Uncertainty quantification(UQ)in metal additive manufacturing(AM)has attracted tremendous interest in order to dramatically improve product reliability.Model-based UQ,which relies on the validity of a computational mo...Uncertainty quantification(UQ)in metal additive manufacturing(AM)has attracted tremendous interest in order to dramatically improve product reliability.Model-based UQ,which relies on the validity of a computational model,has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments.However,its adoption in the practical AM process requires overcoming two main challenges:(1)the inaccurate knowledge of uncertainty sources and(2)the intrinsic uncertainty associated with the computational model.Here,we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology(NIST).We first construct a surrogate model,based on high throughput physical simulations,for predicting the three-dimensional(3D)melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources.We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model.The application of the calibrated melt pool model to UQ of the porosity level,an important quality factor,of AM parts,demonstrates its potential use in AM quality control.The proposed UQ framework can be generally applicable to different AM processes,representing a significant advance toward physicsbased quality control of AM products.展开更多
基金The research is financially supported by a startup fund from the Department of Mechanical Engineering at the University of Michigan-Dearborn,Propelling Original Data Science(PODS)Grants from Michigan Institute of Data Science(MIDAS)National Science Foundation(NSF)Grant CMMI-1662864.
文摘Uncertainty quantification(UQ)in metal additive manufacturing(AM)has attracted tremendous interest in order to dramatically improve product reliability.Model-based UQ,which relies on the validity of a computational model,has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments.However,its adoption in the practical AM process requires overcoming two main challenges:(1)the inaccurate knowledge of uncertainty sources and(2)the intrinsic uncertainty associated with the computational model.Here,we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology(NIST).We first construct a surrogate model,based on high throughput physical simulations,for predicting the three-dimensional(3D)melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources.We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model.The application of the calibrated melt pool model to UQ of the porosity level,an important quality factor,of AM parts,demonstrates its potential use in AM quality control.The proposed UQ framework can be generally applicable to different AM processes,representing a significant advance toward physicsbased quality control of AM products.
