dc.rights.license | Attribution 4.0 International | * |
dc.contributor.author | Duo, Aitor | |
dc.contributor.author | Basagoiti, Rosa | |
dc.contributor.author | ARRAZOLA, PEDRO JOSE | |
dc.contributor.author | Aperribay Zubia, Javier | |
dc.contributor.author | CUESTA ZABALAJAUREGUI, MIKEL | |
dc.date.accessioned | 2019-07-24T10:54:07Z | |
dc.date.available | 2019-07-24T10:54:07Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11984/1477 | |
dc.description | Two different tool geometries were used, R204.6D and BH04.5D from toolmaker Kendu and the tests were carried out on a Lagun vertical machining centre. The material used was 35CrMo4LowS. A kistler rotational dynamometer was installed to acquire the feed force and cutting torque. Along with these signals were acquired other signals provided by the machine. For each of the tools used during the tests, 5 holes were drilled. For each tool geometry the cutting conditions were fixed while the parameter that was modified was the tool wear. Totally, the data consists of 90 csv files, each of them corresponds to a hole made at a certain level of tool wear. The signals that can be found in these files are the internal signals of the machine, the thrust force and the cutting torque. All signals were acquired at -Fs= 250 Hz; T=4 ms-. | en |
dc.description.abstract | This directory contains the raw data acquired by Mondragon Unibertsitatea during the execution of drilling tests. These data were used to obtain the results presented in the article "The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process" published in International Journal of Advanced Manufacturing Technology on January 2019.
Drilling is one of the most critical processes in the machining sector. This process is carried out in the last phases of the machining of a part, therefore, an almost finished part could be destroyed if the proper conditions are not met. Tool wear is one of the biggest problems in machining processes having an increasingly negative impact on the machined part. In order to carry out a study on the effect that tool wear has on the different signals acquired during the process and identify the sensitivity of each of the signals with respect to the others, drilling tests were carried out at different levels of tool wear. This document details the acquired signals and their organization in this directory. | en |
dc.description.sponsorship | Gobierno Vasco | es |
dc.language.iso | eng | en |
dc.relation | Duo, A., Basagoiti, R., Arrazola, P.J. et al. The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process. International Journal of Advanced Manufacturing Technology (2019), Volume 102, Issue 5–8, pp 2133–2146. https://doi.org/10.1007/s00170-019-03300-5 | en |
dc.relation.isreferencedby | https://hdl.handle.net/20.500.11984/1476 | en |
dc.rights | © Mondragon Goi Eskola Politeknikoa | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Drilling test data from new and worn bits | en |
dcterms.accessRights | http://purl.org/coar/access_right/c_abf2 | en |
local.contributor.group | Análisis de datos y ciberseguridad | es |
local.contributor.group | Mecanizado de alto rendimiento | es |
local.identifier.doi | https://doi.org/10.48764/myma-ys63 | |
local.relation.projectID | info:eu-repo/grantAgreement/GV/Elkartek 2017/KK-2017-00021/CAPV/Máquinas y procesos Smart a través de la integración del conocimiento y los datos/SMAPRO | en |
oaire.file | $DSPACE\assetstore | |
oaire.resourceType | http://purl.org/coar/resource_type/c_ddb1 | en |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | en |