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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorDuo, Aitor
dc.contributor.authorBasagoiti Astigarraga, Rosa
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.authorAperribay Zubia, Javier
dc.descriptionTwo 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.abstractThis 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.sponsorshipGobierno Vascoes
dc.relationDuo, 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.
dc.rights© Los autoresen
dc.titleDrilling test data from new and worn bitsen
local.contributor.groupAnálisis de datos y ciberseguridades
local.contributor.groupMecanizado de alto rendimientoes
local.relation.projectIDGV/Elkartek 2017/KK-2017-00021/CAPV/Máquinas y procesos Smart a través de la integración del conocimiento y los datos/SMAPROen

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