A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

Orlyanchik, Vladimir; Kimmell, Jeffrey; Snow, Zack; Paquit, Vincent

doi:10.13139/ORNLNCCS/2439935

Title: A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

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Abstract

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build.

Authors:

Orlyanchik, Vladimir; Kimmell, Jeffrey; Snow, Zack; Paquit, Vincent

ORNL-OLCF

Publication Date:: Thu Oct 03 04:00:00 UTC 2024

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Energy Efficiency and Renewable Energy (EERE); Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A)

Subject:: 42 ENGINEERING; Additive Manufacturing, WAAM, Acoustic Sensor, Weld current

OSTI Identifier:: 2439935

DOI:: https://doi.org/10.13139/ORNLNCCS/2439935

Citation Formats


                    Orlyanchik, Vladimir, Kimmell, Jeffrey, Snow, Zack, and Paquit, Vincent. A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process.  United States: N. p., 2024. 
        Web.  doi:10.13139/ORNLNCCS/2439935.

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                    Orlyanchik, Vladimir, Kimmell, Jeffrey, Snow, Zack, & Paquit, Vincent. A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process.  United States.  doi:https://doi.org/10.13139/ORNLNCCS/2439935

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                    Orlyanchik, Vladimir, Kimmell, Jeffrey, Snow, Zack, and Paquit, Vincent. 2024.  
"A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process".  United States.  doi:https://doi.org/10.13139/ORNLNCCS/2439935.  https://www.osti.gov/servlets/purl/2439935. Pub date:Thu Oct 03 04:00:00 UTC 2024

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@article{osti_2439935,

  title        = {A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process},

  author       = {Orlyanchik, Vladimir and Kimmell, Jeffrey and Snow, Zack and Paquit, Vincent},

  abstractNote = {Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build.},

  doi          = {10.13139/ORNLNCCS/2439935},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Oct 03 04:00:00 UTC 2024},

  month        = {Thu Oct 03 04:00:00 UTC 2024}

}

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DOI: https://doi.org/10.13139/ORNLNCCS/2439935

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