Uncertainty quantification and reduction in metal additive manufacturing

Zhuo Wang,Zhen Hu,Pengwei Liu,Lei Chen,Chen Jiang,Wenhua Yang,Ying Zhao,Long-Qing Chen,Mark F Horstemeyer

doi:10.1038/s41524-020-00444-x

Abstract

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 physics-based quality control of AM products.

Highlights

Additive manufacturing (AM) can offer tremendous time and cost advantages over traditional manufacturing processes especially in fabricating customized components with complex geometry[1,2].the complicated layer-by-layer manufacturing process makes as-fabricated AM components prone to great variability in both quality and properties[3]
We propose a sequential calibration and validation (SeCAV) method[23] with model bias correction to reduce the uncertainty of the data-driven melt pool model
The AM-Bench project led by National Institute of Standards and Technology (NIST) is exclusively devoted to developing a continuing series of highly controlled AM benchmark tests

Summary

INTRODUCTION

Additive manufacturing (AM) can offer tremendous time and cost advantages over traditional manufacturing processes especially in fabricating customized components with complex geometry[1,2]. Model-based AM UQ as shown in Fig. 1 is a much more costeffective approach than experimental-based UQ, which completely relies on repetitive experiments while metal-based AM experiments are notoriously expensive In this context, a datadriven surrogate model[13,20] based on computational data is strongly preferred (see Fig. 1a), to provide instantaneous knowledge about a QoI, the melt pool in this case, under any AM condition (see Fig. 1b). For a surrogate model derived from physics-based simulations, it requires extensive simulations at sufficiently high resolution to provide reliable training data. This can be a challenge for carrying out computationally intensive three-dimensional simulations and post-processing large-scale high-fidelity data. Fig. 1f), to facilitate the UQ analysis of practical product properties

RESULTS

Method

DISCUSSION

METHODS