Gaussian Process-Based Synthetic Data Generation and Multi-Level Validation for Tensile Strength Modeling in FDM Additive Manufacturing

Purpose: This study addresses the small data challenge in additive manufacturing, where destructive tensile testing limits sample size (n = 81), by developing and validating a probabilistic synthetic data generation pipeline. Design, Method, and Approach: A twelve-stage framework couples Gaussian Process Regression (GPR) with three-tier validation. Infill patterns are one-hot encoded, process parameters are sampled uniformly within physical bounds, and synthetic ultimate tensile strength (UTS) is generated with uncertainty-guided noise (γ = 0.3). Fidelity is assessed through distributional, structural, and Train–Synthetic Test–Real (TSTR) tests using XGBoost, Random Forest, and GPR. Results: GPR expanded the dataset 617-fold to 50,000 synthetic observations (1000 used for validation), preserving the dominant infill–UTS dependence, while agreement for other features varied. Models trained on synthetic data reached a physical test R2 of 0.8895 (XGBoost) and 0.9185 (Random Forest), 3.61 and 1.02 percentage points below the experimental baselines (0.9256 and 0.9287); GPR reached 0.9425. Originality: Combining one-hot encoding, bounded sampling, and multi-tier validation, this work shows that probabilistic synthetic data can complement physical specimens when training machine learning surrogates.