Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation

Increasing industry acceptance of powder bed metal Additive Manufacturing requires improved real-time detection and classification of anomalies. Many of these anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each lay...

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Bibliographic Details
Published in:Additive manufacturing Vol. 36; no. C; p. 101453
Main Authors: Scime, Luke, Siddel, Derek, Baird, Seth, Paquit, Vincent
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.12.2020
Elsevier
Subjects:
Additive manufacturing
Convolutional neural network
ENGINEERING
In-situ anomaly detection
Machine learning
Semantic segmentation
Additive manufacturing
Semantic segmentation
In-situ anomaly detection
Convolutional neural network
Machine learning
ISSN:2214-8604, 2214-7810
Online Access:Get full text
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Summary:Increasing industry acceptance of powder bed metal Additive Manufacturing requires improved real-time detection and classification of anomalies. Many of these anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each layer of the building process. In this work, the authors present a novel Convolutional Neural Network architecture for pixel-wise localization (semantic segmentation) of layer-wise powder bed imaging data. Key advantages of the algorithm include its ability to return segmentation results at the native resolution of the imaging sensor, seamlessly transfer learned knowledge between different Additive Manufacturing machines, and provide real-time performance. The algorithm is demonstrated on six different machines spanning three technologies: laser fusion, binder jetting, and electron beam fusion. Finally, the performance of the algorithm is shown to be superior to that of previous algorithms presented by the authors with respect to localization, accuracy, computation time, and generalizability.
Bibliography:USDOE Office of Nuclear Energy (NE)
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
AC05-00OR22725
ISSN:2214-8604
2214-7810
DOI:10.1016/j.addma.2020.101453