A Multi-Objective Optimization of a Porthole Die Extrusion for Quality and Sustainability Issues

Article Preview

Abstract:

Nowadays manufacturing companies have to face conflicting issues continuously. Solving this type of problem means finding solutions that ensure a fair compromise between different objectives. In this work, a porthole die extrusion is considered as a specific case study. Usually, the main objective of this process is to find the combination of input parameters that allow the product quality to be maximized. However, product quality is not the only variable that companies have to take into account. In fact, it is also necessary to design the process in an efficient and sustainable way in order to reduce process cost and environmental impact. To this purpose, in this study the conflicting aims of product quality maximization and energy assumption minimization are considered and optimized. To pursue this aim an experimental investigation was executed, in order to build a preliminary database. The decision variables are the profile thickness and the process velocity. During the tests, the punch was measured in order to quantify the absorbed power along with the environmental impact of the process for changing conditions. In the same way, the mechanical properties of the extruded profile were measured by means of a tensile test, in order to assess the product quality. To solve this kind of problem the use of multi-objective optimization techniques is required in order to find the set of Pareto optimal solutions from which a single configuration will be selected according to specific business needs.

You might also be interested in these eBooks

Info:

Periodical:

Key Engineering Materials (Volumes 622-623)

Pages:

79-86

Citation:

Online since:

September 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Joost R. Duflou, John W. Sutherland, David Dornfeld, Christoph Herrmann, Jack Jeswiet, Sami Kara, Michael Hauschild, Karel Kellens, Towards energy and resource efficient manufacturing: A processes and systems approach, CIRP Annals - Manufacturing Technology. 61 (2012).

DOI: 10.1016/j.cirp.2012.05.002

Google Scholar

[2] A. Abele, R. Anderl, H. Birkhofer, Environmentally-Friendly Product Development - Methods and Tools, Springer, Darmstadt, (2005).

DOI: 10.1007/b138604

Google Scholar

[3] M. Overcash, J. Twomey, J. Isaacs, Manufacturing Unit Process Life Cycle Inventories (UPLCI), Permis'09, Gaithersburg, 30-31 (2009).

DOI: 10.1145/1865909.1865916

Google Scholar

[4] S. Kara, W. Li, Unit Process Energy Consumption Models for Material Removal Processes, CIRP Annals – Manufacturing Technology. 60 (2011) 37-40.

DOI: 10.1016/j.cirp.2011.03.018

Google Scholar

[5] J. Jeswiet, S. Kara, Carbon emissions and CESTM in manufacturing, CIRP Annals - Manufacturing Technology. 57 (2008) 17-20.

DOI: 10.1016/j.cirp.2008.03.117

Google Scholar

[6] G. Ingarao, G. Ambrogio, F. Gagliardi, R. Di Lorenzo, A sustainability point of view on sheet metal forming operations: material wasting and energy consumption in incremental forming and stamping processes, Journal of Cleaner Production. 29-30 (2012).

DOI: 10.1016/j.jclepro.2012.01.012

Google Scholar

[7] Gasper Gantar, Tomaz Pepelnjak, Karl Kuzman, Optimization of sheet metal forming processes by the use of numerical simulations, Journal of Materials Processing Technology. 130–131 (2002) 54-59.

DOI: 10.1016/s0924-0136(02)00786-0

Google Scholar

[8] L. Donati, L. Tomesani, The prediction of seam welds quality in aluminum extrusion, Journal of Materials Processing Technology. 153–154 (2004) 366–373.

DOI: 10.1016/j.jmatprotec.2004.04.215

Google Scholar

[9] L. Li, H. Zhang, J. Zhou, J. Duszczyk, G. Y. Li, Z. H. Zhong, Numerical and experimental study on the extrusion through a porthole die to produce a hollow magnesium profile with longitudinal weld seams, Materials & Design. 29 (2008) 1190-1198.

DOI: 10.1016/j.matdes.2007.05.003

Google Scholar

[10] E. Ceretti, L. Fratini, F. Gagliardi, C. Giardini, A new approach to study material bonding in extrusion porthole dies, CIRP Annals - Manufacturing Technology. 58 (2009) 259-262.

DOI: 10.1016/j.cirp.2009.03.010

Google Scholar

[11] S. Kukkonen, J. Lampinen, Performance assessment of generalized differential evolution 3 with a given set of constrained multi-objective test problems, Evolutionary Computation, CEC'09. IEEE Congress, Trondheim (2009).

DOI: 10.1109/cec.2009.4983178

Google Scholar

[12] M. Caramia, P. Dell'Olmo, Multi-objective management in freight logistics: Increasing capacity, service level and safety with optimization algorithms, Springer, (2008).

DOI: 10.1007/978-3-030-50812-8_2

Google Scholar

[13] S. Kukkonen, J. Lampinen, GDE3: The third evolution step of generalized differential evolution, Evolutionary Computation, The 2005 IEEE Congress, Edinburgh (2005).

DOI: 10.1109/cec.2005.1554717

Google Scholar

[14] S. Ramesh, S. Kannan, S. Baskar, An improved generalized differential evolution algorithm for multi-objective reactive power dispatch, Engineering Optimization. 44 (2012) 391-405.

DOI: 10.1080/0305215x.2011.576761

Google Scholar

[15] K. Deb, R.B. Agrawal, Simulated binary crossover for continuous search space, Complex Systems. 9 (1994) 1-34.

Google Scholar

[16] K. Deb, S. Agrawal, A. Pratap, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, Lecture notes in computer science. 1917 (2000) 849-858.

DOI: 10.1007/3-540-45356-3_83

Google Scholar

[17] I.Y. Kim, O. L. De Weck, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation, Structural and multidisciplinary optimization. 29 (2005) 149-158.

DOI: 10.1007/s00158-004-0465-1

Google Scholar

[18] M. Laumanns, L. Thiele, E. Zitzler, An Adaptive Scheme to Generate the Pareto Front Based on the Epsilon-Constraint Method, Practical Approaches to Multi-Objective Optimization. 04461 (2005).

Google Scholar

[19] J. Heaton, Introduction to neural networks for Java, Heaton Research, (2008).

Google Scholar

[20] J.J. Durillo, A.J. Nebro, jMetal: A Java framework for multi-objective optimization, Advances in Engineering Software. 42 (2011) 760-771.

DOI: 10.1016/j.advengsoft.2011.05.014

Google Scholar