Title: Impacts of Recycled Aircraft Interiors on the Properties of Ocean Plastic PET

Authors: Lila B. Rockwood, Abigail R. Knipe, Jayme L. McAdam, Leia K. Kaminsky, John M. Misasi

DOI: 10.33599/nasampe/s.25.0128

Abstract: This work studied the impacts of mechanically recycled aircraft interior (rAI) panels on the properties of recycled ocean plastic poly(ethylene terephthalate) (opPET). Panels from rAI were size-reduced into a multi-material mixture, compounded into opPET in four different concentrations, and then injection molded into specimens. Processability of the blends during compounding and injection molding is discussed. Thermal degradation characteristics from thermal gravimetric analysis follow a rule of mixtures relationship, where increasing rAI content increased char yield and decreased degradation onset linearly. Melt and crystallization properties measured from differential scanning calorimetry show similar melt temperatures but showed increased crystallization temperatures. Processing viscosity was measured using parallel-plate rheology, but no trend was observed. Blend mechanical properties measured in tension show significantly lower properties than neat opPET. A discussion and analysis using imaging techniques is included to understand this knock-down. Finally, density and near-infrared (NIR) spectroscopy were utilized to both understand and show potential recyclability at standard material recovery facilities. Density was minimally impacted by rAI. All four compounds were ranked as “highly confident” via a NIR sortation methodology. This work provides a baseline to the potential benefits and challenges of including rAI into recycled thermoplastic compounds, making necessary progress towards composites circularity

References: [1] Tekinalp, Halil L., Kunc, Vlastimil, Velez-Garcia, Gregorio M., Duty, Chad E., Love, Lonnie J., Naskar, Amit K., Blue, Craig A., & Ozcan, Soydan. Highly oriented carbon fiber–polymer composites via additive manufacturing. United States. https://doi.org/10.1016/j.compscitech.2014.10.009 [2] Brenken B, Barocio E, Favaloro A, Kunc V, Pipes RB. Development and validation of extrusion deposition additive manufacturing process simulations. Addit Manuf 2019;25:218–26. [3] E. Barocio, P. Pibulchinda, A. J. Thomas, V. Kapre, and A. Franc, “Validated Simulation for Large Scale Additive Manufacturing.,” CAMX 2022, 2022. [4] A. J. Thomas, E. Barocio, I. Bilionis, R.B. Pipes, Toward parametric heat transfer solvers in additive manufacturing, Solid Freeform Fabrication 2024: Proceedings of the 35th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference. [5] M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J Comput Phys, vol. 378, pp. 686–707, 2019. [6] J. Stiasny, S. Chevalier, S. Chatzivasileiadis, Learning without data: Physics-informed neural networks for fast time-domain simulation, in: 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, pp. 634 438–443. [7] H. Eivazi, M. Tahani, P. Schlatter, R. Vinuesa, Physics-informed neural networks for solving reynoldsaveraged navier–stokes equations, Physics of Fluids 34 (2022). [8] E. Haghighat, M. Raissi, A. Moure, H. Gomez, R. Juanes, A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics, Computer Methods in Applied Mechanics and Engineering 379 (2021) 113741. [9] A. Khan and D. A. Lowther, "Physics Informed Neural Networks for Electromagnetic Analysis," in IEEE Transactions on Magnetics, vol. 58, no. 9, pp. 1-4, Sept. 2022, Art no. 7500404, doi: 10.1109/TMAG.2022.3161814 [10] S. Li, G. Wang, Y. Di, L. Wang, H. Wang, Q. Zhou, A physics-informed neural network framework to predict 3d temperature field without labeled data in process of laser metal deposition, Engineering Applications of Artificial Intelligence 120 (2023) 105908. [11] Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [12] X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, pp. 249–256. [13] S. Wang, H. Wang, P. Perdikaris, On the eigenvector bias of fourier feature networks: From regression to solving multi-scale pdes with physics-informed neural networks, Computer Methods in Applied Mechanics and Engineering 384 (2021) 113938.

Conference: SAMPE 2025

Publication Date: 2025/05/19

SKU: TP25-0000000128

Pages: 14

Price: $28.00