Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing

• Main Authors: Höfflin, D. (Author), Sauer, C. (Author), Schiffler, A. (Author), Schmitt, A.-M (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: 3d metals; 3D printing; additive manufacturing; Additive manufacturing process; Additives; in situ monitoring; In-situ monitoring; Metal additives; metal printing; Metal printings; neural network; Neural-networks; Powder bed; Powder metals; Quality assurance; Semantic image segmentations; semantic segmentation; Semantic segmentation; Semantic Segmentation; Semantic Web; Semantics; thermal distortion; Thermal distortions
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 14248220 (ISSN)
• DOI: 10.3390/s23094183

Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception–style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end. © 2023 by the authors.