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Automated Segmentation and Chord Length Distribution of Melt Pools in Complex 3D Printed Metal Artifacts

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Abstract

We present a new computational approach for large-scale segmentation and spatially-resolved analysis of melt pools in complex 3D printed parts and qualification artifacts. Our hybrid segmentation includes human-in-the-loop image processing of a few representative optical images of melt pools that are then used for training machine learning models for automated segmentation of melt pool boundaries in large parts. Our approach specifically targets minimizing the need for manual annotation. Considering imperfect segmentation and errors unavoidable with most algorithms, we further propose chord length distribution as a statistical description of melt pool sizes relatively tolerant to segmentation errors. We first show and validate our new approach on optical images of melt pools in a simple 3D printed plate sample (IN718 alloy) as well as selected regions of a complex qualification artifact (AlSi10Mg alloy). We then demonstrate the application of our approach on an entire cross section of the artifact.

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Code availability

The codes for calculations of CLD/SR-CLD as well as for training/fine-tuning models for segmentation of melt pools are available at https://github.com/materialsinformaticsaz/meltpool_segment_and_chords.

Notes

  1. We report the runtime of both models for a Lenovo NeXtScale nx360 M5 high-performance system with 28 CPU nodes having 6 GB memory/node.

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Acknowledgements

SEW acknowledges the support by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-2137419. The images used in this research were part of the Global Test Artifact Data Exchange Program (GTADExP)—an effort at The University of Texas at El Paso (UTEP) within the W.M. Keck Center for 3D Innovation. The authors are grateful to the many UTEP students associated with GTADExP and specifically Oscar Garcia and Luis Tarango, who prepared the samples and images for the study described here. For this research, the GTADExP program was sponsored, in part, through award 70NANB21H006 from the U.S. Department of Commerce, National Institute of Standards and Technology (NIST). Additional support at UTEP was provided by strategic investments via discretionary UTEP Keck Center funds and the Mr. and Mrs. MacIntosh Murchison Chair I in Engineering Endowment. Finally, the authors acknowledge High Performance Computing resources supported by the University of Arizona TRIF, UITS, and the Office of Research, Innovation, and Impact, and maintained by the UArizona Research Technologies Department. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the National Science Foundation, the U.S. Department of Commerce, NIST, or the U.S. Government.

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Authors and Affiliations

  1. Graduate Interdisciplinary Program in Applied Mathematics, University of Arizona, Tucson, AZ, 85721, USA

    Sheila E. Whitman & Marat I. Latypov

  2. Department of Materials Science and Engineering, University of Arizona, Tucson, AZ, 85721, USA

    Guangyu Hu & Marat I. Latypov

  3. Tailored Alloys, Youngstown, OH, 44504, USA

    Hunter C. Taylor

  4. W.M. Keck Center for 3D Innovation, The University of Texas at El Paso, El Paso, TX, 79968, USA

    Hunter C. Taylor & Ryan B. Wicker

  5. Department of Aerospace and Mechanical Engineering, University of Texas at El Paso, El Paso, TX, 79968, USA

    Ryan B. Wicker

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  1. Sheila E. Whitman
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Correspondence to Marat I. Latypov.

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Whitman, S.E., Hu, G., Taylor, H.C. et al. Automated Segmentation and Chord Length Distribution of Melt Pools in Complex 3D Printed Metal Artifacts. Integr Mater Manuf Innov 13, 229–243 (2024). https://doi.org/10.1007/s40192-023-00329-z

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