Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis
- Journal article
- Creator
-
Ryo Tamura (author) (Search by this author)National Institute for Materials Science
;
;Kenji Nagata (author) (Search by this author);Keitaro Sodeyama (author) (Search by this author);Kensaku Nakamura (author) (Search by this author);Toshiki Tokuhira (author) (Search by this author);Satoshi Shibata (author) (Search by this author);Kazuki Hammura (author) (Search by this author);Hiroki Sugisawa (author) (Search by this author);Masaya Kawamura (author) (Search by this author);Teruki Tsurimoto (author) (Search by this author);Masanobu Naito (author) (Search by this author)Masahiko Demura (author) (Search by this author)National Institute for Materials Science
;
Takashi Nakanishi (author) (Search by this author)National Institute for Materials Science
- Keyword
- Polypropylene, X-ray diffraction, Bayesian spectral deconvolution, Ising machine, Machine learning
- Date published
- 2024-12-31
- Updated at
- 2024-08-30 16:31:01 +0900
Glycol lignin/MAH-g-PP blends and composites with exceptional mechanical properties for automotive applications
- Journal article
- Creator
-
Jonathon Tanks (author) (Search by this author)National Institute for Materials Science
;
Kenji Tamura (author) (Search by this author)National Institute for Materials Science
;
Kimiyoshi Naito (author) (Search by this author)National Institute for Materials Science
;
;Thi Thi Nge (author) (Search by this author)Tatsuhiko Yamada (author) (Search by this author)
- Keyword
- Automotive composites, Glycol lignin, Polypropylene, Mechanical properties, Biomass
- Date published
- 2023-04-09
- Updated at
- 2025-04-17 08:30:12 +0900