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Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder

Random forest regression was applied to optimize the melt-blending process of polyphenylene sulfide (PPS) with poly(ethylene-glycidyl methacrylate-methyl acrylate) (E-GMA-MA) elastomer to improve the Charpy impact strength. A training dataset was constructed using four elastomers with different GMA...

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Detalles Bibliográficos
Autores principales:	Takada, Shingo, Suzuki, Toru, Takebayashi, Yoshihiro, Ono, Takumi, Yoda, Satoshi
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8674312/ https://www.ncbi.nlm.nih.gov/pubmed/34911974 http://dx.doi.org/10.1038/s41598-021-03513-3

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