Influence of 3D Printing Parameters on ULTEM 9085 Mechanical Properties Using Explainable Machine Learning

ABSTRACT Fused filament fabrication (FFF) printed polyetherimide (PEI), commercially available as ULTEM 9085, is qualified by the Federal Aviation Administration for aerospace structural applications. Its mechanical performance depends strongly on printing parameters, including build orientation, number of contours, raster angle, and air gap, whose nonlinear interactions govern void formation, interlayer bonding, and ultimate strength. Prior work using artificial neural networks (ANNs) predicted ULTEM 9085 properties with under 5% error from a single data split, leaving two questions open: whether that accuracy holds across repeated splits, and whether the model's reasoning can be physically interpreted. This study addresses both gaps. Eight regression algorithms (Ridge, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, XGBoost, K‐Nearest Neighbors, and Support Vector Regression) are benchmarked to jointly predict complete stress–strain curves and three scalar mechanical properties (tensile strength, elastic modulus, yield strength) from the four printing parameters, evaluated across five random data splits. SHAP and LIME are then applied to the best‐performing model to attribute its predictions to individual printing parameters. Extra Trees Regressor achieves the lowest and most stable test mean absolute error (MAE) of 0.162 ± 0.041 in scaled space across five seeds, with scalar property errors of 4.0% (tensile strength), 4.4% (elastic modulus), and 8.5% (yield strength), accuracy comparable to the ANN baseline at a fraction of the computational cost. SHAP and LIME attributions agree that the number of contours and build orientation are the dominant drivers of tensile strength, with air gap acting as a smaller, suppressive factor. These results support Extra Trees with SHAP/LIME explanation as a repeatable, interpretable, and computationally inexpensive surrogate for reducing the physical testing burden in aerospace qualification of 3D‐printed ULTEM 9085.