Optimization of precharge placement in sheet molding compound process
Source abstract
Abstract
Abstract This study aims to provide precise predictions for the compression of reinforced polymers during the sheet Molding Compound (SMC) process, ensuring the attainment of a predefined structure while preventing material overflow during the process. The primary challenge revolves around identifying the optimal initial shape to prevent material rebound during the process. To confront this issue, a numerical model is utilized, faithfully simulating the SMC process and forming the foundation for our investigations. Furthermore, to optimize the pre-fill stage, a surrogate model is proposed to enhance modeling efficiency, and then an inverse analysis method is applied. This approach of minimizing material rebound during the SMC process results in a reliable metamodel to predict an initial mass shape accurately and at a low computational cost, thus ensuring the squeezed material fits the mold shape.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Injection Molding Process and Properties · Manufacturing Process and Optimization · Epoxy Resin Curing Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. Hsissou R, Seghiri R, Benzekri Z, Hilali M, Rafik M, Elharfi A (2021) Polymer composite materials: a comprehensive review. Compos Struct 262:11364010.1016/j.compstruct.2021.113640 ↗
2. Park C, Lee W (2012) Compression molding in polymer matrix composites. In: Manufacturing techniques for polymer matrix composites (PMCs), pp 47–94. Elsevier. Amsterdam10.1533/9780857096258.1.47 ↗
3. Barasinski A, Chinesta F, Ghnatios C, De Luca P (2023) Process simulation: moulding processes. In: Design and manufacture of structural composites, pp 401–426. Elsevier. Amsterdam10.1016/B978-0-12-819160-6.00002-0 ↗
4. Orgéas L, Dumont PJ (2011) Sheet molding compounds. Wiley encyclopedia of composites, pp 1–3610.1002/9781118097298.weoc222 ↗
5. Binetruy C, Chinesta F, Keunings R (2015) Flows in Polymers, Reinforced Polymers and Composites: A multi-Scale Approach. Springer, Berlin10.1007/978-3-319-16757-2 ↗
6. Perez M, Guevelou S, Abisset-Chavanne E, Chinesta F, Keunings R (2017) From dilute to entangled fibre suspensions involved in the flow of reinforced polymers: a unified framework. J Non-Newtonian Fluid Mech 250:8–1710.1016/j.jnnfm.2017.10.003 ↗
7. Perez M, Prono D, Ghnatios C, Abisset E, Duval JL, Chinesta F (2020) Advanced modeling and simulation of sheet moulding compound (smc) processes. Int J Mater Form 13:675–68510.1007/s12289-019-01506-2 ↗
8. Babu BJ, Waigaonkar S, Rajput A (2011) Experimental investigations and optimization of processibility of sheet moulding compound. Journal of Polymer Engineering10.1515/polyeng.2011.062 ↗
9. Lee L, Fan J, Kim J, Im Y-T (1991) Flow analysis of sheet molding compounds in compression molding. Int Polym Process 6(1):61–7210.3139/217.910061 ↗
10. Pfrommer J, Zimmerling C, Liu J, Kärger L, Henning F, Beyerer J (2018) Optimisation of manufacturing process parameters using deep neural networks as surrogate models. Procedia CiRP 72:426–43110.1016/j.procir.2018.03.046 ↗
11. Kudela J, Matousek R (2022) Recent advances and applications of surrogate models for finite element method computations: a review. Soft Comput 26(24):13709–1373310.1007/s00500-022-07362-8 ↗
12. Kim M-S, Lee WI, Han W-S, Vautrin A (2011) Optimisation of location and dimension of smc precharge in compression moulding process. Comput & Struct 89(15–16):1523–153410.1016/j.compstruc.2011.04.004 ↗
13. Ghnatios C, Masson F, Huerta A, Leygue A, Cueto E, Chinesta F (2012) Proper generalized decomposition based dynamic data-driven control of thermal processes. Comput Methods Appl Mech Eng 213:29–4110.1016/j.cma.2011.11.018 ↗
14. Chinesta F, Cueto E, Huerta A (2014) Pgd for solving multidimensional and parametric models. Separated representations and pgd-based model reduction: fundamentals and applications. Springer. Berlin, Heidelberg, pp 27–8910.1007/978-3-7091-1794-1_2 ↗
15. Ibáñez R, Abisset-Chavanne E, Ammar A, González D, Cueto E, Huerta A, Duval JL, Chinesta Fet al (2018) A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition. Complexity 201810.1155/2018/5608286 ↗
16. Heaton J (2018) Ian goodfellow, yoshua bengio, and aaron courville: deep learning: The mit press, 2016, 800 pp, isbn: 0262035618. Genet Program Evolvable Mach 19(1-2):305–30710.1007/s10710-017-9314-z ↗
17. Hesthaven JS, Rozza G (2008) Proper generalized decomposition of multiscale problems. SIAM J Numer Anal 46(1):348–377
18. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–182810.1109/TPAMI.2013.50 ↗
19. Nielsen MA (2015) Neural Networks and Deep Learning, vol 25. Determination press, San Francisco, CA, USA
20. Hernández Q, Badías A, González D, Chinesta F, Cueto E (2021) Structure-preserving neural networks. J Comput Phys 426:10995010.1016/j.jcp.2020.109950 ↗
21. Weichert D, Link P, Stoll A, Rüping S, Ihlenfeldt S, Wrobel S (2019) A review of machine learning for the optimization of production processes. J Adv Manuf Technol 104(5–8):1889–190210.1007/s00170-019-03988-5 ↗
22. Kindermann J, Linden A (1990) Inversion of neural networks by gradient descent. Parallel Comput 14(3):277–28610.1016/0167-8191(90)90081-J ↗
23. Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2(2):164–16810.1090/qam/10666 ↗
24. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–44110.1137/0111030 ↗
25. Rodriguez S, Monteiro E, Mechbal N, Rebillat M, Chinesta F (2023) Hybrid twin of rtm process at the scarce data limit. Int J Mater Form 16(4):4010.1007/s12289-023-01747-2 ↗
26. Tezduyar TE (2001) Finite element methods for flow problems with moving boundaries and interfaces. Arch Comput Methods Eng 8:83–13010.1007/BF02897870 ↗
27. Loon R, Anderson PD, Vosse FN, Sherwin SJ (2007) Comparison of various fluid-structure interaction methods for deformable bodies. Comput & Struct 85(11–14):833–84310.1016/j.compstruc.2007.01.010 ↗
28. Chinesta F, Mabrouki T, Ramon A (2002) Some difficulties in the flow front treatment in fixed mesh simulations of composites forming processes
29. Chinesta F, Cueto E, Quintela P, Paredes J (2004) Induced anisotropy in foams forming processes: modelling and simulation. J Mater Process Technol - J MATER PROCESS TECHNOL 155:1482–1488. https://doi.org/10.1016/j.jmatprotec.2004.04.37810.1016/j.jmatprotec.2004.04.378 ↗
30. Garcıa J, Gascón L, Chinesta F (2003) A fixed mesh numerical method for modelling the flow in liquid composites moulding processes using a volume of fluid technique. Comput Methods Appl Mech Eng 192(7–8):877–89310.1016/S0045-7825(02)00604-7 ↗
31. Piegl L, Tiller W (1996) The NURBS Book. Springer, Berlin, Germany10.1007/978-3-642-97385-7 ↗
32. Lange K, Lange K (2010) Singular value decomposition. Numerical analysis for statisticians, pp 129–14210.1007/978-1-4419-5945-4_9 ↗
33. Brunton SL, Kutz JN (2022) Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge, UK10.1017/9781009089517 ↗