Gaussian process monitoring of layerwise-dependent imaging data

Additive Manufacturing (AM) enables the direct production of complex geometries from computer-aided designs (CAD). The AM fabrication process is often executed in a layer-by-layer manner, whereby minute printing errors in one layer can manifest significant defects in the final part. In-situ quality monitoring and control are currently limited for AM processes and cause low repeatability. Recently. advanced imaging is increasingly invested in AM and leads to the proliferation of layerwise imaging data, which provides an opportunity to transform quality control of AM from post-build inspection to in-situ quality monitoring. However, existing methodologies for in-situ inspection primarily focus on key characteristics of image profiles that tend to be limited in the ability to analyze the variance components, as well as root causes and failure patterns that are critical to process improvement. This letter presents an Additive Gaussian Process with dependent layerwise correlation (AGP-D) to model the spatio-temporal correlation of layerwise imaging data for AM quality monitoring. The AGP-D consists of three independent GP modules. The first GP approximates the base profile, whereas the second and third GP capture the correlation within the same layer and among layers, respectively. Based on posterior predictions of new layers, Hotelling T2 and generalized likelihood ratio (GLR) control tests are formulated to detect process shifts in the newly fabricated layer and analyze root causes. The proposed methodology is evaluated and validated using both simulation data and real-world case study of a cylinder build fabricated by a laser powder bed fusion (LPBF) machine. Experimental results show the proposed AGP-D is effective for real-time modeling and monitoring of layerwise-correlated imaging data.

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Work Title Gaussian process monitoring of layerwise-dependent imaging data
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Open Access
Creators
  1. Runsang Liu
  2. Bryan D. Vogt
  3. Hui Yang
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. IEEE Robotics and Automation Letters
Publication Date October 1, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1109/LRA.2021.3102625
Deposited November 15, 2021

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  • Added Final_Submission.pdf
  • Added Creator Runsang Liu
  • Added Creator Bryan D. Vogt
  • Added Creator Hui Yang
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