Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

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Abstract

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000 µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. We also introduce for the first time in the AM in situ sensing literature the a90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system's flaw detection performance. It is the belief of the authors that future studies should adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

Original languageEnglish
Article number103817
JournalAdditive Manufacturing
Volume78
DOIs
StatePublished - Sep 25 2023

Funding

This research was sponsored by the US Department of Energy’s Advanced Manufacturing Office with support from RTX . This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05–00OR22725 with the U.S. Department of Energy (DOE). DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://energy.gov/downloads/doe-public- access-plan). The authors would like to thank the team at ZEISS Industrial Metrology, LLC., including Paul Brackman, Dr. Pradeep Bhattad, and Dr. Curtis Frederick, for their help during XCT data generation. The authors would also like to thank Dr. James Haley for his thoughtful insights upon technical review of this document. Notice of Copyright: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05–00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research was sponsored by the US Department of Energy's Advanced Manufacturing Office with support from RTX. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05–00OR22725 with the U.S. Department of Energy (DOE). DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://energy.gov/downloads/doe-public- access-plan). The authors would like to thank the team at ZEISS Industrial Metrology, LLC. including Paul Brackman, Dr. Pradeep Bhattad, and Dr. Curtis Frederick, for their help during XCT data generation. The authors would also like to thank Dr. James Haley for his thoughtful insights upon technical review of this document.

Keywords

  • Additive manufacturing
  • In situ sensing
  • Non-destructive evaluation
  • Powder bed fusion
  • Process Monitoring

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