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

Internet

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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