Spiral Extrusion Die Design using Modified Differential Evolution Algorithm
Abstract
In this work, a spiral extrusion die for industrial production of plastic foil has been designed using a modified differential evolution algorithm. The proposed method managed to provide a die design that was compliant with all demands of the foil manufacturer and lowered the production cost. Third-Party software is used to compute the die characteristics from the geometry designed by modified differential evolution.
References
Crawford, R. J. 1998. Plastics engineering. 3rd ed. Butterworth-Heinemann, Boston, USA. ISBN 0-7506-3764-1.
Michaeli, W. and Hopmann C. 2016. Extrusion dies for plastics and rubber. 4th Edition. Hanser Publishers, Cincinnati, USA. ISBN 978-1-56990-623-1.
Rauwendall, C. 2014. Polymer extrusion. 5th edition. Cincinnati: Hanser Publishers, Cincinnati, USA. ISBN 978-1-56990-516-6.
Storn, R. and Price, K. 1997. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization 11, 4, pp. 341–359. DOI: 10.1023/A:1008202821328
Das, S. and Suganthan, P. N. 2011. Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15, 1, pp. 4–31. DOI: 10.1109/TEVC.2010.2059031
Das, S., Mullick, S. S., and Suganthan, P. N. 2016. Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation, 27, April 2016, pp. 1-30. DOI: 10.1016/j.swevo.2016.01.004
Yildiy, A. 2019. PREDICTING THE ENERGY PRODUCTION OF A ROOFTOP PV PLANT BY USING DIFFERENTIAL EVOLUTION ALGORITHM. Turkish Journal of Engineering (TUJE), 3, 3, pp. 106–109. DOI: 10.31127/tuje.466953
Badillo-Olvera, A., et al. 2019. Burst detection and localization in water pipelines based on an extended differential evolution algorithm. Journal of Hydroinformatics. DOI: 10.2166/hydro.2019.123
Wang, L., et al. 2018. Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm. Energy, 153: 15 June 2018, pp. 801–815. DOI: 10.1016/j.energy.2018.04.078
Gamperle, R., Muller, S. D., and Koumoutsakos, P. 2002. A parameter study for differential evolution. In WSEAS Int. Conf. on Advances in intelligent systems, fuzzy systems, evolutionary computation. WSEAS Press, Stevens Point, WI, USA, Vol. 10, No. 10, pp. 293–298.
Liu, J. and Lampinen, J. 2002. On setting the control parameter of the differential evolution method. In Proceedings of the 8th international conference on soft computing (MENDEL 2002). Brno, Czech Republic, pp. 11–18.
Neri, F. and Tirronen, V. 2010. Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review, 33, 1–2, pp. 61–106. DOI: 10.1007/s10462-009-9137-2
Virtual Extrusion Laboratory. 2016. Software, on-line: http://www.compuplast.com/product/vel-modules, Comuplast.
Matousek, R., Popela, P. and Kudela, J. 2017. Heuristic Approaches to Stochastic Quadratic Assignment Problem: VaR and CVar Cases. MENDEL. 23, 1 (Jun. 2017), 73-78. DOI: https://doi.org/10.13164/mendel.2017.1.073
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