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Classification and Detection Method for Discharges in Electrical Discharge Machining through Unsupervised Learning

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Abstract:

This paper aims to extend the evaluation of the process of electric discharge machining by analysing the discharges. Therefore, a method for detecting and classifying discharges was developed. To detect different discharge types, experiments were conducted with varying of technology parameters, such as peak current or duty factor. During the experiments, the voltages and the currents were measured via an oscilloscope. For the classification, an unsupervised machine learning method was applied, to cluster and classify the detected discharges and compare them with the measured material removal rate and the measured tool wear rate.

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Periodical:

Solid State Phenomena (Volume 391)

Pages:

43-53

DOI:

https://doi.org/10.4028/p-71ES2b

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Online since:

April 2026

Authors:

Philipp Damm*, Oliver Bederitzky, Jakob Vogel, Arthur Riefer, Philipp Plänitz, Christoph Lerez, Gunnar Meichsner, Matthias Hackert-Oschätzchen

Keywords:

Detection Method, Die Sinking EDM, Discharges, Machine Learning

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Creative Commons CC BY 4.0

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* - Corresponding Author

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