Effect of Process and Material Parameters on the Permeabilities of Reinforcement Mats: Experimentations and Machine Learning Techniques-Reference-Cited by-同舟云学术

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Effect of Process and Material Parameters on the Permeabilities of Reinforcement Mats: Experimentations and Machine Learning Techniques

Published:2024-05-04 Issue:6 Volume:25 Page:2285-2302
ISSN:1229-9197
Container-title:Fibers and Polymers
language:en
Short-container-title:Fibers Polym

Author:

Zade Anita,Neogi Swati,Kuppusamy Raghu Raja Pandiyan

Funder

“Extra Mural Research (EMR) Scheme” of the Science & Engineering Research Board (SERB), Department of Science and Technology (DST), India.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12221-024-00571-y.pdf

Reference24 articles.

1. R.S. Pierce, B.G. Falzon, M.C. Thompson, A multi-physics process model for simulating the manufacture of resin-infused composite aerostructures. Compos. Sci. Technol. 149, 269–279 (2017). https://doi.org/10.1016/j.compscitech.2017.07.003

2. W. Sanjay Mazumdar, Opportunity and Challenges in Automotive Composites Industry. (2013), pp. 8–21

3. K. Raghu Raja Pandiyan, S. Neogi, Simulation of air entrapment and resin curing during manufacturing of composite cab front by resin transfer moulding process. Arch. Metall. Mater. 62(3), 1839–1844 (2017)

4. K. Raghu Raja Pandiyan, S. Neogi, Viscosity modeling of a medium reactive unsaturated polyester resin used for liquid composite molding process. J. Appl. Polym. Sci. 125, 1400–1408 (2012)

5. M. Seyednourani, M. Yildiz, H.S. Sas, A two-stage optimization methodology for gate and vent locations and distribution media layout for liquid composite molding process. Compos. Part A Appl. Sci. Manuf. 149, 106522 (2021). https://doi.org/10.1016/j.compositesa.2021.106522

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