Drilling Projects by Tool Condition Monitoring System (TCMS)

In this paper, an online tool condition monitoring system (TCMS) for drilling is presented. The method is based on monitoring the spindle and feed motor currents. Root mean square (RMS) values of the spindle and feed motor currents, drill diameter, spindle speed and feed rate are the inputs to the n...

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Published in:Engineering Transactions
Main Authors: Justo GARCÍA-SANZ-CALCEDO, David R. SALGADO, Alfonso G. GONZÁLEZ
Format: Article
Language:English
Published: Institute of Fundamental Technological Research Polish Academy of Sciences 2016-12-01
Subjects:
Online Access:https://et.ippt.pan.pl/index.php/et/article/view/674
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author Justo GARCÍA-SANZ-CALCEDO
David R. SALGADO
Alfonso G. GONZÁLEZ
author_facet Justo GARCÍA-SANZ-CALCEDO
David R. SALGADO
Alfonso G. GONZÁLEZ
author_sort Justo GARCÍA-SANZ-CALCEDO
collection DOAJ
container_title Engineering Transactions
description In this paper, an online tool condition monitoring system (TCMS) for drilling is presented. The method is based on monitoring the spindle and feed motor currents. Root mean square (RMS) values of the spindle and feed motor currents, drill diameter, spindle speed and feed rate are the inputs to the network, and drill wear is the output. Drilling experiments were carried out over a wide range of cutting conditions to map the relationship between the input information and a tool wear. The performance and the architecture of the neural network have been validated with experiments, and a good agreement in an estimation of the tool condition was found. The results show that this method can be effectively used to verify and determine the tool wear.
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spelling doaj-e65d4cbcf096465e89b575c2c5181fa52025-11-03T00:46:33ZengInstitute of Fundamental Technological Research Polish Academy of SciencesEngineering Transactions0867-888X2450-80712016-12-0164410.24423/EngTrans.674.2016Drilling Projects by Tool Condition Monitoring System (TCMS)Justo GARCÍA-SANZ-CALCEDO0David R. SALGADO1Alfonso G. GONZÁLEZ2University of ExtremaduraUniversity of ExtremaduraUniversity of ExtremaduraIn this paper, an online tool condition monitoring system (TCMS) for drilling is presented. The method is based on monitoring the spindle and feed motor currents. Root mean square (RMS) values of the spindle and feed motor currents, drill diameter, spindle speed and feed rate are the inputs to the network, and drill wear is the output. Drilling experiments were carried out over a wide range of cutting conditions to map the relationship between the input information and a tool wear. The performance and the architecture of the neural network have been validated with experiments, and a good agreement in an estimation of the tool condition was found. The results show that this method can be effectively used to verify and determine the tool wear.https://et.ippt.pan.pl/index.php/et/article/view/674mechanical projectmonitoringdrilling projects
spellingShingle Justo GARCÍA-SANZ-CALCEDO
David R. SALGADO
Alfonso G. GONZÁLEZ
Drilling Projects by Tool Condition Monitoring System (TCMS)
mechanical project
monitoring
drilling projects
title Drilling Projects by Tool Condition Monitoring System (TCMS)
title_full Drilling Projects by Tool Condition Monitoring System (TCMS)
title_fullStr Drilling Projects by Tool Condition Monitoring System (TCMS)
title_full_unstemmed Drilling Projects by Tool Condition Monitoring System (TCMS)
title_short Drilling Projects by Tool Condition Monitoring System (TCMS)
title_sort drilling projects by tool condition monitoring system tcms
topic mechanical project
monitoring
drilling projects
url https://et.ippt.pan.pl/index.php/et/article/view/674
work_keys_str_mv AT justogarciasanzcalcedo drillingprojectsbytoolconditionmonitoringsystemtcms
AT davidrsalgado drillingprojectsbytoolconditionmonitoringsystemtcms
AT alfonsoggonzalez drillingprojectsbytoolconditionmonitoringsystemtcms