|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||A Statistical-physical Framework for the Analysis of Uncertainties due to Material Parameters in Multi-physics Modelling
||Amanda Giam, Jiaxiang Cai, Fan Chen, Zhisheng Ye, Wentao Yan
|On-Site Speaker (Planned)
A bottleneck in Laser-Powder Bed Fusion (L-PBF) metal additive manufacturing (AM) is the quality inconsistency of products. To avoid costly experimentation, computational multi-physics modelling is being used to tackle this issue, but its’ effectiveness is limited by modelling parameter uncertainties. Therefore, a statistical-physical framework is utilized to characterise uncertainty in multi-physics models. Data is gleaned from a high-fidelity thermal-fluid model with a two-level full factorial design for five selected material parameters. Statistical techniques including the analysis of variance, scatter plots and linear regression are employed for sensitivity analyses of input factors on the response melt pool dimensions. To account for physics in the L-PBF process, crucial physical phenomena are thoroughly analysed. This complements statistical findings with domain knowledge, yielding a validated joint evaluation. Uncertainty propagation is investigated via graphical relations of input-output standard deviations. Consistent results from the sensitivity and uncertainty analyses can provide practical guidance for simulations and experiments.
||Additive Manufacturing, Computational Materials Science & Engineering, Other