Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications

This study investigates the Electrical Discharge Machining (EDM) of zirconium diboride (ZrB2), a novel conductive ceramic with exceptional properties, including high temperature resistance, excellent thermal conductivity, and remarkable hardness. These properties make ZrB2 highly suitable for extrem...

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Detalles Bibliográficos
Autores: Luis Pérez, Carmelo Javier, Torres Salcedo, Alexia, Puertas Arbizu, Ignacio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/53978
Acceso en línea:https://hdl.handle.net/2454/53978
Access Level:acceso abierto
Palabra clave:ZrB2
EDM
Modeling
Artificial neural network
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spelling Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applicationsLuis Pérez, Carmelo JavierTorres Salcedo, AlexiaPuertas Arbizu, IgnacioZrB2EDMModelingArtificial neural networkThis study investigates the Electrical Discharge Machining (EDM) of zirconium diboride (ZrB2), a novel conductive ceramic with exceptional properties, including high temperature resistance, excellent thermal conductivity, and remarkable hardness. These properties make ZrB2 highly suitable for extreme environments, such as aerospace and nuclear applications. To the best of our knowledge, no comprehensive studies have addressed the manufacturing of ZrB2 parts by EDM, positioning this research as a cutting-edge contribution. Two electrode materials, graphite (C) and copper-graphite (Cu–C), were used to analyze the material removal rate (MRR) and surface roughness (Ra) as functions of current intensity (I), pulse time (ti), and duty cycle (η). Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) were used to model the response variables. While MLR was effective for MRR (R2 > 0.9), ANN outperformed it in predicting Ra, especially for Cu–C electrodes (R2 = 0.9366 vs. 0.3847 for MLR). Current intensity was the most influential parameter for MRR, while pulse time significantly affected Ra. Residual analysis confirmed ANN superior accuracy for Ra, with residuals below ±1 vs. ±2 for MLR. The study culminated in the successful EDM manufacture of a ZrB2 hexagonal nut, employing optimized parameters (I = 6 A, ti = 50 μs, η = 0.3, for the C electrode) derived using ANN models and particle swarm optimization. This result demonstrates the EDM process ability to produce high-precision components with complex geometries, showcasing its versatility and industrial potential. Therefore, this study broadens the understanding of ZrB2 machinability and expands its applications in advanced technologies.ElsevierIngenieríaIngeniaritzaInstitute for Advanced Materials and Mathematics - INAMAT22025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/53978reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2025 The Authors. This is an open access article under the CC BY-NC-ND license.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/539782026-06-17T12:41:47Z
dc.title.none.fl_str_mv Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
title Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
spellingShingle Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
Luis Pérez, Carmelo Javier
ZrB2
EDM
Modeling
Artificial neural network
title_short Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
title_full Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
title_fullStr Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
title_full_unstemmed Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
title_sort Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
dc.creator.none.fl_str_mv Luis Pérez, Carmelo Javier
Torres Salcedo, Alexia
Puertas Arbizu, Ignacio
author Luis Pérez, Carmelo Javier
author_facet Luis Pérez, Carmelo Javier
Torres Salcedo, Alexia
Puertas Arbizu, Ignacio
author_role author
author2 Torres Salcedo, Alexia
Puertas Arbizu, Ignacio
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería
Ingeniaritza
Institute for Advanced Materials and Mathematics - INAMAT2
dc.subject.none.fl_str_mv ZrB2
EDM
Modeling
Artificial neural network
topic ZrB2
EDM
Modeling
Artificial neural network
description This study investigates the Electrical Discharge Machining (EDM) of zirconium diboride (ZrB2), a novel conductive ceramic with exceptional properties, including high temperature resistance, excellent thermal conductivity, and remarkable hardness. These properties make ZrB2 highly suitable for extreme environments, such as aerospace and nuclear applications. To the best of our knowledge, no comprehensive studies have addressed the manufacturing of ZrB2 parts by EDM, positioning this research as a cutting-edge contribution. Two electrode materials, graphite (C) and copper-graphite (Cu–C), were used to analyze the material removal rate (MRR) and surface roughness (Ra) as functions of current intensity (I), pulse time (ti), and duty cycle (η). Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) were used to model the response variables. While MLR was effective for MRR (R2 > 0.9), ANN outperformed it in predicting Ra, especially for Cu–C electrodes (R2 = 0.9366 vs. 0.3847 for MLR). Current intensity was the most influential parameter for MRR, while pulse time significantly affected Ra. Residual analysis confirmed ANN superior accuracy for Ra, with residuals below ±1 vs. ±2 for MLR. The study culminated in the successful EDM manufacture of a ZrB2 hexagonal nut, employing optimized parameters (I = 6 A, ti = 50 μs, η = 0.3, for the C electrode) derived using ANN models and particle swarm optimization. This result demonstrates the EDM process ability to produce high-precision components with complex geometries, showcasing its versatility and industrial potential. Therefore, this study broadens the understanding of ZrB2 machinability and expands its applications in advanced technologies.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/53978
url https://hdl.handle.net/2454/53978
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv © 2025 The Authors. This is an open access article under the CC BY-NC-ND license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2025 The Authors. This is an open access article under the CC BY-NC-ND license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
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collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
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