Iterative Uncertainty Calibration for Modeling Metal Additive Manufacturing Processes Using Statistical Moment-Based Metric

Abstract

Metal additive manufacturing (AM) has recently attracted attention due to its potential for batch/mass production of metal parts. This process, however, currently suffers from problems including low productivity, inconsistency in the properties of the printed parts, and defects such as lack of fusion, keyholing, and un-melted powders. Finite Element (FE) modeling cannot accurately model the metal AM process and has a high computational cost. Empirical models based on experiments are time-consuming and expensive. This paper improves a previously developed framework that takes advantages of both empirical and FE models. The validity and accuracy of the metamodel developed in the previous framework depend on the initial assumption of parameter uncertainties. This causes a problem when the assumed uncertainties are far from the actual values. The proposed framework introduces an iterative calibration process to overcome this limitation. In addition, the u_pooling metric used as the calibration metric in the previous framework is found not as good as the second-order statistical moment-based metric (SMM), after comparing several calibration metrics. The proposed framework is then applied to a four-variable porosity modeling problem. The obtained model is more accurate than using other approaches with only 10 available experimental data points for calibration and validation.

Modeling and optimization of height-related geometrical parameters for thin wall structures manufactured by metal additive manufacturing

“…The future would be to apply the calibration and validation framework defined in Ref. [34] to validate the AAS model and also calibrate the uncertainties in the process. Moreover, another future work is to model and optimize the width and height of the wall simultaneously.…”

“…One way to validate the AAS model is to train the model with more experiments and applying the calibration framework defined in Ref. [34] where a bias between the predicted model and experimental values are defined and the bias is calibrated in a loop. Although the later model is not valid, the aforementioned relationships between process parameters and AAS still exist.…”