Two optimization problems are formulated to improve the effectiveness and productivity of pultrusion processes, to preserve the quality of pultruded profiles, and to take into account the ambient industrial shop temperature and requirements of process technologists. To solve these problems, an optimization methodology using designed computer experiments and the response surface technique was developed. The effects of room temperature and curing allowed behind the die exit on the energy consumption and pull speed were investigated. A more accurate and realistic process optimization was achieved by the temperature control strategy with heater switch-on and -off operations. This indirect optimization methodology allowed us to develop interactive technological maps on the basis of an accessible-to-all Excel code for technologists working in industrial shops. As an example, demonstrating the effectiveness of the methodology developed and utilization of the interactive technological map, the optimization of a real pultrusion process, producing two rod profiles with ears simultaneously, is carried out.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
J. Li, S. C. Joshi, and Y. C. Lam, “Curing optimization for pultruded composite sections,” Compos. Sci. Technol., 62, 457-467 (2002).
P. Carlone, G. S. Palazzo, and R. Pasquino, “Pultrusion manufacturing process development: Cure optimization by hybrid computational methods,” Comput. Math. Appl., 53, 1464-1471 (2007).
Y. C. Lam, J. Li, and S. C. Joshi, “Simultaneous optimization of die-heating and pull-speed in pultrusion of thermosetting composites,” Polym. Compos., 24, No. 1, 199-209 (2003).
S. C. Joshi, T. C. Lam, and U. W. Tun, “Improved cure optimization in pultrusion with preheating and die-cooler temperature,” Compos. Part A, 34, 1151-1159 (2003).
R. M. L. Coelho and V. M. A. Calado, “An optimization procedure for the pultrusion process based on a finite-element formulation,” Polym. Compos., 23, No. 3, 329-341 (2002).
J. A. D. Wilcox and D. T. Wright, “Towards pultrusion process optimization using artificial neural networks,” J. Mater. Process. Technol., 83, 131–141 (1998).
X. Chen, H. Xie, H. Chen, and F. Zhang, “Optimization for CFRP pultrusion process based on genetic algorithm-neural network,” Inter. J. Mater. Forming, 3, No. 2, 1391-1399 (2010).
C. C. Tutum, K. Deb, and I. Baran, “Constrained efficient global optimization for pultrusion process,” Mater. Manuf. Process., 30, No. 4, 538-551 (2015).
C. C. Tutum, I. Baran, and K. Deb, “Optimum design of pultrusion process via evolutionary multi-objective optimization,” Int. J. Adv. Manuf. Technol., 72, 1205-1217 (2014).
S. C. Joshi, Y. C. Lam, and K. Zaw, “Optimization for quality thermosetting composites pultrudate through die heater layout and power control,” Proc. of 16th Int. Conf. Compos. Mater., Kyoto, 7 P (2007).
C. F. J. Wu and M. Hamada, Experiments: Planning, Analysis, and Parameter Design Optimization, John Wiley & Sons, New York (2000).
R. H. Myers and D. C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, New York (2002).
E. Barkanov, P. Akishin, N. L. Miazza, and S. Galvez, “ANSYS-based algorithms for a simulation of pultrusion processes,” Mech. Adv. Mater. Struc., 24, No. 5, 377-384 (2017).
E. Barkanov, P. Akishin, E. Namsone, A. Bondarchuk, and N. Pantelelis, “Real time characterization of pultrusion processes with a temperature control,” Mech. Compos. Mater., 56, No. 2, 135-148 (2020).
E. Barkanov, P. Akishin, N. L. Miazza, S. Galvez, and N. Pantelelis, “Experimental validation of thermochemical algorithm for a simulation of pultrusion processes,” J. Phys. Conf. Ser., 991, 012009 (2018).
J. Auzins, A. Janushevskis, J. Janushevskis, and E. Skukis, “Software EDAOpt for experimental design, analysis and multiobjective robust optimization,” Proc. of 1st Int. Conf. Eng. Appl. Sc. Optim., Kos, 1055-1077 (2014).
D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York (2017).
F. A. C. Viana, R. T. Haftka, and V. Steffen, “Multiple surrogates: how cross-validation errors can help us to obtain the best predictor,” Struct. Multidiscipl. Optim., 39, 439-457 (2009).
URL: http://www.compor.lv/en (reference date 22.09.2020).
E. Namsone, A. Arshanitsa, and A. Morozovs, “Analysis of curing kinetic models for polyester resin CL ISO 112 G,” Key Eng. Mater., 850, 70-75 (2020).
The financial support of European Regional Development Fund for the project No. 22.214.171.124/18/A/053 “An effectiveness improvement of conventional pultrusion processes” is acknowledged.
Russian translation published in Mekhanika Kompozitnykh Materialov, Vol. 56, No. 6, pp. 1015-1036, November-December, 2020.
About this article
Cite this article
Barkanov, E., Akishin, P., Namsone, E. et al. Optimization of Pultrusion Processes for an Industrial Application. Mech Compos Mater 56, 697–712 (2021). https://doi.org/10.1007/s11029-021-09916-7
- industrial process
- experimental design
- response surfaces
- interactive technological map