AI-assisted, curvature-aware post-slicing G-code optimization for material-efficient FDM printing
Year: 2026
Type: article
Abstract: Fused Deposition Modeling (FDM) slicers typically apply uniform process parameters across entire parts, leading to inefficient material usage and limited adaptation to local geometric or functional requirements. This work presents a post-slicing G-code optimization framework that operates directly at the instruction level without modifying the original CAD model or re-running the slicer. The framework integrates three modules: (1) curvature-aware adaptive layer height control, (2) function-specific reinforcement for load-bearing regions, and (3) AI-based weak-zone detection using unsupervised clustering. Physically correct extrusion is enforced through incremental ΔE handling in relative mode (M83). The method is validated through three case studies: a quadcopter frame, an ASTM D638 tensile test specimen, and a complex mounting bracket, all printed using a Creality Ender-3 printer (0.4 mm nozzle, Nylon filament) with baseline G-code generated by Ultimaker Cura 5.0. Compared to the original toolpaths, the optimized G-code preserves geometric fidelity (RMS deviation < 0.05 mm) while reducing effective material extrusion by approximately 22–25% across the three geometries. Print time increased by 7–9% due to feedrate smoothing and localized reinforcement. These results suggest that instruction-level, post-slicing optimization can improve material efficiency for functional FDM components across diverse geometries, though further validation across additional printers, materials, and part complexities is needed to establish full generalizability.
Author: Laith Al-Juboori
Institution: Higher Colleges of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7160875153