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Effects of machining parameters on spectral entropy of acoustic emission signals in the electro erosion

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Abstract

Understanding and optimizing mechanical manufacturing processes is essential for sustainable industrial development. Among unconventional machining methods, electrical discharge machining (EDM) distinguishes itself by its capability to remove material through successive electrical discharges submerged in a dielectric fluid. EDM encompasses intricate phenomena influenced by machine parameters, dielectric choice, and the materials involved. Unlike conventional machining, EDM operates with the tool electrode in close proximity to, but not in physical contact with, the workpiece, achieving material removal through localized overheating. This study focuses on monitoring EDM phenomena during the machining of AISI H13 steel, exploring variations in machining parameters and electrode materials (electrolytic copper and graphite). Acoustic emission (AE) signals and machine learning (ML) are employed for experimental characterization and data analysis. Spectral entropy is applied to AE signals, quantifying inherent signal uncertainty. The findings reveal remarkable accuracy (97.7%) and underscore the superior control achieved with graphite electrodes in managing machining phenomena compared to electrolytic copper electrodes.

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Acknowledgements

The authors would like to acknowledge (i) CAPES and (ii) PUC Minas, especially PROPPG Mecânica, for their continuous support of research and development (R&D), crucial for technological development, and these work achievements.

Funding

This research is funded by Pontifical Catholic University of Minas Gerais and CAPES.

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Authors and Affiliations

  1. Mechanical Engineering Graduate Program, PUC Minas, Belo Horizonte, MG, Brazil

    Samuel Soares Ferreira & Luís Henrique Andrade Maia

  2. PUCPR, Mechanical Engineering Graduate Program, Curitiba, PR, Brazil

    Fred Lacerda Amorim

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  1. Samuel Soares Ferreira
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  3. Fred Lacerda Amorim
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Contributions

Conceptualization: S.S.F., F.L.A., L.H.A.M. Methodology: S.S.F., F.L.A., L.H.A.M. Software: S.S.F., L.H.A.M. Validation: S.S.F., F.L.A., L.H.A.M. Data curation: S.S.F., F.L.A., L.H.A.M. Writing-original draft preparation: S.S.F. Writing-review and editing: S.S.F., L.H.A.M. All authors have participated in the manuscript preparation and have read and agreed to the published version of the manuscript.

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Correspondence to Samuel Soares Ferreira.

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Highlights

• Analysis of acoustic emission (AE) signals contain rich information that provides insights into the phenomena and behaviors of a process.

• Electrical discharge machining (EDM) parameters significantly affect these phenomena, subsequently influencing AE signal responses.

• More aggressive parameters in EDM have lower process efficiency.

• Graphite electrode promotes better control of electrical sparks compared to electrolytic copper electrodes.

• Spectral entropy shows promise in characterizing the various phenomena observed in EDM, especially when considering the variation in parameters and electrode materials.

• Machine learning serves as a powerful tool for data analysis and predicting machining process responses.

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Ferreira, S.S., Maia, L.H.A. & Amorim, F.L. Effects of machining parameters on spectral entropy of acoustic emission signals in the electro erosion. Int J Adv Manuf Technol 131, 289–299 (2024). https://doi.org/10.1007/s00170-024-13129-2

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  • DOI: https://doi.org/10.1007/s00170-024-13129-2

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