Optimal window size for the extraction of features for tool wear estimation Casusol A.J. es_PE Zegarra F.C. es_PE Vargas-Machuca J. es_PE Coronado A.M. es_PE 2024-05-30T23:13:38Z 2024-05-30T23:13:38Z 2021
dc.description This work was funded by CONCYTEC-FONDECYT in the framework of call E038-01, grant No. 020-2019-FONDECYT-BM-INC.INV.
dc.description.abstract Prediction of machine tool wear is highly dependent on the quality of the measured data and the ability to extract information from such raw data. These data are presented in the form of time series, which cannot be used directly by conventional machine learning algorithms, such as the one used in this work. To link the raw data and the learning algorithm, it is first necessary to extract a feature set from the time series. An important but little analyzed aspect is the size of the window required for feature extraction. If this window is too small, not much information will be obtained, on the other hand, if the window is too large, there will be more chance of outliers and other irregularities of the data being introduced. In the present work, we use a novel database corresponding to machine tool wear to demonstrate the impact of window size. An optimally chosen window size, plus an adequate feature extraction, allows us to obtain results comparable to the state of the art, i.e., median scores of 89 %, which are comparable to that obtained by the first place of the recently held data challenge. © 2021 IEEE.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.scopus 2-s2.0-85116228625
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
dc.rights info:eu-repo/semantics/openAccess
dc.subject SVR
dc.subject CNC milling machine es_PE
dc.subject feature engineering es_PE
dc.subject hyperparameter optimization es_PE
dc.title Optimal window size for the extraction of features for tool wear estimation
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication