Knowledge-infused probabilistic modelling for decision support in metal additive manufacturing

• Hybrid Bayesian framework improves fatigue predictions by 28-42% over conventional methods • Physics-based yield strength correction accounts for AM process variability • Quantifies both material (aleatoric) and model (epistemic) uncertainty sources • Validated across four major AM alloys with 10,501 experimental data points This study presents a probabilistic framework for fatigue life prediction in additively manufactured (AM) metals by integrating Bayesian neural networks with a novel physics-informed yield strength correction. The framework analyses 10,501 fatigue data points across Ti-6Al-4V, AlSi10Mg, 316L, and IN718 alloys under varying loading conditions (from R = 0.0 to R = − 1 ), addressing critical limitations of conventional deterministic methods. The yield strength correction accounts for process-induced material variations, while the Bayesian approach quantifies both aleatoric (material variability) and epistemic (model uncertainty) uncertainty components. Results demonstrate significant improvements over conventional predictions, with fatigue strength corrections up to 7 × at 2 million cycles. The hybrid methodology reduces prediction errors by 28-42% while providing statistically rigorous confidence bounds that capture 92% of experimental data within 95% prediction intervals. This integrated approach enables more reliable AM component design through its combination of physical interpretability and comprehensive uncertainty quantification, offering particular value for safety-critical applications where traditional methods prove overly conservative.