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Updated: Jul 30, 2025

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

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Published on: September 1, 2023

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

Anna-Maria Schmitt¹, Christian Sauer¹, Dennis Höfflin¹

¹Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This study introduces a novel method for monitoring metal additive manufacturing (AM) quality by comparing actual and target layers using neural network image segmentation. The approach effectively detects part damage and thermal distortions during the AM process.

Keywords:

additive manufacturing in situ monitoring metal printing neural network semantic segmentation thermal distortion

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Hybrid Printing for the Fabrication of Smart Sensors

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Published on: January 31, 2019

Area of Science:

Additive Manufacturing
Quality Assurance
Machine Learning

Background:

Metal Additive Manufacturing (AM) requires robust quality assurance methods.
Monitoring process deviations is crucial for ensuring part integrity.

Purpose of the Study:

To develop and evaluate a method for real-time monitoring of metal AM processes.
To detect defects such as part breakage and thermal deformations.

Main Methods:

Utilized Xception-style neural networks for powder bed image segmentation.
Compared segmented actual layers with reference target layers.
Analyzed metrics including area, centroids, and normalized area difference.

Main Results:

The method successfully identified a broken-off part and a part with thermal deformations in a test print job.
Calculated metrics proved effective in detecting damage and distortions.
The approach offers valuable insights into process quality.

Conclusions:

The presented method provides a viable approach for monitoring metal AM quality assurance.
Further improvements can address limitations related to camera resolution and lighting conditions.