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Automated Powder Bed Analysis Uses Smart Defect Detection Software

Isar Aerospace has recently integrated the nebumind software into their 3D printing process (LPBF – Laser Powder Bed Fusion) to automate the identification and reporting of defects during the application of a new powder layer, saving time and costs in manufacturing. In the past, engineers had to evaluate the powder bed images in a laborious manual process to find possible anomalies and assess their impact on component quality. Now the nebumind software detects defects through automated analysis of the powder bed images, providing the engineer with important information for evaluating the quality of the printing process.

As an emerging company in the space industry sector, Isar Aerospace relies on cutting-edge manufacturing techniques, such as laser-based powder bed fusion (L-PBF), to manufacture complex engine components. One of the critical steps in 3D printing is the powder recoating conducted after each printed layer, as it is prone to error: If the surface to be exposed is not coated evenly and completely, this can lead to defects in the component.

Manual Analysis Time-Consuming, Error-Prone and Retrospectively

Nowadays, sensors and cameras integrated in the L-PBF systems monitor the coating step, that are manually analyzed for uncoated areas, damage to the recoater or other defects by engineers responsible for the process. This approach is extremely time-consuming: for instance, if a 3D printed metal component generates 2,500 recoater images and an engineer takes 3 seconds to analyse each image, they spend more than 2 hours on this task for one build job alone. Moreover, as the analysis is conducted in a post-processing step, defects are usually only identified after a component has been printed.

Automated Image Analysis Presents In-Process Defects As Digital Twin

To prevent this manual process, Isar Aerospace has connected the nebumind software into their machine environment automating the defect detection for each recoating layer. The software receives powder bed images before and after recoating, generated by an optical camera with a resolution of 1280 x 1024 pixels, and extracts them automatically via the direct machine interface. The software uses an algorithm to spot defects in the images automatically. For its algorithm, nebumind has employed primarily stable algorithms from image and edge detection. In addition, the positions of the defects are analyzed across multiple layers to minimize the false positive rate.

nebumind recoater images with defects and 3d visualisation

By adopting the nebumind software, Isar Aerospace realizes a time saving of up to 80% compared to the previous manual analysis, as engineers can focus on the images labelled by the algorithm.

In-Situ Process Analysis Accelerates Process Development Eliminating Defective Components

In the near future, nebumind’s defect recognition will be moved upstream from post- printing to in-process, so that the software will be able to point out possible anomalies immediately after coating, thereby enabling the user to stop the printing process in the event of an irreparable defect.

To further automate the process, it is planned to correlate the found recoater defects with other process data from the print job and CT data from the final quality check to identify root causes and prevent recoater defects from happening in the first place. In the long term, nebumind not only wants to monitor the printing process with its software, but also to intervene in the process in a regulating manner, for example to stop the printing process in the event of irreparable errors or to compensate for an incorrect coating in the subsequent layer.

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