Layer-wise spatial modeling of porosity in additive manufacturing

Jia (Peter) Liu, Chenang Liu, Yun Bai, Prahalada Rao, Christopher B. Williams and Zhenyu (James) Kong

IISE Transactions, 2019, vol. 51, issue 2, 109-123

Abstract: The objective of this work is to model and quantify the layer-wise spatial evolution of porosity in parts made using Additive Manufacturing (AM) processes. This is an important research area because porosity has a direct impact on the functional integrity of AM parts such as their fatigue life and strength. To realize this objective, an Augmented Layer-wise Spatial log Gaussian Cox process (ALS-LGCP) model is proposed. The ALS-LGCP approach quantifies the spatial distribution of pores within each layer of the AM part and tracks their sequential evolution across layers. Capturing the layer-wise spatial behavior of porosity leads to a deeper understanding of where (at what location), when (at which layer), and to what severity (size and number) pores are formed. This work therefore provides a mathematical framework for identifying specific pore-prone areas in an AM part, and tracking the evolution of porosity in AM parts in a layer-wise manner. This knowledge is essential for initiating remedial corrective actions to avoid porosity in future parts, e.g., by changing the process parameters or part design. The ALS-LGCP approach proposed herein is a significant improvement over the current scalar metric used to quantify porosity, namely, the percentage porosity relative to the bulk part volume. In this article, the ALS-LGCP approach is tested for metal parts made using a binder jetting AM process to model the layer-wise spatial behavior of porosity. Based on offline, non-destructive X-Ray computed tomography (XCT) scan data of the part the approach identifies those areas with high risk of porosity with statistical fidelity approaching 85% (F-score). While the proposed work uses offline XCT data, it takes the critical first-step from a data analytics perspective for taking advantage of the recently reported breakthroughs in online, in-situ X-Ray-based monitoring of AM processes. Further, the ALS-LGCP approach is readily extensible for porosity analysis in other AM processes; our future forays will focus on improving the computational tractability of the approach for online monitoring.

Date: 2019
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DOI: 10.1080/24725854.2018.1478169

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