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master's thesis
Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. urn:nbn:hr:235:570663
Šlankovič, Matej
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Šlankovič, M. (2023). Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija (Master's thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:570663

Šlankovič, Matej. "Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:235:570663

Šlankovič, Matej. "Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:235:570663

Šlankovič, M. (2023). 'Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija', Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 18 February 2025, https://urn.nsk.hr/urn:nbn:hr:235:570663

Šlankovič M. Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2023 [cited 2025 February 18] Available at: https://urn.nsk.hr/urn:nbn:hr:235:570663

M. Šlankovič, "Klasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija", Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:235:570663

Metadata
TitleKlasifikacija tvrdoće obratka kod bušenja kamena primjenom signala vibracija
Title (english)Workpiece hardness classification using vibration signals in stone drilling
AuthorMatej Šlankovič
MentorDanko Brezak (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2023-07-20, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Production Mechanical Engineering
Abstract
Kroz rad se istražuje mogućnost primjene neuronske mreže s radijalnim baznim funkcijama za klasifikaciju tvrdoće kamena na temelju signala vibracija snimljenih pri obradi bušenjem. U prvom dijelu rada iznosi se značaj i vrste nadzora obradnih procesa, što je dodatno prošireno dubljim pregledom karakteristika signala snimanih tijekom indirektnog nadzora. Na kraju teorijskog dijela, analizirani su model obradnog sustava s modulom za indirektni nadzor tvrdoće kamena i novodizajnirano svrdlo s kanalom za ispuhivanje. Nakon teorijskog dijela, detaljno je prikazan provedeni eksperiment. Redom su opisani: eksperimentalni postav, korištene vrste kamenih uzoraka, mjerenje i obrada signala, te izdvajanje značajki. Zatim je iznesena struktura, matematički model i principi rada neuronske mreže s radijalnim baznim funkcijama. Naposljetku su prikazani rezultati provedenog eksperimenta te je iznesen konačan zaključak o primjenjivosti mreže za klasifikaciju tvrdoće.
Abstract (english)
The paper explores the possibility of applying the radial basis function neural network for the stone hardness classification based on vibration signals measured during drilling process. In the first part of the paper, the importance and types of monitoring of machining processes are presented. Which is additionally expanded by a deeper overview of indirectly monitored signals. At the end of the theoretical part, the model of machining system with the module for indirect stone hardness monitoring and the newly designed drill bit with a channel that allows the flow of cutting fluids were analyzed. After the theoretical part, the conducted experiment is thoroughly described in the following order: the experimental setup, used types of stone samples, measurement and processing of signals, and feature extraction. Subsequently followed by explanation of the structure, mathematical model and operating principles of the radial basis function neural network. Final chapter presents the results of conducted experiment and concluding remarks of applicability of neural network for hardness classification.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:570663
Study programmeTitle: Mechanical Engineering; specializations in: Design, Process and Energy Engineering, Production Engineering, Engineering Modelling and Computer Simulation, Marine Engineering, Industrial Engineering and Management, Materials Engineering, Mechatronics and Robotics, Autonomous Systems
Course: Autonomous Systems
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-07-05 18:52:15