  Machine learning offers remarkable benefits for improving workplaces and
working conditions amongst others in the recycling industry. Here e.g.
hand-sorting of medium value scrap is labor intensive and requires experienced
and skilled workers. On the one hand, they have to be highly concentrated for
making proper readings and analyses of the material, but on the other hand,
this work is monotonous. Therefore, a machine learning approach is proposed for
a quick and reliable automated identification of alloys in the recycling
industry, while the mere scrap handling is regarded to be left in the hands of
the workers. To this end, a set of twelve tool and high-speed steels from the
field were selected to be identified by their spectrum induced by electric
arcs. For data acquisition, the optical emission spectrometer Thorlabs CCS 100
was used. Spectra have been post-processed to be fed into the supervised
machine learning algorithm. The development of the machine learning software is
conducted according to the steps of the VDI 2221 standard method. For
programming Python 3 as well as the python-library sklearn were used. By
systematic parameter variation, the appropriate machine learning algorithm was
selected and validated. Subsequent validation steps showed that the automated
identification process using a machine learning approach and the optical
emission spectrometry is applicable, reaching a maximum F1 score of 96.9 %.
This performance is as good as the performance of a highly trained worker using
visual grinding spark identification. The tests were based on a self-generated
set of 600 spectra per single alloy (7,200 spectra in total) which were
produced using an industry workshop device.
