Open Access
Issue |
E3S Web Conf.
Volume 428, 2023
2023 Research, Invention, and Innovation Congress (RI2C 2023)
|
|
---|---|---|
Article Number | 02013 | |
Number of page(s) | 7 | |
Section | Technology for Environment and Sustainable Development | |
DOI | https://doi.org/10.1051/e3sconf/202342802013 | |
Published online | 14 September 2023 |
- J. Zhang, V.S. Chevali, H. Wang, C.H. Wang, Current status of carbon fibre and carbon fibre composites recycling, Composites Part B: Engineering, 193 (2020): 108053 [CrossRef] [Google Scholar]
- A.P. Mouritz, Introduction to aerospace materials, Elsevier, (2012) [Google Scholar]
- M. Carus, A. Eder, L. Dammer, H. Korte, L. Scholz, R. Essel, M. Barth, Wood-plastic composites (WPC) and natural fibre composites (NFC), Nova-Institute: Hürth, Germany, 16 (2015) [Google Scholar]
- M. Elbadawi, L.E. McCoubrey, F.K. Gavins, J.J. Ong, A. Goyanes, S. Gaisford, A.W. Basit, Disrupting 3D printing of medicines with machine learning, Trends in Pharmacological Sciences, 42, 9 (2021): 745-757 [CrossRef] [PubMed] [Google Scholar]
- O. Faruk, A.K. Bledzki, H.P. Fink, M. Sain, Progress report on natural fiber reinforced composites, Macromolecular Materials and Engineering, 299, 1 (2014): 9-26 [CrossRef] [Google Scholar]
- S. Athmaja, M. Hanumanthappa, V. Kavitha, A survey of machine learning algorithms for big data analytics, In 2017 International conference on innovations in information, embedded and communication systems (ICIIECS), IEEE, (2017): 1-4 [Google Scholar]
- A. Sharma, T. Mukhopadhyay, S.M. Rangappa, S. Siengchin, V. Kushvaha, Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design, Archives of Computational Methods in Engineering, 29, 5 (2022): 3341-3385 [CrossRef] [Google Scholar]
- M.X. Zhu, Q.C. Yu, H.G. Song, T.X. Chen, J.M. Chen, Rational design of high-energy-density polymer composites by machine learning approach, ACS Applied Energy Materials, 4, 2 (2021): 14491458 [Google Scholar]
- Y.K. Hamidi, A. Berrado, M.C. Altan, Machine learning applications in polymer composites. In AIP Conference Proceedings, AIP Publishing, 2205, 1 (2020) [Google Scholar]
- P. Pattnaik, A. Sharma, M. Choudhary, V. Singh, P. Agarwal, V. Kukshal, Role of machine learning in the field of Fiber reinforced polymer composites: A preliminary discussion. Materials Today: Proceedings, 44 (2021): 4703-4708 [CrossRef] [Google Scholar]
- M.A. Matos, S.T. Pinho, V.L. Tagarielli, Application of machine learning to predict the multiaxial strainsensing response of CNT-polymer composites, Carbon, 146, (2019): 265-275 [CrossRef] [Google Scholar]
- S. Cassola, M. Duhovic, T. Schmidt, D. May, Machine learning for polymer composites process simulation-a review, Composites Part B: Engineering, (2022): 110208 [CrossRef] [Google Scholar]
- P.P. Das, M.M. Rabby, V. Vadlamudi, R. Raihan, Moisture content prediction in polymer composites using machine learning techniques, Polymers, 14, 20 (2022): 4403 [CrossRef] [PubMed] [Google Scholar]
- J. Wang, M.A. Ayari, A. Khandakar, M.E. Chowdhury, S.A. Uz Zaman, T. Rahman, B. Vaferi, Estimating the relative crystallinity of biodegradable polylactic acid and polyglycolide polymer composites by machine learning methodologies, Polymers, 14, 3 (2022): 527 [CrossRef] [PubMed] [Google Scholar]
- H.E. Balcıoğlu, A.Ç. Seçkin, Comparison of machine learning methods and finite element analysis on the fracture behavior of polymer composites, Archive of Applied Mechanics, 91 (2021): 223-239 [CrossRef] [Google Scholar]
- B. Liu, N. Vu-Bac, X. Zhuang, X. Fu, T. Rabczuk, Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites, Composites Science and Technology, 224 (2022): 109425 [CrossRef] [Google Scholar]
- G. Kamath, B. Mishra, S. Tiwari, A. Bhardwaj, S.S. Marar, S. Soni, S.B. Anjappa, Experimental and statistical evaluation of drilling induced damages in glass fiber reinforced polymer composites-Taguchi integrated supervised machine learning approach, Engineered Science, 19 (2022): 312-318 [Google Scholar]
- C.T. Chen, G.X. Gu, Machine learning for composite materials, MRs Communications, 9, 2 (2019): 556566 [Google Scholar]
- A. Sharma, T. Mukhopadhyay, S.M. Rangappa, S. Siengchin, V. Kushvaha, Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design, Archives of Computational Methods in Engineering, 29, 5 (2022): 3341-3385 [CrossRef] [Google Scholar]
