查看文章

In this work, process monitoring data, including layerwise imagery, multi-spectral emissions, and laser scan vector data, were collected during laser-based powder bed fusion additive manufacturing and correlated to fatigue performance. All parts were X-ray CT scanned post-build, and internal flaws were identified via an automated defect recognition software. Convolutional neural networks were trained to discriminate flaws from nominal build conditions using in situ data modalities only. Trained classifiers were then tested against a previously unseen data set collected from an independent build, and classification performance and metrics for information content provided by each individual modality were formally established. Correlations were drawn between the detected flaw populations and the corresponding fatigue properties, demonstrating that fatigue critical lack-of-fusion flaws can be detected via machine …