metrics, and fuzzy normalization was applied to rank parameter sets by a closeness coefficient (C*) representing overall deposition quality. The fuzzy-based ranking effectively distinguished stable from unstable deposition regimes: high-ranked samples (C*>0.8) exhibited well-balanced bead geometry and moderate wetting angles, while low-ranked samples (C*<0.5) showed asymmetric melt pools and excessive dilution. Verification prints using the top-, mid-, and low-ranked parameter sets confirmed this trend, with roughness and porosity measurements. The insights gained from this work can guide adaptive process optimization, improve build reliability, and support the development of intelligent, data-driven control strategies for next-generation metal additive manufacturing systems.