Modelling the laser overageing treatment of a 6xxx Al alloy by means of physical simulation tests

Modelling the laser overageing treatment of a 6xxx Al alloy by means of physical simulation tests

Antonio Piccininni, Pasquale Guglielmi, Angela Cusanno, Gianfranco Palumbo

download PDF

Abstract. The local modification of the material properties is a promising strategy to broaden the range of applications for Aluminum (Al) alloys, since excessive thinning are avoided, and sound parts obtained. For example, by means of laser heating, the strain behaviour of age hardenable Al alloys can be locally affected to enhance the formability at room temperature. But predicting the properties modification (occurrence of overageing/solutioning) due to the local heating still needs investigations. In this work, short-term heat treatments on AA6063-T6 samples were conducted using the Gleeble system (able to subject the material to high heating rates combined with large temperature gradients). Wide ranges of temperature and time were thus explored, and the change of mechanical properties assessed by hardness tests. Experimental data were used to create a model and thus define the heating parameters able to bring the material to the overaged state or, alternatively, to the fully solutioned one.

Keywords
Laser Heat Treatment, Aluminium Alloys, Hardness

Published online 9/5/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Antonio Piccininni, Pasquale Guglielmi, Angela Cusanno, Gianfranco Palumbo, Modelling the laser overageing treatment of a 6xxx Al alloy by means of physical simulation tests, Materials Research Proceedings, Vol. 35, pp 62-69, 2023

DOI: https://doi.org/10.21741/9781644902714-8

The article was published as article 8 of the book Italian Manufacturing Association Conference

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] Mayer RM, Poulikakos LD, Lees AR, Heutschi K, Kalivoda MT, Soltic P. Reducing the environmental impact of road and rail vehicles. Environ Impact Assess Rev 2012;32:25-32. https://doi.org/10.1016/j.eiar.2011.02.001
[2] Hirsch J. Recent development in aluminium for automotive applications. Trans Nonferrous Met Soc China (English Ed 2014;24:1995-2002. https://doi.org/10.1016/S1003-6326(14)63305-7
[3] DIN EN 45545-2. Railway applications – Fire protection on railway vehicles – Part 2: Requirements for fire behaviour of materials and components 2016.
[4] Kalpakjian S, Schmid SR. Manufacturing processes for engineering materials. Singapore; London: Pearson Education; 2017.
[5] Geiger M, Merklein M, Vogt U. Aluminum tailored heat treated blanks. Prod Eng 2009;3:401-10. https://doi.org/10.1007/s11740-009-0179-8
[6] Piccininni A, Palumbo G. Design and optimization of the local laser treatment to improve the formability of age hardenable aluminium alloys. Materials (Basel) 2020;13. https://doi.org/10.3390/ma13071576
[7] Kahrimanidis A, Lechner M, Degner J, Wortberg D, Merklein M. Process design of aluminum tailor heat treated blanks. Materials (Basel) 2015;8:8524-38. https://doi.org/10.3390/ma8125476
[8] Peixinho N, Soares D, Vilarinho C, Pereira P, Dimas D. Experimental study of impact energy absorption in aluminium square tubes with thermal triggers. Mater Res 2012;15:323-32. https://doi.org/10.1590/S1516-14392012005000011
[9] Piccininni A, Magrinho JP, Silva MB, Palumbo G. Formability Analysis of a Local Heat-Treated Aluminium Alloy Thin-Walled Tube. Springer International Publishing; 2021. https://doi.org/10.1007/978-3-030-75381-8_230
[10] Buchanan K, Colas K, Ribis J, Lopez A, Garnier J. Analysis of the metastable precipitates in peak-hardness aged Al-Mg-Si(-Cu) alloys with differing Si contents. Acta Mater 2017;132:209-21. https://doi.org/10.1016/j.actamat.2017.04.037
[11] Sekhar AP, Nandy S, Ray KK, Das D. Prediction of Aging Kinetics and Yield Strength of 6063 Alloy. J Mater Eng Perform 2019;28:2764-78. https://doi.org/10.1007/s11665-019-04086-z
[12] Maalouf M. Logistic regression in data analysis: An overview. Int J Data Anal Tech Strateg 2011;3:281-99. https://doi.org/10.1504/IJDATS.2011.041335