prikaz prve stranice dokumenta Primjena metoda strojnog učenja u proizvodnim sustavima
  Download
PDF 4.1 MB
master's thesis
Primjena metoda strojnog učenja u proizvodnim sustavima
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2018. urn:nbn:hr:235:657226
De Marco, Marjam
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

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

Cite this document

De Marco, M. (2018). Primjena metoda strojnog učenja u proizvodnim sustavima (Master's thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:657226

De Marco, Marjam. "Primjena metoda strojnog učenja u proizvodnim sustavima." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2018. https://urn.nsk.hr/urn:nbn:hr:235:657226

De Marco, Marjam. "Primjena metoda strojnog učenja u proizvodnim sustavima." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2018. https://urn.nsk.hr/urn:nbn:hr:235:657226

De Marco, M. (2018). 'Primjena metoda strojnog učenja u proizvodnim sustavima', Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 25 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:657226

De Marco M. Primjena metoda strojnog učenja u proizvodnim sustavima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2018 [cited 2024 November 25] Available at: https://urn.nsk.hr/urn:nbn:hr:235:657226

M. De Marco, "Primjena metoda strojnog učenja u proizvodnim sustavima", Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:235:657226

Metadata
TitlePrimjena metoda strojnog učenja u proizvodnim sustavima
Title (english)Application of machine learning methods in production systems
AuthorMarjam De Marco
MentorDragutin Lisjak (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2018-12-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Production Mechanical Engineering
Abstract
Tema ovog diplomskog rada je primjena metoda strojnog učenja u linijskoj proizvodnji protupožarnih zaklopki i regulatora varijabilnog protoka za operaciju montaže. Primjena strojnog učenja i dubinske analize podataka postaje standard u svim aspektima proizvodnje, kako bi se otkrile skrivene informacije i znanje utkano u podacima, a uvelike pridonoseći procesu donošenja odluka i poslovanja. Cilj ovog rada je pronaći matematički model koji će prema kriteriju točnosti analizirati podatke te doprinijeti razumijevanju procesa montaže linijske proizvodnje. U prvom djelu rada opisana je teorija strojnog učenja, tehnike za dubinsku analizu podataka – klaster analiza, regresija i klasifikacija. U praktičnom dijelu rada, na podacima iz poslovnog sustava napravljena je sustavna analiza kao i priprema podataka. U cilju rješavanja navedenog problema, u radu su kreirani regresijski i klasifikacijski modeli te je napravljena klaster analiza sa svrhom optimizacije rezultata klasifikacijskog modela. U zaključnom dijelu rada dane su i smjernice za daljnja istraživanja.
Abstract (english)
The subject of this thesis is application of machine learning methods in line production of fire dampers and variable flow regulators for the assembly line. The use of machine learning and deep data analysis has become a standard in all aspects of production. Its use is to reveal hidden information and knowledge in the data which greatly contributes to the decision making process. The aim of this thesis is to find a mathematical model that will analyze the data according to the criterion of accuracy and contribute to the understanding of the line production assembly process. The first part of the paper describes the theory behind machine learning, the techniques for deep data analysis – cluster methods, regression and classification. In the practical part of the thesis, systematic analysis and data preparation were performed on the data from the business system In order to address this problem, regression and classification models were created. To optimize the classification model results a cluster analysis was used. Further research guidelines are noted in the conclusion.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:657226
Study programmeTitle: Mechanical Engineering
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 on2019-05-24 13:02:43