A reliable turning process by the early use of a deep simulation model at several manufacturing stages
dc.contributor.author | Urbicain Pelayo, Gorka | |
dc.contributor.author | Álvarez, Álvaro | |
dc.contributor.author | López de Lacalle Marcaide, Luis Norberto | |
dc.contributor.author | Arsuaga Berrueta, Mikel | |
dc.contributor.author | Alonso, Miguel A. | |
dc.contributor.author | Veiga Suárez, Fernando | |
dc.date.accessioned | 2018-12-26T11:12:01Z | |
dc.date.available | 2018-12-26T11:12:01Z | |
dc.date.issued | 2017-05-02 | |
dc.identifier.citation | Machines 5(2) : (2017) // Article ID 15 | es_ES |
dc.identifier.issn | 2075-1702 | |
dc.identifier.uri | http://hdl.handle.net/10810/30566 | |
dc.description.abstract | The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy, and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on the equations describing the physical laws of the machining processes; however, additional efforts are needed to overcome the gap between scientific research and real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on "deep-knowledge and models" that aid machine designers, production engineers, or machinists to get the most out of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system and process as input data, and generates results that help in the proper decision-making and machining plan. Direct benefits can be found in (a) the fixture/ clamping optimal design; (b) the machine tool configuration; (c) the definition of chatter-free optimum cutting conditions and (d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper. | es_ES |
dc.description.sponsorship | The work presented in this paper was supported in some sections within the project entitled MuProD-Innovative Proactive Quality Control System for In-Process Multi-Stage Defect Reduction- of the Seventh Framework Program of the European Union [FoF.NMP.2011-5] and UPV/EHU under program UFI 11/29. Thanks are given to Tecnalia, for collaboration in testing, and especially to Ainhoa Gorrotxategi and Ander Jimenez for the sound work in the project. Thanks to Gamesa Eolica and Guruzpe, for the time, technical advices, and efforts during the analysis in examples. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/285075 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | simulation software | es_ES |
dc.subject | manufacturing systems | es_ES |
dc.subject | process integration | es_ES |
dc.subject | machining optimization | es_ES |
dc.subject | Industry 4.0 | es_ES |
dc.subject | knowledge-based manufacturing | es_ES |
dc.title | A reliable turning process by the early use of a deep simulation model at several manufacturing stages | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2017 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 ( http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.mdpi.com/2075-1702/5/2/15 | es_ES |
dc.identifier.doi | 10.3390/machines5020015 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ingeniería mecánica | es_ES |
dc.departamentoeu | Ingeniaritza mekanikoa | es_ES |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's license is described as © 2017 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 ( http://creativecommons.org/licenses/by/4.0/).