Interpretable machine learning modeling of formation process of ceramic materials using the precursor polymer method

Authors

• Yoh-ichi Mototake, Hitotsubashi University, JapanFollow
• Hayato Okubo, Graduate School of Social Data Science, Hitotsubashi University, Japan
• Naohiro Shichijo, Graduate School of Social Data Science, Hitotsubashi University, Japan
• Hiroyuki Kumazoe, Graduate School of Social Data Science, Hitotsubashi University, Japan
• Satoshi Tanaka, Graduate School of Social Data Science, Hitotsubashi University, Japan
• Toshimitsu Aritake, Graduate School of Social Data Science, Hitotsubashi University, Japan
• Hidetoshi Morita, Graduate School of Social Data Science, Hitotsubashi University, Japan
• Yoshifumi Amamoto, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa Campus, Japan
• Soichiro Kyushin, Advanced Institute of Materials Science, 1 5 1 Tenjin cho, Kiryu, Japan
• Mitsuhiko Sato, Advanced Institute of Materials Science, 1 5 1 Tenjin cho, Kiryu, Japan

Conference Dates

October 26-31, 2025

Abstract

Please click Additional Files below to see the full abstract

Recommended Citation

Yoh-ichi Mototake, Hayato Okubo, Naohiro Shichijo, Hiroyuki Kumazoe, Satoshi Tanaka, Toshimitsu Aritake, Hidetoshi Morita, Yoshifumi Amamoto, Soichiro Kyushin, and Mitsuhiko Sato, "Interpretable machine learning modeling of formation process of ceramic materials using the precursor polymer method" in "Ceramic Matrix Composites III", R. Darolia, GE Aerospace; K. Goto, JAXA; T. Akatsu, Tokyo University of Technology; S. Kitaoka, Japan Fire Ceramics Center; G. Vignoles, University of Bordeaux Eds, ECI Symposium Series, (2025). https://dc.engconfintl.org/ceramic_matrix_composites_iii/35