TY - JOUR
T1 - Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning
AU - Chen, Jie
AU - Liu, Yongming
N1 - Funding Information:
The research is supported by fund from NAVAIR through Technical Data Analysis, Inc. (Contract # N68335-20-C-0477 , Program Officer at NAVAIR: Jan Kasprzak and Nam Pham, Program Manager at TDA: Dr. Anahita Imanian). The support is greatly appreciated.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/3
Y1 - 2021/3
N2 - The probabilistic fatigue properties of additively manufactured (AM) Ti-6Al-4V using selective laser melted (SLM) process is analyzed considering the effects of process parameters. The Probabilistic Physics-guided Neural Network (PPgNN) is proposed for the modeling. With this developed model, both mean and variance of the fatigue life can be learned. The PPgNN contains constraints on model parameters to obtain the probabilistic stress-life relationships (P-S-N curves) with the physics-consistent curvature and nonconstant variance. The PPgNN model is also able to be trained using the data set with missing data for more reliable predictions. Experimental fatigue data are collected from extensive literatures for AM Ti-6Al-4V in as-built and annealed condition subjected to various process parameters (scanning speed, laser power, hatch space, layer thickness, heat temperature, heat time). The PPgNN model is validated using the experimental data. Next, a group of models with the same architecture and training data but different initial neural network biases and weights are obtained to account for the the randomness of NN. Following this, the predictive performance is compared between models training using all data (both complete and incomplete) and only complete data. Finally, global sensitivity analysis using the Delta Moment-Independent Measure is conducted to investigate the importance of process parameters. By only varying one process parameter and keeping the remaining ones fixed, the effect of each parameter on probabilistic fatigue lives is studied.
AB - The probabilistic fatigue properties of additively manufactured (AM) Ti-6Al-4V using selective laser melted (SLM) process is analyzed considering the effects of process parameters. The Probabilistic Physics-guided Neural Network (PPgNN) is proposed for the modeling. With this developed model, both mean and variance of the fatigue life can be learned. The PPgNN contains constraints on model parameters to obtain the probabilistic stress-life relationships (P-S-N curves) with the physics-consistent curvature and nonconstant variance. The PPgNN model is also able to be trained using the data set with missing data for more reliable predictions. Experimental fatigue data are collected from extensive literatures for AM Ti-6Al-4V in as-built and annealed condition subjected to various process parameters (scanning speed, laser power, hatch space, layer thickness, heat temperature, heat time). The PPgNN model is validated using the experimental data. Next, a group of models with the same architecture and training data but different initial neural network biases and weights are obtained to account for the the randomness of NN. Following this, the predictive performance is compared between models training using all data (both complete and incomplete) and only complete data. Finally, global sensitivity analysis using the Delta Moment-Independent Measure is conducted to investigate the importance of process parameters. By only varying one process parameter and keeping the remaining ones fixed, the effect of each parameter on probabilistic fatigue lives is studied.
KW - Additive manufacturing
KW - Fatigue
KW - Physics-guided learning
KW - Selective laser melting
KW - Uncertainty quantification
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U2 - 10.1016/j.addma.2021.101876
DO - 10.1016/j.addma.2021.101876
M3 - Article
AN - SCOPUS:85100384560
SN - 2214-8604
VL - 39
JO - Additive Manufacturing
JF - Additive Manufacturing
M1 - 101876
ER -