- G.R. Arpitha, H. Mohit, P. Madhu, A. Verma, Effect of sugarcane bagasse and alumina reinforcements on physical, mechanical, and thermal characteristics of epoxy composites using artificial neural networks and response surface methodology, Biomass Conversion and Biorefinery, (2023): 1-19 [Google Scholar]
- G. Spathis, E. Kontou, Creep failure time prediction of polymers and polymer composites, Composites Science and Technology, 72, 9 (2012): 959-964 [CrossRef] [Google Scholar]
- P.K. Penumakala, J. Santo, A. Thomas, A critical review on the fused deposition modeling of thermoplastic polymer composites, Composites Part B: Engineering, 201, (2020): 108336 [CrossRef] [Google Scholar]
- G. Anand, N. Alagumurthi, R. Elansezhian, K. Palanikumar, N. Venkateshwaran, Investigation of drilling parameters on hybrid polymer composites using grey relational analysis, regression, fuzzy logic, and ANN models, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40 (2018): 120 [CrossRef] [Google Scholar]
- M. Noryani, S.M. Sapuan, M.T. Mastura, M.Y.M. Zuhri, E.S. Zainudin, A statistical framework for selecting natural fibre reinforced polymer composites based on regression model. Fibers and Polymers, 19 (2018): 1039-1049 [CrossRef] [Google Scholar]
- H. El Kadi, Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks-A review, Composite structures, 73, 1 (2006): 1-23 [CrossRef] [Google Scholar]
- S.M. Rangappa, S. Siengchin, H.N. Dhakal, Greencomposites: Ecofriendly and sustainability, Applied Science and Engineering Progress, 13, 3 (2020): 183184 [Google Scholar]
- S.M. Rangappa, S. Siengchin, Natural fibers as perspective materials, Applied Science and Engineering Progress, 11, 4 (2018) [Google Scholar]
- S.H. Jang, H. Yin, Effective electrical conductivity of carbon nanotube-polymer composites: a simplified model and its validation, Materials Research Express, 2, 4 (2015): 045602 [CrossRef] [Google Scholar]
- F. Chen, J. Wang, Z. Guo, F. Jiang, R. Ouyang, P. Ding, Machine learning and structural design to optimize the flame retardancy of polymer nanocomposites with graphene oxide hydrogen bonded zinc hydroxystannate, ACS Applied Materials & Interfaces, 13, 45 (2021): 53425-53438 [CrossRef] [PubMed] [Google Scholar]
- M. Puttegowda, H. Pulikkalparambil, S.M. Rangappa, Trends and developments in natural fiber composites, Applied Science and Engineering Progress, 14, 4 (2021): 543-552 [Google Scholar]
- M.C. de Souza, I. Moroz, I. Cesarino, A.L. Leão, M. Jawaid, O.A.T. Dias, A Review of Natural Fibers Reinforced Composites for Railroad Applications, Applied Science and Engineering Progress, 15, 2 (2022): 5800-5800 [Google Scholar]
- Y.D. Boon, S.C. Joshi, S.K. Bhudolia, G. Gohel, Recent advances on the design automation for performance-optimized fiber reinforced polymer composite components, Journal of Composites Science, 4, 2 (2020): 61 [CrossRef] [Google Scholar]
- A. Milad, S.H. Hussein, A.R. Khekan, M. Rashid, H. Al-Msari, T.H. Tran, Development of ensemble machine learning approaches for designing fiberreinforced polymer composite strain prediction model, Engineering with Computers, 38, 4 (2022): 3625-3637 [CrossRef] [Google Scholar]
- D. Yue, Y. Feng, X.X. Liu, J.H. Yin, W.C. Zhang, H. Guo, Q.Q. Lei, Prediction of Energy Storage Performance in Polymer Composites Using High‐Throughput Stochastic Breakdown Simulation and Machine Learning, Advanced Science, 9, 17 (2022): 2105773 [CrossRef] [Google Scholar]
- M.X. Zhu, Q.C. Yu, H.G. Song, T.X. Chen, J.M. Chen, Rational design of high-energy-density polymer composites by machine learning approach, ACS Applied Energy Materials, 4, 2 (2021): 14491458 [Google Scholar]
- M. Shi, C.P. Feng, J. Li, S.Y. Guo, Machine learning to optimize nanocomposite materials for electromagnetic interference shielding, Composites Science and Technology, 223 (2022): 109414 [CrossRef] [Google Scholar]
- Z. Liang, Z. Li, S. Zhou, Y. Sun, J. Yuan, C. Zhang, Machine-learning exploration of polymer compatibility, Cell Reports Physical Science, 3, 6 (2022): 100931 [CrossRef] [Google Scholar]
- H. Wei, S. Zhao, Q. Rong, H. Bao, Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods, International Journal of Heat and Mass Transfer, 127 (2018): 908-916 [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.