Prikaz osnovnih podataka o disertaciji
Unapređenje performansi procesa u LEAN industrijskim sistemima primenom tehnoloških pilara Industrije 4.0
Improving process performance in LEAN industrial systems by using Industry 4.0 technological pillars
dc.contributor.advisor | Mačužić, Ivan | |
dc.contributor.other | Stefanović, Miladin | |
dc.contributor.other | Milovanović, Miloš | |
dc.contributor.other | Đapan, Marko | |
dc.creator | Vukićević, Arso | |
dc.date.accessioned | 2021-10-28T09:36:41Z | |
dc.date.available | 2021-10-28T09:36:41Z | |
dc.date.issued | 2021-05-20 | |
dc.identifier.uri | http://eteze.kg.ac.rs/application/showtheses?thesesId=8297 | |
dc.identifier.uri | https://fedorakg.kg.ac.rs/fedora/get/o:1383/bdef:Content/download | |
dc.identifier.uri | https://nardus.mpn.gov.rs/handle/123456789/18676 | |
dc.description.abstract | Apstrakt: Termin Industrija 4.0 (I4.0) označava transformaciju globalne privrede pod uticajem tehnoloških dostignuća XXI veka. U okviru ove disertacije analizirane su mogućnosti primene tehnoloških pilara I4.0 za rešavanje reprezentativnih problema (model zona) u LEAN industrijskim sistemima. U prvoj studiji predložen je okvir za upravljanje nebezbednim uslovima i nebezbednim postupcima primenom mobilnih i internet tehnologija. Takođe, izvršena je analiza zahteva i predloženo je rešenje prilagođeno za mala i srednja preduzeća. U drugoj studiji slučaja predloženo je rešenje za unapređenje dimenzione kontrole ekstrudovanih gumenih profila. Rešenje je bazirano na upotrebi digitalne kamere i algoritama kompjuterske geometrije i obrade slika – čime je postignuto je poboljšanje performansi operatera od ~6%. U trećoj studiji slučaja predložen je sistem za popis materijala koji se transportuju na paletama u štamparskoj industriji. Digitalizovani popis pokazao se kao neuporedivo brži i pouzdaniji od trenutnog-ručnog popisa skladišta. U četvrtoj studiji slučaja predloženo je rešenje za analizu neergonomskog transporta tereta ručnim kolicima. Upotrebom IoT modula detektuju se povećane vrednosti sile kojom radnik deluje, što omogućava analizu odgovarajućih frejmova snimljenih internet kamerom. Identifikacijom kritičnih momenata, u kojima algoritmi dubokog učenja omogućavaju trodimenzionalni prikaz položaja tela operatera, omogućena je preciznija analiza ergonomskih rizika. Validacija predloženih algoritama i rešenja ukazuje na veliki potencijal za njihovu širu praktičnu primenu. Zaključeno je da postoji veliki broj specifičnih problema u industriji koji zahtevaju namenska rešenja – što otvara velike mogućnosti za dalja istraživanja i razvoj digitalizovanih rešenja baziranih na pilarima I4.0. | sr |
dc.description.abstract | Abstract: The term Industry 4.0 (I4.0) refers to the transformation of the global economy under the technological advances of XXI century. The aim of this dissertation was to assess possibilities for applying I4.0 technological pillars for solving various representative problems (model zones) of LEAN industrial systems. In the first case study, a framework for managing unsafe conditions and unsafe acts by using mobile and internet technologies was proposed. Moreover, an analysis of requirements and аdaptation of the proposed solution for use in small and medium enterprises was performed. In the second case study, a solution for the dimensional inspection of extruded rubber profiles was proposed. The solution is based on the use of a digital camera and algorithms for computational geometry and image processing – which improved operators’ performances by ~ 6%. In the third case study, the system for the inventory of materials that are transported on pallets in the printing industry was proposed. The digitized inventory has shown as significantly faster and robust compared to the currentmanual inventory practice. In the fourth case study, a solution for the detection and analysis of non-ergonomic cargo transport by handcart was proposed. By using an IoT module, increased values of the force exerted by the worker were detected, which enabled the analysis of corresponding frames acquired by the IP camera. Identification of critical moments, by using Deep learning algorithms for three-dimensional assessment of the operator's body pose, enables a more precise analysis of ergonomic risks. Validation of developed algorithms and solutions indicated a high potential for wider applications. It was concluded that there are a number of specific problems in the industry that require dedicated solutions - which opens up opportunities for further research and development of digitalized solutions based on the I4.0 pillars. | en |
dc.format | application/pdf | |
dc.language | sr | |
dc.publisher | Универзитет у Крагујевцу, Факултет инжењерских наука | sr |
dc.rights | openAccess | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Универзитет у Крагујевцу | sr |
dc.subject | Industrija 4.0 | sr |
dc.subject | Industry 4.0 | en |
dc.subject | Occupational health and safety | en |
dc.subject | Quality control | en |
dc.subject | Logistics | en |
dc.subject | Machine vision | en |
dc.subject | Internet of Things | en |
dc.subject | Bezbednost i zdravlje na radu | sr |
dc.subject | Kontrola kvaliteta | sr |
dc.subject | Logistika | sr |
dc.subject | Mašinska vizija | sr |
dc.subject | Internet stvari | sr |
dc.title | Unapređenje performansi procesa u LEAN industrijskim sistemima primenom tehnoloških pilara Industrije 4.0 | sr |
dc.title.alternative | Improving process performance in LEAN industrial systems by using Industry 4.0 technological pillars | en |
dc.type | doctoralThesis | |
dc.rights.license | BY | |
dcterms.abstract | Мачужић, Иван; Ђапан, Марко; Миловановић, Милош; Стефановић, Миладин; Вукићевић, Aрсо; Унапређење перформанси процеса у ЛЕAН индустријским системима применом технолошких пилара Индустрије 4.0; Унапређење перформанси процеса у ЛЕAН индустријским системима применом технолошких пилара Индустрије 4.0; | |
dc.identifier.fulltext | https://nardus.mpn.gov.rs/bitstream/id/77149/Arso_Vukicevi_Izvestaj_komisije_FIN.pdf | |
dc.identifier.fulltext | https://nardus.mpn.gov.rs/bitstream/id/77148/Doctoral_thesis_11560.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_nardus_18676 |