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Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images

  • Masoumeh Aminzadeh
  • Thomas R. Kurfess
Article

Abstract

Despite their advances and numerous benefits, metal powder-bed additive manufacturing (AM) processes still suffer from the high chances of defect formation and a need for improved quality. This work develops an online monitoring system for quality of fusion and defect formation in every layer of the laser powder-bed fusion process using computer vision and Bayesian inference. An imaging setup is developed that for the first time allows capturing in-situ (during the build) images from every layer that visualize detailed layer defects and porosity. A database of camera images from every layer of AM parts made with various part quality was created that is the first visual labeled dataset from in-situ visual images of the powder-bed AM (also visualizing detailed layer features). The dataset is used in training-based classification to detect layers or sub-regions of the layer with low quality of fusion or defects. Features are carefully selected based on physical intuition into the process and extracted from the images of the various types of builds. A Bayesian classifier is developed and trained to classify the quality of the build that signifies the defective and unacceptable build layers or regions. The results can be used for quasi-real-time (layer-wise) process control, further process decisions, or corrective actions.

Keywords

Additive manufacturing Online quality inspection In-situ defect detection Bayesian inference Supervised learning Feature-based classification Computer vision Metal powder-bed additive manufacturing Laser powder-bed fusion 3D printing 

Notes

Acknowledgements

The authors would like to thank the Edison Welding Institute for funding this research and providing the equipment and support for conducting this work. This work was fulfilled under project “Measurement Science for Advanced Manufacturing” (MSAM) supported by the National Institute of Standards and Technology (NIST).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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