Abstract
A promising approach to the rapid inspection of inclusions in wrought aluminium alloys is optical emission spectroscopy (OES). However, in order to separate the peaks corresponding to particular inclusions from the peaks obtained from various microstructural features in the matrix, an advanced filtering of the OES spectrum is necessary. The methodology developed in this work is based on big-data-driven predictions of whether an on-line analysed sample is good or bad. A sufficient amount of relevant data, necessary for data-driven predictions, was established by the systematic quality control of samples of AA6082 using optical and scanning electron microscopy and by analysing the same surface using OES. By following a machine-learning process, an algorithm was developed to enable the on-line division of the samples into good and bad, based on criteria received from the casting house. Although the obtained results are promising, further improvements are necessary before this method can be validated for use in regular production.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Böhlen J. M. et al. (2011) Advances in the ultra-fast inclusion analysis in steel by spark-OES. Paper presented at the 8th International Workshop on Progress in Analytical Chemistry and Materials Characterization in Steel and Metal Industries, Luxemburg, Belgium, 17–19 May 2011
Böhlen J. M., Yellepeddi R. (2009) Combined quantitative analysis and ultra-fast analysis of nonmetallic inclusions by optical emission spectrometry. Millennium Steel 167–171
Li K. et al. (2010) Analysis of inclusions in steel and aluminum with the ARL iSpark Spark-DAT – Recent improvements. Paper presented at the CCATM 2010 Conference and Exhibition on Analysis and Testing of Materials, Beijing, China, 12–15 September 2010
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 The Minerals, Metals & Materials Society
About this paper
Cite this paper
Kevorkijan, V., Šustar, T., Lesjak, I., Degiampietro, M., Langus, J. (2019). Optical Emission Spectrometry (OES) Data-Driven Inspection of Inclusions in Wrought Aluminium Alloys. In: Chesonis, C. (eds) Light Metals 2019. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-05864-7_118
Download citation
DOI: https://doi.org/10.1007/978-3-030-05864-7_118
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05863-0
Online ISBN: 978-3-030-05864-7
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)