Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 243, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2023.109852
Keywords
Fatigue-life prediction; Probabilistic neural network; Physical information; Additive manufacturing; Superalloy
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This study proposes a probabilistic neural network framework integrating physical information for predicting the fatigue life of additively manufactured components. The experimental results confirm that this framework can predict fatigue life more accurately and provide more reliable prediction performance.
Microstructural inhomogeneity in additively manufactured (AM) components leads to uncertainty in their fatigue performance. While purely data-driven methods can only provide deterministic outcomes and lack physical interpretability. Furthermore, considering the dispersion of fatigue life, a probabilistic neural network framework integrating physical information, namely a physics-informed probabilistic neural network (PIPNN), is proposed for predicting the fatigue life of AM parts. The framework describes the dispersion of fatigue life in the parametric form of probability statistics. It incorporates physical laws and models to constrain neurons and loss function, enabling the network to learn deeper physical laws that align with the fatigue process, thus enhancing the interpretability and prediction reliability of the model. Fatigue experiments were performed on Hastelloy X superalloy specimens fabricated using laser powder bed fusion, serving as the basis for validating and comparing the PIPNN model with a probabilistic neural network. The results indicate that PIPNN adeptly captures the heteroskedasticity of fatigue life and exhibits superior prediction accuracy and more reliable prediction performance in fatigue-life prediction. PIPNN offers a physically consistent method for fatigue-life prediction considering probabilistic statistics.
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