Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning

Quality control in metal additive manufacturing prioritizes the development of advanced inspection schemes to characterize the defect evolution during processing and post-processing. This involves grand challenges in detecting internal defects and analyzing large and complex defect datasets in macroscopic samples. Here, we present an inspection pipeline that integrates (i) fast, micro X-ray computed tomography reconstruction, (ii) automated 3D morphology analysis, and (iii) machine learning-based big data analysis. X-ray computed tomography and automated computer vision result in a holistic defect morphology database for the inspected macroscopic volume, based on which machine learning analysis is employed to reveal quantitative insights into the global evolution of defect characteristics beyond qualitative human observations. We demonstrate this pipeline by examining the global-scale pore evolution in post-processing of binder jetting additive manufacturing, from the green state, to the sintered state, and to the hot isostatic pressed state of copper. The pipeline is shown to be effective at detecting and processing the information associated with a large number (similar to 10(5)) of pores in macroscopic volumes. The subsequent principal component analysis and clustering analysis extract the key morphological descriptors and categorize the detected pores into four morphological groups. By quantifying the evolution of (i) the weight of pore morphology parameters and (ii) the pore number and volume fraction of each categorized group, new understandings are developed regarding the effects of sintering and hot isostatic pressing on pore decomposition, shrinkage, and smoothing during post-processing of binder jetting.