Title: A Model Based Accelerated RTM Process Design for Optimal Performance

Authors: Sanjay Sharma, Jason Scharf

DOI: 10.33599/nasampe/s.25.0118

Abstract: Typical CFRP composite high-rate manufacturing processes require a multi-physics understanding of the key material and process design variables. A model-based approach may deliver an optimized manufacturing process and yet require experimental validation of quality and mechanical performance to make it an acceptable solution to the industry. This study captures a multi-physics model-based development of an accelerated RTM process design for low permeability fiber reinforcement, while delivering laminates that meet the specifications on quality and key mechanical properties. Hexcel’s biaxial IM8 HiMax® non crimp fabric with a thermoplastic veil and 1078-1 resin is chosen for the study to develop a process design methodology for (177 °C) cure epoxy. Multi-physics material models of IM8 HiMax® and 10781 resin are used to simulate and predict the optimum cure cycles. Critical mechanical testing compares the outcomes from different cure cycles, including a baseline process nominally followed by the industry. Results show that the accelerated cured panels (50% cycle time compared to the baseline) are of good quality and perform just as well for the mechanical properties. This model-based approach can be extended to more complex geometry and structures for this material system or applied to additional composite material systems.

References: [1] How to store liquid hydrogen for zero-emission flight. 28 Feb. 2024. <https://www.airbus. com/en/newsroom/news/2021-12-how-to-store-liquid-hydrogen-for-zero-emission-flight>. [2] Wiedemann, M. System Lightweight Design for Aviation. Cham: Springer, 2023. [3] Grogan, D., Leen, S., Semprimoschnig, C., & Ó Brádaigh, C. "Damage characterisation of cryogenically cycled carbon fibre/PEEK laminates". Composites Part A: Applied Science and Manufacturing 66 (2014): 237-250. [4] Grogan, D. "Damage and permeability in linerless composite cryogenic tanks". PhD thesis, National University of Ireland, Galway, 2015. [5] Schürmann, H. "Wichtige Kenngrößen der Einzelschichten und des Laminats". Konstruieren mit Faser-Kunststoff-Verbunden. Berlin, Heidelberg: Springer, 2005. [6] Naumann, J. "Robust Detection and Explanation of Cracks in Microscopic Images". Master's thesis, Technische Universität Braunschweig, 2024. [7] Arzt, M., Deschamps, J., Schmied, C., Pietzsch, T., Schmidt, D., Tomancak, P., Haase, R. & Jug, F. "LABKIT: Labeling and Segmentation Toolkit for Big Image Data". Frontiers in Computer Science 4 (2022). [8] Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak P. & Cardona, A. "Fiji: an open-source platform for biologicalimage analysis". Nature Methods 9 (2012): 676–682. [9] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. "Scikit-learn: Machine Learning in Python". Journal of Machine Learning Research 12 (2011): 2825–2830. [10] Plaue, M. Data Science. Berlin; Heidelberg: Springer, 2023. [11] Ronneberger, O., Fischer, P. & Brox, T. "U-Net: Convolutional Networks for Biomedical Image Segmentation". Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Munich, Germany, October 5-9, 2015. Navab, N., Hornegger, J., Wells, W. & Frangi, A. (eds). Springer. pp. 234–241. [12] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J. & Houlsby, N. "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale". International Conference on Learning Representations. 2021. [13] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. & Guo, B. "Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows". IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada, October 10- 17, 2021. pp. 9992-10002. [14] Hassani, A. & Shi, H. "Dilated Neighborhood Attention Transformer". 2022. <https://arxiv.org/abs/2209.15001>. [15] Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., Wang, X. & Qiao, Y. "InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions". IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, BC, Canada, June 17-24, 2023. pp. 14408-14419. [16] Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H. & Wei, Y. "Deformable Convolutional Networks". IEEE International Conference on Computer Vision (ICCV). Venice, Italy, October 22-29, 2017. pp. 764-773. [17] Zhu, X., Hu, H., Lin, S. & Dai, J. "Deformable ConvNets V2: More Deformable, Better Results". IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA, June 15-20, 2019. pp. 9300-9308. [18] Krizhevsky, A., Sutskever, I. & Hinton, G.E. "ImageNet Classification with Deep Convolutional Neural Networks". Advances in Neural Information Processing Systems 25 (NIPS 2012). Lake Tahoe, NV, USA, December 3-8, 2012. Pereira, F., Burges, C.J., Bottou, L. & Weinberger, K.Q. (eds). Bd. 25, Curran Associates, Inc. [19] Croft, K., Lessard, L., Pasini, D., Hojjati, M., Chen, J. & Yousefpour, A. "Experimental study of the effect of automated fiber placement induced defects on performance of composite laminates". Composites Part A: Applied Science and Manufacturing (42) 2011: 484-491. [20] Paszke, A., et al. "PyTorch: An Imperative Style, High-Performance Deep Learning Library". Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Vancouver, BC, Canada, December 8-14, 2019. Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E. & Garnett, R. (eds). Bd. 32, Curran Associates, Inc. [21] Loshchilov, I. & Hutter, F. "Decoupled Weight Decay Regularization". International Conference on Learning Representations. New Orleans, LA, USA, May 6-9, 2019. [22] Loshchilov, I. & Hutter, F. "SGDR: Stochastic Gradient Descent with Warm Restarts". International Conference on Learning Representations (ICLR) 2017 Conference Track. Toulon, France, April 24-26, 2017. [23] Schäfer, Y. & Chadwick, A. "Fiber Volume Fraction and Distribution in Thermoplastic Composites for in-situ AFP". ECCM21. Nantes, France, July 2-5, 2024. Vol. 8, pp. 426- 433. [24] Appels, J.P., Sämann, P., Naumann, J., Brauer, C., Stefaniak, D., Atli-Veltin, B. & Dransfeld, C. "Investigation of Fiber Volume Fraction as Key Parameter in Cryogenic Hydrogen Tank Development". Proceedings of SAMPE 2025. Indianapolis, IN, USA, May 19-22, 2025. (Publication pending, Submission ID: TP25-0000000109)

Conference: SAMPE 2025

Publication Date: 2025/05/19

SKU: TP25-0000000118

Pages: 15

Price: $30.00