2026-04-14T21:34:14Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/56212024-03-04T11:34:22Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Duo, Aitor author Basagoiti, Rosa author ARRAZOLA, PEDRO JOSE author CUESTA ZABALAJAUREGUI, MIKEL author 2021 Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can avoid costs resulting from that undesirable situation. This research work presents the results obtained in drilling process monitoring carried out on Inconel 718. Monitoring systems should be light and scalable. Following this idea, multiple sensors for external signal acquisition are used in this work (cutting forces, vibrations, and acoustic emissions) and several machine internal signals are collected. The main objective is to evaluate the capacity of each acquisition source for the reconstruction of the tool wear curve and subsequently detection of tool breakage. Given the difficulty of using all of these signals in a real system, the methodology used to analyse the data makes it possible to have a comparative analysis of the potential of each of these sources for tool wear monitoring during the drilling process. The results indicate cutting forces whether they come from internal signals or external signals can carry out this task accurately. At the same time of data acquisition, detailed tool wear measurements were added. 1362-3052 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=166262 https://hdl.handle.net/20.500.11984/5621 Tool condition monitoring Inconel 718 Drilling Data mining Machine learning Sensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operation