ANALIZA I PRIMENA OPC UA ZA DIGITALNU TRANSFORMACIJU MAŠINE ZA BRIZGANJE PLASTIKE

Milica Milić

doi:10.24867/34BE08Milic

Elektrotehničko i računarsko inženjerstvo

God. 41 Br. 02 (2026): Зборник радова Факултета техничких наука

ANALIZA I PRIMENA OPC UA ZA DIGITALNU TRANSFORMACIJU MAŠINE ZA BRIZGANJE PLASTIKE

Milica Milić

34BE08Milic.pdf

DOI:: https://doi.org/10.24867/34BE08Milic
Predato: February 12, 2026
Objavljeno: 2026-03-09

Apstrakt

U okviru ovog rada obrađena je primena OPC UA specifikacije u industriji prerade plastike, kako bi se implementiralo proizvodno postrojenje u skladu sa karakteristikama postavljenim Industrijom 4.0. Tema rada obuhvata konfigurisanje OPC UA servera primenom EUROMAP 77 informacionog modela, kao i testiranje bezbednosnih aspekata OPC UA na primeru mašine za brizganje plastike. Predstavljene su ključne prednosti OPC UA u odnosu na klasičnu implementaciju proizvodnog postrojenja.

Reference

[1] M. Schleipen, H. S. Gilani, T. Bischoff, and J. Pfrommer, ‘OPC UA & Industrie 4.0 - Enabling Technology with High Diversity and Variability’, Procedia CIRP, vol. 57, pp. 315–320, 12 2016.
[2] A. Martins, B. M. L. Silva, H. Costelha, C. Neves, J. Lyons, and J. Cosgrove, ‘An approach to integrating manufacturing data from legacy Injection Moulding Machines using OPC UA’, 09 2021.
[3] F. Tayalati, I. Boukrouh, A. Azmani, and M. Azmani, ‘Implementation of Digital Twin and Deep Learning for Process Monitoring: Case Study in Injection Molding Manufacturing’, 08 2024, p. 8.
[4] M. R. Ristanović, ‘Arhitektura sistema industrijske automatike’, Mašinski fakultet Univerziteta u Beogradu, 2020, p. 3–4.
[5] O. Antons, J. C. Arlinghaus, ‘Distributed control for industry 4.0 - a comparative simulation study’, IFAC-PapersOnLine 54 (1) (2021) 516–521, 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021.

[1] [1] M. Schleipen, H. S. Gilani, T. Bischoff, and J. Pfrommer, ‘OPC UA & Industrie 4.0 - Enabling Technology with High Diversity and Variability’, Procedia CIRP, vol. 57, pp. 315–320, 12 2016.

[2] [2] A. Martins, B. M. L. Silva, H. Costelha, C. Neves, J. Lyons, and J. Cosgrove, ‘An approach to integrating manufacturing data from legacy Injection Moulding Machines using OPC UA’, 09 2021.

[3] [3] F. Tayalati, I. Boukrouh, A. Azmani, and M. Azmani, ‘Implementation of Digital Twin and Deep Learning for Process Monitoring: Case Study in Injection Molding Manufacturing’, 08 2024, p. 8.

[4] [4] M. R. Ristanović, ‘Arhitektura sistema industrijske automatike’, Mašinski fakultet Univerziteta u Beogradu, 2020, p. 3–4.

[5] [5] O. Antons, J. C. Arlinghaus, ‘Distributed control for industry 4.0 - a comparative simulation study’, IFAC-PapersOnLine 54 (1) (2021) 516–521, 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021.