Automating Anomaly Detection in Additive Manufacturing with the dFL​ скачать в хорошем качестве

Automating Anomaly Detection in Additive Manufacturing with the dFL​

Step inside an additive manufacturing workflow where high-frequency sensor data actually turns into decisions. In this case study, we use dFL by Sophelio on a NIST Laser Powder Bed Fusion dataset to fuse machine commands, real melt-pool signals, and XCT scans into a single, engineer-ready workspace. dFL’s custom visualizations surface process drifts layer-by-layer, then Python-based autolabelers transform those insights into an automated anomaly detector that flags power deviations across all 250 layers in minutes. The result: a repeatable path from raw AM telemetry to targeted quality assurance and faster process optimization—without staring at every graph. Learn more about dFL by visiting https://dfl.sophelio.io