Artificial intelligence-based process for metal scrap sorting
Maximilian Auer, Kai Osswald, Raphael Volz, Joerg Woidasky

TL;DR
This paper presents a machine learning-based method utilizing optical emission spectrometry for automated alloy identification in metal scrap sorting, achieving accuracy comparable to skilled human workers.
Contribution
It introduces a novel application of machine learning with optical emission spectra for fast, reliable alloy identification in recycling, following a standardized development process.
Findings
Maximum F1 score of 96.9% achieved
Validated method matches skilled worker performance
Developed with 7,200 spectra from industry device
Abstract
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…
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Taxonomy
TopicsMineral Processing and Grinding · Industrial Vision Systems and Defect Detection
