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Shape Memory Materials Analysis and Research Tool (SM2ART): Finding Data Anomalies and Trends

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Abstract

Typically, the first step in alloy selection and material production is to use handbooks, databases, or other materials guides to down-select to a specific composition and processing method for the desired application. This is true for conventional materials, such as steels, aluminums, and polymers, but until recently, no similar data source existed for shape memory materials (SMMs). There is no shortage of information in the SMM field; with over 90 years of research in the form of peer-reviewed articles, papers, and published data from companies; however, these data have not been accessible in a single location. This has posed many difficulties for the research and development of SMMs and has caused the field to move slowly. To remedy this, a web-based comprehensive repository known as the Shape Memory Materials Analysis and Research Tool (SM2ART) database has been developed. SM2ART provides unrestricted access to data from thousands of peer-reviewed articles and published data. These data are organized in a 2D and 3D visualization platform and provides viewers insight into shape memory alloys (SMAs), superelastic alloys, magnetic alloys, shape memory polymers (SMPs), and shape memory ceramics (SMCs). The work presented here provides a summary of the data available within the SM2ART database.

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Acknowledgements

Funding from the NASA Aeronautics Research Mission Directorate (ARMD) Transformational Tools and Technologies (TTT) project is gratefully acknowledged. The authors wish to thank Tyler Kujawa, Daniel D. Kilkenny, Sean A. Bostik, Ron Gould, and Edward A. Jones for their contributions in the software development that made the SM2ART database possible. The authors also thank Joshua Stuckner and Ian S. Howell for their contributions toward the analytical tools in the database, and Glen Bigelow and Darrell Gaydosh for content and reviews of the database. The authors also thank student interns Avery Young and Faith Gantz at the University of North Texas, William Trehern at Texas A&M University, Lina V. Daza at the University of Puerto Rico—Mayaguez, and Julie Foroosh at the University of Central Florida for data collection and extraction for the shape memory alloy section in the database. The authors also thank Hang Yu and Hunter Rauch from the Virginia Technical Institute for their contributions toward the shape memory ceramics section in the database, Kevin A. Cavicchi and Sayan Basak at the University of Akron for their contributions toward the shape memory polymers section in the database, Mohammad Elahinia and Parisa Bayati at the University of Toledo for their contributions toward the superelastic alloys section in the database, Ibrahim Karaman and William Trehern at Texas A&M University for their contributions toward CuAl alloys in the database, and Haluk E. Karaca, Guher P. Toker, and Sayed Ehsan Saghaian at the University of Kentucky for their contributions toward the magnetic shape memory alloy section in the database.

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    P. E. Caltagirone & O. Benafan

  2. Materials and Structures Division, NASA Glenn Research Center, Oak Ridge Associated Universities, Cleveland, OH, 44135, USA

    P. E. Caltagirone

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Caltagirone, P.E., Benafan, O. Shape Memory Materials Analysis and Research Tool (SM2ART): Finding Data Anomalies and Trends. Shap. Mem. Superelasticity 9, 558–584 (2023). https://doi.org/10.1007/s40830-023-00457-7

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