19 ADVANCED MATERIALS & PROCESSES | APRIL 2023 allowables was 58.7% less expensive than developing two conventional S-Basis MMPDS allowables. Using the exact same empirical dataset, ML allows the concomitant development of AM process parameters and materials allowables. The training data informs parameter optimization and allows for making statistically substantiated predictions about mechanical performance. Continued maturation of the technology is required, and a possible path forward posited. The key takeaway is that the ML approach has the potential to be just as accurate as the conventional approach while also being much more cost- effective and flexible. ~AM&P Acknowledgments The authors of the paper acknowledge the contributions of the following people who were members of U.S. Army Funded (AMMP Consortium) Project Developing ML-allowables on Stainless Steel 17-4 PH and the America Makes Project Developing ML-allowables on Fire Retardant Polymer: Hector Sandoval (Lockheed Martin Missiles & Fire Control); Sung Park, Tayelor McKay, and Crosby Owens (Northrop Grumman Aerospace Systems); Doug Hall (Battelle); Chris Holshouser (Texas A&M Engineering Experiment Station formally from WSU-NIAR); Olga Eliseeva and Katie Hardin (EWI); Kevin Sheehy, Carlos Berumen, Pat Garner, and Billy Herring (Stratasys Direct Manufacturing). For more information: Annie Wang, president, Senvol LLC, 335 Madison Ave., 4th Fl, New York, NY 10017, email@example.com, senvol.com. References 1. M.D. Wilkinson, The FAIR Principles: Guidelines for Publishing Reusable Data, Brief presented at the FAIR Additive Manufacturing (AM) Data Management GET ENGAGED, GET INVOLVED, GET CONNECTED The ASM Advanced Manufacturing Committee was recently launched and ASM members with interest and experience in additive manufacturing and other materials processing and manufacturing innovations are welcome to join. Visit the ASM Connect home page at connect.asminternational.org to post information or ask questions related to advanced manufacturing. For more information, contact committee chair William E. Frazier, FASM, or staff liaison Scott Henry, matinfo@asminter- national.org. Workshop, October 27, 2020, https:// www.asminternational.org/web/nist- asmdatamanagementworkshop. 2. PwC EU Services, Cost of Not Having FAIR Research Data, European Commission, European Union, March 2018, DOI: 10.2777/02999. 3. Ayon Dey, Machine Learning algorithms: A Review, International Journal of Computer Science and Information Technologies, 7(3), 2016. 4. Vansh Jatana, Machine Learning Algorithms, SRM Institute of Science and Technology, Research Proposal, June 2019, DOI: 10.13140/ RG.2.2.20559.92329. 5. O. Theobald, Machine Learning for Absolute Beginners, 2nd Edition, Scatter Plot Press, Middletown, DE, 2017, ISBN: 9781549617218. 6. Metallic Materials Properties Development and Standardization (MMPDS): MMPDS-06, (Federal Aviation Administration, Washington, D.C., 2011.