Branko Tadic

Prof. Branko Tadic


Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2022 in Engineering Science and Technology, an International Journal
Verified
Journal Article
Open access
Sonja KostićSonja Kostić
Sonja Kostić
JMJasmina Miljojkovic
Jasmina Miljojkovic
GSGoran Simunovic
Goran Simunovic
DVDjordje Vukelic
Djordje Vukelic
BTBranko Tadic
Branko Tadic

11 February 2022 in Metals
Verified
Journal Article
Open access
PDF file
ZKZeljko Kanovic
Zeljko Kanovic
DVDjordje Vukelic
Djordje Vukelic
KSKatica Simunovic
Katica Simunovic
MPMiljana Prica
Miljana Prica
Tomislav ŠARIĆTomislav ŠARIĆ
Tomislav ŠARIĆ
GSGoran Simunovic
Goran Simunovic
Surface roughness is an important indicator of the quality of the machined surface. One of the methods that can be applied to improve surface roughness is ball burnishing. Ball burnishing is a finishing process in which a ball is rolled over the workpiece surface. Defining adequate input variables of the ball burnishing process to ensure obtaining the required surface roughness is a typical problem in scientific research. This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball. The experiments were conducted following a randomized full factorial design for different levels of input variables. The input variables included the initial arithmetic mean roughness (the initial surface roughness), the depth of ball penetration, the burnishing feed, and the burnishing ball diameter, while the output variable was the arithmetic mean roughness after ball burnishing (the final surface roughness). The surface roughness modeling was performed based on the experimental results, using regression analysis (RA), artificial neural network (ANN), and support vector regression (SVR). The regression model displayed large prediction errors at low surface roughness values (below 1 μm), but it proved to be reliable for higher roughness values. The ANN and SVR models have excellently predicted roughness across a range of input variables. Mean percentage error (MPE) during the experimental training research was 29.727%, 0.995%, and 1.592%, and MPE in the confirmation experiments was 34.534%, 1.559%, and 2.164%, for RA, ANN, and SVR, respectively. Based on the obtained MPEs, it can be concluded that the application of ANN and SVR was adequate for modeling the ball burnishing process and prediction of the roughness of the treated surface in terms of the possibility of practical application in real industrial conditions.

15 March 2019 in International Journal of Simulation Modelling
Verified
Journal Article
Open access
DVDjordje Vukelic
Djordje Vukelic
BABoris Agarski
Boris Agarski
IBI. Budak
I. Budak
GSGoran Simunovic
Goran Simunovic
BBBorut Buchmeister
Borut Buchmeister
BTBranko Tadic
Branko Tadic

30 September 2012 in International Journal of Precision Engineering and Manufacturing
Verified
Journal Article
Closed access
PDF file
BTBranko Tadic
Branko Tadic
BJBranislav Jeremic
Branislav Jeremic
PTPetar Todorovic
Petar Todorovic
DVDjordje Vukelic
Djordje Vukelic
UPUros Proso
Uros Proso
IBIgor Budak
Igor Budak

15 October 2022 in Tehnicki vjesnik - Technical Gazette
Verified
Journal Article
Closed access
PDF file
LBLjiljana Brzakovic
Ljiljana Brzakovic
VMVladimir Milovanovic
Vladimir Milovanovic
VKVladimir Kočović
Vladimir Kočović
GSGoran Simunovic
Goran Simunovic
DVDjordje Vukelic
Djordje Vukelic
BTBranko Tadic
Branko Tadic
The paper presents theoretical and experimental analyses of the kinetic friction coefficient of a ball bearing in conditions of rotational motion initiated by dynamic impact force. A method has been developed and a measurement system allows the measu...

10 March 2021 in International Journal of Simulation Modelling
Verified
Journal Article
Open access
DVDjordje Vukelic
Djordje Vukelic
ZKZeljko Kanovic
Zeljko Kanovic
Mario ŠokacMario Šokac
Mario Šokac
ŽSŽeljko Santoši
Željko Santoši
IBI. Budak
I. Budak
BTBranko Tadic
Branko Tadic