dc.contributor.author | Etxebeste , Mikel | |
dc.contributor.author | Ortiz-de-Zarate, Gorka | |
dc.contributor.author | ARRIETA, Iñaki Mirena | |
dc.contributor.author | ARRAZOLA, PEDRO JOSE | |
dc.date.accessioned | 2025-03-12T16:46:35Z | |
dc.date.available | 2025-03-12T16:46:35Z | |
dc.date.issued | 2025 | |
dc.identifier.issn | 1526-6125 | en |
dc.identifier.other | https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=180105 | en |
dc.identifier.uri | https://hdl.handle.net/20.500.11984/6917 | |
dc.description.abstract | Large cutting tools are widely used in sectors such as automotive, where complex shape aluminium components are machined at high cutting speeds, in a single clamping and in short cycle times with elevated Material Removal Rate (MRR). However, their relatively low stiffness and natural frequencies make chatter the primary productivity limitation. Developing optimised tools to overcome these limitations is often cost-prohibitive with current design methods. This paper presents a virtual design methodology for optimising large milling tools to mitigate chatter through topology optimisation and Finite Element Modal Analysis (FEMA). Topology optimisation enhanced tool dynamics, enabling chatter reduction under higher productivity conditions. An improved FEMA model was developed to accurately predict the modal parameters of the cutting tools, featuring a high-fidelity representation of the tool-holder clamping to the spindle. The predicted modal parameters enable cost-effective chatter prediction for tool design validations, minimising development and experimental costs. To validate the methodology, a prototype of the optimised tool was manufactured and tested through experimental modal analysis and machining tests, demonstrating significant productivity improvement in MRR compared to the initial design. | en |
dc.language.iso | eng | en |
dc.publisher | Elsevier | en |
dc.rights | © 2025 The Authors | en |
dc.title | A virtual design methodology to improve the dynamics and productivity of large milling tools | en |
dcterms.accessRights | http://purl.org/coar/access_right/c_f1cf | en |
dcterms.source | Journal of Manufacturing Processes | en |
local.contributor.group | Mecanizado de alto rendimiento | es |
local.description.peerreviewed | false | en |
local.identifier.doi | https://doi.org/10.1016/j.jmapro.2025.01.024 | en |
local.embargo.enddate | 2027-01-31 | |
local.source.details | Vol. 134. Pp. 1096-1113. January, 2025 | en |
oaire.format.mimetype | application/pdf | en |
oaire.file | $DSPACE\assetstore | en |
oaire.resourceType | http://purl.org/coar/resource_type/c_6501 | en |
oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | en |
oaire.funderName | Gobierno Vasco | en |
oaire.funderName | Gobierno Vasco | en |
oaire.funderName | Gobierno de España | en |
oaire.funderIdentifier | https://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086 | en |
oaire.funderIdentifier | https://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086 | en |
oaire.funderIdentifier | https://ror.org/038jjxj40 / http://data.crossref.org/fundingdata/funder/10.13039/501100010198 | |
oaire.fundingStream | Elkartek 2024 | en |
oaire.fundingStream | Programa de apoyo a la I+D Empresarial Hazitek 2022 | en |
oaire.fundingStream | Proyectos de Generación de Conocimiento y a actuaciones para la formación de personal investigador predoctoral | en |
oaire.awardNumber | KK-2024-00005 | en |
oaire.awardNumber | ZL-2022-00741 | en |
oaire.awardNumber | PID2022-139655OB-I00 | en |
oaire.awardTitle | Nueva generación de procesos para la (re)fabricación sostenible (ORLEGI) | en |
oaire.awardTitle | EVMACH | en |
oaire.awardTitle | Diseño a medida de la integridad superficial de los componentes mecanizados para mejorar su durabilidad en aplicaciones de salud y Aeronáuticas (TAILORSURF) | en |
oaire.awardURI | Sin información | en |
oaire.awardURI | Sin información | en |
oaire.awardURI | Sin información | en |