Layer-wise spatial modeling of porosity in additive manufacturing

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.