Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool
dc.contributor.author | Pérez-Salinas, Cristian Fabian | |
dc.contributor.author | Del Olmo Sanz, Ander | |
dc.contributor.author | López de Lacalle Marcaide, Luis Norberto | |
dc.date.accessioned | 2022-08-12T08:28:06Z | |
dc.date.available | 2022-08-12T08:28:06Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Materials 15(15) : (2022) // Article ID 5135 | es_ES |
dc.identifier.issn | 1996-1944 | |
dc.identifier.uri | http://hdl.handle.net/10810/57301 | |
dc.description.abstract | In 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.sponsorship | This 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.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/958357 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2019-109340RB-I00 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PDC2021-121792-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | cutting edge micro geometry | es_ES |
dc.subject | edge preparation | es_ES |
dc.subject | drag finishing | es_ES |
dc.subject | broaching tool | es_ES |
dc.subject | R&R analysis | es_ES |
dc.subject | prediction ANN | es_ES |
dc.title | Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-08-11T11:51:07Z | |
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.publisherversion | https://www.mdpi.com/1996-1944/15/15/5135 | es_ES |
dc.identifier.doi | 10.3390/ma15155135 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ingeniería mecánica | |
dc.departamentoeu | Ingeniaritza mekanikoa |
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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/).