Title: Generic Framework for Developing Process Digital Twin Applicable to High Value-Added Manufacturing

Authors: Ram Upadhyay, Domenico Borzacchiello, Jose Aguado, Umang Garg, Vivek Arora

DOI: 10.33599/nasampe/s.21.0577

Abstract: Our team has developed a generic modular framework to create Process Digital Twin (PDT) that can be customized for any process. Elements of this framework are (i) configuration of manufacturing process inputs as seen by user, (ii) capturing variation of manufacturing inputs (iii) build accurate and fast multi-parametric numerical solution using advanced model order reduction (MOR) techniques, (iv) Extract features for quality (FFQ) using specialized algorithms and (v) predict probability of success from quality models and (vi) capability to receive post-production process and quality data and use machine learning algorithms to automatically update various models. Irrespective of the physical and geometrical complexity of individual process, our numerical techniques assure a real time (< 10 sec) multi-parametric solution. Primary output of process digital twin (PDT) is predicting probability of success of a process based on all manufacturing inputs both material and physical. This allows us to use PDT to maximize production success by adjusting controllable inputs apriori. We have successfully used the framework to build digital twins for complex composite manufacturing processes that produce fan blades for aircraft engines and implemented it into manufacturing process. We will present some examples to demonstrate the paradigm shift of reactive to adaptive manufacturing.

References: 1. Upadhyay, R.K. and Sinha, S., “3.6 GE-90 and Derivative Fan Blade Manufacturing Design”, Comprehensive Composite Materials II, Volume 3 doi:10.1016/B978-0-12-803581-8.10075-X180, Elsevier Ltd. , 2018 2. Lednicky, T., Upadhyay, R.K., and Baer, M., “Paradigm Shift in Composite Fan Blade Manufacturing using Process Digital Twins”, paper to be presented at SAMPE 2020 conference, Seattle, 2020 3. Bathe, K. J. (2006. Finite Element Procedures. Civil engineering series. Prentice-Hall, Englewood Cliffs, NJ., 2006 4. Zienkiewicz, O. C. and Taylor, R. L., The finite element method. Vol. 1 The basis. Butterworth Heinemann, Oxford, 5th edition, 2000 5. Zienkiewicz, O. C. and Taylor, R. L., The finite element method. Vol. 2 Solid mechanics. Butterworth Heinemann, Oxford, 5th edition, 2000 6. Jolliffe, I., Principal Component Analysis. Springer Series in Statistics. Springer-Verlag, New York, 2nd edition., 2002 7. Białecki, R., Kassab, A., and Fic, A., “Proper Orthogonal Decomposition and modal analysis for acceleration of transient FEM thermal analysis”. Int. J. Numer. Meth. Engng., 62(6):774–797, 2005 8. Quarteroni, A., Manzoni, A., Negri, F.: Reduced Basis Methods for Partial Differential Equations: An Introduction, 1st edn. Modeling and Simulation in Science, Engineering and Technology. Springer, Basel, 2015. https://doi.org/10.1007/978-3-319-15431-2 9. Ghnatios, C., Masson, F., Huerta, A., Leygue, A., Cueto, E., Chinesta, F.: “Proper generalized decomposition based dynamic data-driven control of thermal processes”. Comput. Methods Appl. Mech. Eng. 213–216, 29–41, 2012 10. Borzacchiello, D., Aguado, J., Chinesta, F.: “Reduced order modelling for efficient optimization of a hot-wall chemical vapour deposition reactor”. Int. J. Numer. Method Heat Fluid Flow 27(4), 1602–1622, 2017. https://doi.org/10.1108/HFF-04-2016-0153 11. Aguado, J., Borzacchiello, D., Ghnatios, C., Lebel, F., Upadhyay, R., Binetruy, C., Chinesta, F.: “A Simulation App based on reduced order modeling for manufacturing optimization of composite outlet guide vanes”. Adv. Model. Simul. Eng. Sci. 4(1), 1–26, 2017. https://doi.org/10.1186/s40323-017-0087-y 12. Hernández, J., Caicedo, M., Ferrer, A.: “Dimensional hyper-reduction of nonlinear finite element models via empirical cubature”. Comput. Methods Appl. Mech. Eng. 313, 687–722, 2017. https://doi.org/10. 1016/j.cma.2016.10.022 13. Gamerman, D., and Lopes, H. F.: Markov chain Monte Carlo: stochastic simulation for Bayesian inference. Chapman and Hall/CRC, 2006. 14. Cheeseman, P.C., and Stutz, J.C.: “Bayesian classification (AutoClass): theory and results. Advances in knowledge discovery and data mining” 180, 153-180, 1996

Conference: SAMPE NEXUS 2021

Publication Date: 2021/06/29

SKU: TP21-0000000577

Pages: 15

Price: FREE