Title: Digitalization Challenges in Composites

Authors: Asjad Shafi, Ike Latham, James Barron

DOI: 10.33599/nasampe/c.24.0229

Abstract: Composites have high specific-strength and specific-stiffness. They have found a wide range of applications in transportation, aerospace, marine, consumer goods, pipe and tank, wind energy, and construction. The global composites market is expected to grow at an annual rate of 4-7 percent in the next decade. However, composites are not growing at their full potential due to high developmental and manufacturing costs. Digital Transformation offers a huge potential for reducing design and manufacturing costs, but it is not without its challenges. The first step towards this transformation is digitization or the collection of scientific data as it is being generated. The collected data is used to develop Digital tools. These tools can be broadly divided into two groups: Predictive/Design tools and Process Simulation (Scale Up) tools. We review available digitalization tools, identify gaps, and wherever possible, propose solutions to fill these gaps. We also discuss Rheo-kinetic models which are fundamental to any manufacturing process simulation. These tools can be used to optimization and troubleshoot the manufacturing process. For digitalization program to be successful, it must be a part of the work process, simplify the work process and increase the efficiency. The digitalization plan must have a strong work flow engine in the back where different manufacturing process simulation models, material property prediction models, databases, data visualization, search engines and other digitalization tools should be able to seamlessly integrate through an innovator friendly canvas (front end).

References: 1. “Advanced Composites Market Size, Share & Trends Analysis Report by Product (Aramid, Carbon, Glass), By Application (Aerospace & Defense, Automotive, Construction, Sporting Goods, Wind Energy), By Region, And Segment Forecasts, 2022 – 2030,” Report ID GVR-1-68038-268-6, Grand View Research 2022. 2. “Facilitating Industry by Engineering, Roadmapping and Science (FIBERS) to Advance U.S. Manufacturing of Composites,” AMTech Program - National Institute for Science and Technology, Award Number 70NANB14H057, 2014. 3. Sherwood, J., Drane, P., & White, K., “U.S. Composites Manufacturing Industry Technical Roadmap.” Facilitating Industry by Engineering, Roadmapping and Science (FIBERS) Consortium, 2017. 4. Sapuan, S. M., Mujtaba, I. M., “Composite Materials Technology: Neural Network Applications.” Boca Raton: CRC Press, 2010. 5. Kamal, M. R., & Sourour, S. “Kinetics and thermal characterization of thermoset cure.” Polym. Eng. Sci. 13 (1973): 59-64. DOI 10.1002/pen.760130110. 6. Castro, J.M., & Macosko, C., “Studies of mold filling and curing in the reaction injection molding process,” A.I.Ch.E. J. 28 (1982): 250-260. DOI 10.1002/aic.6902802. 7. Dusi, M. R., May, C. A. & Seferis, J. C., “Predictive Models as Aids to Thermoset Resin Processing.” ACS Symp. Series 227:1983 301-318. DOI 10.1021/bk-1982-0227.ch018. 8. Han, K., Lee, L. J., Nakamura, S., Shafi, A., & White, D. “Dry Spot Formation and Changes in Liquid Composite Molding: II—Modeling and Simulation.” J Composite Materials, 30: (1996): 1475-1493. DOI 10.1177/002199839603001304. 9. Zade, A., & Kuppusamy, R. R. P., “A review on numerical optimization in liquid composite moulding processes.” Materials Today: Proceedings 19 (2019): 329-332. DOI 10.1016/ j.matpr.2019.07.605. 10. Shah M. & Chaudhary, V., “Flow modeling and simulation study of vacuum assisted resin transfer molding (VARTM) process: A review.” 2020 IOP Conf. Ser.: Mater. Sci. Eng. 872 (2020): DOI 10.1088/1757-899X/872/1/012087. 11. Shenogina, N. B., Tsige, M., Patnaik, S. S., & Mukhopadhyay, S. M., “Molecular Modeling Approach to Prediction of Thermo-Mechanical Behavior of Thermoset Polymer Networks.” Macromolecules 45 (2012): 5307-5315. DOI 10.1021/ma3007587. 12. Li, C., Coons, E., & Strachan, A., “Material property prediction of thermoset polymers by molecular dynamics simulations.” Acta Mechanica 225 (2014): 1187-1196. DOI 10.1007/s00707-013-1064-2. 13. Nouri, N., & Ziaei-Rad, S., “A Molecular Dynamics Investigation on Mechanical Properties of Cross-Linked Polymer Networks.” Macromolecules 44 (2011): 5481-5489, DOI 10.1021/ma2005519. 14. Nakajin, K., Minami, T., & Kawata, M., “Prediction of physical properties of thermosetting resin by using machine learning and structural formulas of raw materials.” MRS Advances 5 (2020): 1567-1575, DOI 10.1557/adv.2020.266. : Kibrete, F.; Trzepieci´ nski, T.; 15. Gómez, F., & Bruce, W., “Digital Transformation Initiative: Chemistry and Advanced Materials Industry, White Paper.” Geneva Switzerland: World Economic Forum, 2019. 16. Bharadwaj, R. “Machine Learning in the Chemical Industry – BASF, DOW, Royal Dutch Shell, and More,” Emerj: The AI Research and Advisory Company, https://emerj.com/ai-sector-overviews/machine-learning-chemical-industry-basf-dow-shell/, November 22, 2019. 17. Sumpter, B. G. & Noid, D. W. “Neural Networks as Tools for Predicting Materials Properties.” ANTEC (1997): 2557-2560. 18. Wagner N. & Rondinelli, J. M., “Theory-guided Machine learning in Materials Science Frontiers in Materials.” Frontiers in Materials 3 (2016). DOI 10.3389/fmats.2016.00028. 19. Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, S., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V., “Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data.” IEEE Transactions on Knowledge and Data Engineering 29 (2017): 2318-2331. DOI 10.1109/TKDE.2017.2720168. 20. Gebremedhen, H.S., Woldemichael, D.E., “Artificial Intelligence in Predicting Mechanical Properties of Composite Materials. J. Compos. Sci., 7, 364. 2023, https://doi.org/10.3390/jcs7090364. 21. Shafi, A., Latham, I. and Obi, B., “Digitalization Challenges in the Polyurethane Industry,” Polyurethanes Technical Conference 2021, Denver, Colorado, USA, 5-7 October 2021, ISBN: 978-1-7138-3935-4, 110-122. 22. Aguirre-Vargas, F., Shafi, A., Gillis, P., Cookson, P., Mukhopadhyay, S., and Schmidt, C., “Embedding Digitalization into Polyurethane Applications,” Polyurethanes Technical Conferenc 2021, Denver, Colorado, USA, 5-7 October 2021, ISBN: 978-1-7138-3935-4, 146-156. 23. www.intugent.com.

Conference: CAMX 2024 | San Diego CA

Publication Date: 2024/9/9

SKU: TP24-0000000229

Pages: 11

Price: $22.00