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Karakterizacija obrabe rezilnega orodja preko spremljanja vibracijskega odziva sistema
ID Korbar, Jure (Author), ID Čepon, Gregor (Mentor) More about this mentor... This link opens in a new window

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Abstract
Konsistentna izdelava komponent, ki izpolnjujejo visoke standarde kvalitete, zahteva visoko zanesljivost proizvodnega procesa. V primeru mehanskega odvzemanja materiala visoka zanesljivost zahteva spremljanje stanja obrabe orodja za odrezavanje, saj le–ta vpliva na kvaliteto površine obdelovanca. Preko spremljanja obrabe orodja lahko izvajamo proces prediktivnega vzdrževanja in s tem zmanjšamo izmet v fazi proizvodnega procesa. Velik potencial na tem področju predstavlja uporaba posrednih metod, saj je moč v realnem času detektirati stanje rezilnega orodja. Glavni izziv na področju razvoja posrednih metod merjenja obrabe orodja predstavlja karakterizacija povezave med obrabo in merjeno veličino. V sklopu obravnavanega dela smo razvili metodo posrednega merjenja obrabe preko spremljanja vibracijskega odziva orodja. Merjene signale smo v skladu z metodo MSSA (ang. Multi-channel Singular Spectrum Analysis) za razcep časovnih vrst razčlenili na posamezne komponente. Na osnovi izbranih cenilk porazdelitve MSSA komponent vibracijskega odziva smo z uporabo nevronskih mrež preko regresijskega pristopa in klasifikacije identificirali stopnjo obrabe rezilnega orodja. Pri tem smo pokazali, da cenilke parametrov porazdelitve MSSA komponent omogočajo natančnejšo identifikacijo obrabe kot cenilke merjenih signalov.

Language:Slovenian
Keywords:obraba orodja, vibracijski odziv, strojno učenje, MSSA, nevronske mreže, PVDF pospeškomer
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Place of publishing:Ljubljana
Publisher:[J. Korbar]
Year:2022
Number of pages:XXVI, 58 str.
PID:20.500.12556/RUL-139006 This link opens in a new window
UDC:539.375.6+621.9.021:004.032.26(043.2)
COBISS.SI-ID:119852803 This link opens in a new window
Publication date in RUL:29.08.2022
Views:1008
Downloads:161
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Secondary language

Language:English
Title:Characterisation of cutting tool wear by monitoring system vibration response
Abstract:
Consistent production of components meeting high-quality standards requires high production process reliability. In the case of mechanical material removal, high reliability requires monitoring the state of wear of the cutting tool, as it affects the surface quality of the workpiece. A predictive maintenance process can be implemented by monitoring tool wear in order to reduce the number of low-quality pieces. The use of indirect methods carries significant potential in this field, as it enables detecting the cutting tool's condition in real-time. The main challenge in developing indirect methods for monitoring tool wear is characterizing the relationship between wear and the measured quantity. In this work, we developed a method of indirect wear measurement by monitoring the tool's vibration response. The measured signals were split into individual components by the MSSA (Multi-channel Singular Spectrum Analysis) method for time series decomposition. Based on selected estimators of the distribution of MSSA components of the vibration response, the degree of wear of the cutting tool was identified using neural networks through a regression approach and classification. It was demonstrated that the distribution parameter estimators of MSSA components enable more accurate identification of wear than estimators of the measured signals.

Keywords:tool wear, vibration response, machine learning, MSSA, neural networks, PVDF accelerometer

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