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dc.contributor.authorPérez-Salinas, Cristian Fabian ORCID
dc.contributor.authorDel Olmo Sanz, Ander ORCID
dc.contributor.authorLópez de Lacalle Marcaide, Luis Norberto ORCID
dc.date.accessioned2022-08-12T08:28:06Z
dc.date.available2022-08-12T08:28:06Z
dc.date.issued2022
dc.identifier.citationMaterials 15(15) : (2022) // Article ID 5135es_ES
dc.identifier.issn1996-1944
dc.identifier.urihttp://hdl.handle.net/10810/57301
dc.description.abstractIn recent years, cutting edge preparation became a topic of high interest in the manufacturing industry because of the important role it plays in the performance of the cutting tool. This paper describes the use of the drag finishing DF cutting edge preparation process on the cutting tool for the broaching process. The main process parameters were manipulated and analyzed, as well as their influence on the cutting edge rounding, material remove rate MRR, and surface quality/roughness (Ra, Rz). In parallel, a repeatability and reproducibility R&R analysis and cutting edge radius re prediction were performed using machine learning by an artificial neural network ANN. The results achieved indicate that the influencing factors on re, MRR, and roughness, in order of importance, are drag depth, drag time, mixing percentage, and grain size, respectively. The reproducibility accuracy of re is reliable compared to traditional processes, such as brushing and blasting. The prediction accuracy of the re of preparation with ANN is observed in the low training and prediction errors 1.22% and 0.77%, respectively, evidencing the effectiveness of the algorithm. Finally, it is demonstrated that the DF has reliable feasibility in the application of edge preparation on broaching tools under controlled conditions.es_ES
dc.description.sponsorshipThis research was funded by Basque government group IT 1573-22 and the Ministry of Mineco Grant PID2019-109340RB-I00 and PDC2021-121792-I00 funded by MCIN/AEI/10.13039/501100011033. Thanks, are also due to European commission by H2020 project n. 958357 InterQ Interlinked Process, Product and Data Quality framework for Zero-Defects Manufacturing. Experiments were performed by help of project (QUOLINK TED2021-130044B-I00) Ministerio de Ciencia e Innovación 2021.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/958357es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2019-109340RB-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PDC2021-121792-I00es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es/
dc.subjectcutting edge micro geometryes_ES
dc.subjectedge preparationes_ES
dc.subjectdrag finishinges_ES
dc.subjectbroaching tooles_ES
dc.subjectR&R analysises_ES
dc.subjectprediction ANNes_ES
dc.titleEstimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tooles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2022-08-11T11:51:07Z
dc.rights.holder© 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/1996-1944/15/15/5135es_ES
dc.identifier.doi10.3390/ma15155135
dc.contributor.funderEuropean Commission
dc.departamentoesIngeniería mecánica
dc.departamentoeuIngeniaritza mekanikoa


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© 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Except where otherwise noted, this item's license is described as © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).