Additive Manufacturing

Follow this topic Ask about this topic

Momentum

6 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 35

All topics

Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection
Apr 27, 2026Stefano Raimondo, Matteo Bugatti, Marco GrassoGraph Neural Networks U-Net
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy · Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy
IoT-Enhanced CNN-Based Labelled Crack Detection for Additive Manufacturing Image Annotation in Industry 4.0
Apr 22, 2026Mohsen Asghari Ilani, Yaser Mike BanadEdge Inference Industrial Inspection
School of Management, Swansea University, Swansea, United Kingdom · School of Electrical and Computer Engineering, University of Oklahoma, Norman, 73019, U.S.A.
Muscle-inspired magnetic actuators that push, pull, crawl, and grasp
Apr 20, 2026Muhammad Bilal Khan, Florian Hofmann, Kilian Schäfer +2Robotic Grasping Additive Manufacturing
1Functional Materials, Institute of Materials Science, Technical University of Darmstadt, 64287 Darmstadt, Germany · 2Additive Manufacturing Center, Technical University of Darmstadt, 64289 Darmstadt, Germany
Material-Agnostic Zero-Shot Thermal Inference for Metal Additive Manufacturing via a Parametric PINN Framework
Apr 16, 2026Hyeonsu Lee, Jihoon JeongNeural Surrogate Modeling Zero-Shot Learning
Wm Michael Barnes ’64 Department of Industrial and Systems Engineering Texas A&M University College Station, TX 77843, USA
Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection
Mar 23, 2026Ekta U. Samani, Christopher G. AtkesonManufacturing Large Language Model-Guided Optimization
Robotics Institute at Carnegie Mellon University

Apr 27, 2026, cs.CVJ/K move · Enter open · S save

Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection

Stefano Raimondo, Matteo Bugatti, Marco Grasso

Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy · Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy

Graph Neural Networks U-Net Industrial Inspection Convolutional Neural Networks Additive Manufacturing

Open paper AskPDF arXiv

The technological maturity of in situ inspection and monitoring methods in additive manufacturing is steadily increasing, enabling more efficient and practical qualification procedures. In this context, image segmentation of powder bed images in Laser Powder Bed Fusion (L-PBF) has been investigated by various authors, leveraging both edge detection and machine learning approaches to identify deviations from nominal geometry. Despite these developments, several challenges remain, including the sensitivity of segmentation performance to industrial illumination conditions and layer-to-layer variability in pixel intensity patterns. The study addresses these limitations by proposing a graph-augmented segmentation approach. The underlying principle consists of preserving the geometrical information at a global level rather than at pixel-wise level, modeling dependencies and relational information among spatial regions with a Graph Neural Network bottleneck embedded into a U-Net architecture. This allows enhancing the consistency and accuracy of the geometry reconstruction in the presence of spatial and layer-wise photometric variability systematically faced in real data. The method is evaluated against benchmark techniques for the in situ reconstruction of lattice structures produced by L-PBF, demonstrating its potential as a scalable solution for robust in situ inspection and geometric verification in industrial environments.

Momentum

Related topics

Latest papers 35

Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection

IoT-Enhanced CNN-Based Labelled Crack Detection for Additive Manufacturing Image Annotation in Industry 4.0

Muscle-inspired magnetic actuators that push, pull, crawl, and grasp

Material-Agnostic Zero-Shot Thermal Inference for Metal Additive Manufacturing via a Parametric PINN Framework

Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection