Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel
Pulsed Electrochemical Machining (PECM) is an advanced manufacturing method used in the metalworking industry to manufacture metal tools or components utilizing the principle of electrolysis. As a result, chemical and electrical phenomena of different natures are involved, making it difficult to de...
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| Format: | Article |
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Universidad De La Salle Bajío
2025-07-01
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| Series: | Nova Scientia |
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| Online Access: | https://novascientia.lasallebajio.edu.mx/ojs/index.php/novascientia/article/view/3629 |
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| author | Irvin Uriel Nopalera Angeles Everardo Efrén Granda Gutiérrez Roberto Alejo Eleuterio René Arnulfo García Hernández Ángel Hernández Castañeda María Guadalupe Pineda Arizmendi |
| author_facet | Irvin Uriel Nopalera Angeles Everardo Efrén Granda Gutiérrez Roberto Alejo Eleuterio René Arnulfo García Hernández Ángel Hernández Castañeda María Guadalupe Pineda Arizmendi |
| author_sort | Irvin Uriel Nopalera Angeles |
| collection | DOAJ |
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Pulsed Electrochemical Machining (PECM) is an advanced manufacturing method used in the metalworking industry to manufacture metal tools or components utilizing the principle of electrolysis. As a result, chemical and electrical phenomena of different natures are involved, making it difficult to describe the behavior that governs material wear accurately. An approach known as ANFIS (Adaptive Neuro Fuzzy Inference System) was employed to address this issue, integrating two artificial intelligence techniques: artificial neural networks and fuzzy logic. This algorithm performs inference-based mapping using fuzzy logic while adaptively adjusting parameters through a supervised criterion, as in neural networks. For this purpose, experimental data from electrochemical machining divided into subsets were used to train and test the model. In this way, a root mean square error in the prediction of the material removal rate of 0.394 and a coefficient of determination of 0.845 were obtained. Finally, the analysis of the residuals allowed to exclude the presence of heteroscedasticity, confirming an adequate predictive performance without bias and with constant variance according to the Breusch-Pagan statistical test criterion
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| format | Article |
| id | doaj-art-39a8994f1f2d40adbb9d62b8a80b25c1 |
| institution | Kabale University |
| issn | 2007-0705 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universidad De La Salle Bajío |
| record_format | Article |
| series | Nova Scientia |
| spelling | doaj-art-39a8994f1f2d40adbb9d62b8a80b25c12025-08-20T03:34:10ZengUniversidad De La Salle BajíoNova Scientia2007-07052025-07-01173410.21640/ns.v17i34.3629Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steelIrvin Uriel Nopalera Angeles0Everardo Efrén Granda Gutiérrez1https://orcid.org/0000-0002-9316-9627Roberto Alejo Eleuterio2https://orcid.org/0000-0002-7580-3305René Arnulfo García Hernández3https://orcid.org/0000-0001-7941-377XÁngel Hernández Castañeda4https://orcid.org/0000-0002-2811-0813María Guadalupe Pineda Arizmendi5https://orcid.org/0000-0001-8267-9800Universidad Autónoma del Estado de MéxicoUniversidad Autónoma del Estado de México Tecnológico Nacional de México Universidad Autónoma del Estado de México Universidad Autónoma del Estado de México Tecnológico Nacional de México Pulsed Electrochemical Machining (PECM) is an advanced manufacturing method used in the metalworking industry to manufacture metal tools or components utilizing the principle of electrolysis. As a result, chemical and electrical phenomena of different natures are involved, making it difficult to describe the behavior that governs material wear accurately. An approach known as ANFIS (Adaptive Neuro Fuzzy Inference System) was employed to address this issue, integrating two artificial intelligence techniques: artificial neural networks and fuzzy logic. This algorithm performs inference-based mapping using fuzzy logic while adaptively adjusting parameters through a supervised criterion, as in neural networks. For this purpose, experimental data from electrochemical machining divided into subsets were used to train and test the model. In this way, a root mean square error in the prediction of the material removal rate of 0.394 and a coefficient of determination of 0.845 were obtained. Finally, the analysis of the residuals allowed to exclude the presence of heteroscedasticity, confirming an adequate predictive performance without bias and with constant variance according to the Breusch-Pagan statistical test criterion https://novascientia.lasallebajio.edu.mx/ojs/index.php/novascientia/article/view/3629Neuro-fuzzyelectrochemical machiningmaterial removal ratemodeling |
| spellingShingle | Irvin Uriel Nopalera Angeles Everardo Efrén Granda Gutiérrez Roberto Alejo Eleuterio René Arnulfo García Hernández Ángel Hernández Castañeda María Guadalupe Pineda Arizmendi Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel Nova Scientia Neuro-fuzzy electrochemical machining material removal rate modeling |
| title | Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel |
| title_full | Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel |
| title_fullStr | Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel |
| title_full_unstemmed | Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel |
| title_short | Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel |
| title_sort | neuro fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel |
| topic | Neuro-fuzzy electrochemical machining material removal rate modeling |
| url | https://novascientia.lasallebajio.edu.mx/ojs/index.php/novascientia/article/view/3629 |
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