Прегледај по Аутор "Tabaković, Slobodan"
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- СтавкаMachining Simulation and Verification of Tool Path for CNC Machine Tools with Serial and Hybrid Kinematics(Faculty of mechanical and civil engineering Kraljevo, University of Kragujevac, 2017) Zivanović, Saša; Tabaković, Slobodan; Zeljković, Milan; Mlađenović, Cvijetin; Košarac, Aleksandar; Gašić, MilomirDevelopment of modern machine tools basically is directed on improvement of kinematic structures and exploitation characteristics. As a result of this in last two decades industry more and more uses machine tools based on parallel and hybrid kinematics with significant increasing of speed of main and movement and feederate. In order to provide more efficient exploitations of these machines, reduce of the preparation time of production, increase of the safety of users and machines is necessary application of software for simulation and verification programs. This paper presents the concept of modern technological preparing of manufacturing in the case of the definition of virtual machine tools based on conventional - serial and hybrid kinematics, simulation of machining and verification of programs for machining of characteristic workpiece.
- СтавкаNext-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining(MDPI, 2023-11-15) Košarac, Aleksandar; Tabaković, Slobodan; Mlađenović, Cvijetin; Zeljković, Milan; Orašanin, Goran; Liang, Steven Y.Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this research explores the machining parameters optimization minimizing surface roughness in milling titanium alloy Ti-6Al-4V. A full factorial design involving four factors (cutting speed, feed rate, depth of cut, and the cooling/lubricating method), each having three levels, implies the 81 experimental runs. Using the Taguchi method, the number of experimental runs was reduced from 81 to 27 through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. The second part explores the possibilities of using different ML techniques to create a predictive model for average surface roughness using the previously created small datasets. The paper presents a comparative analysis of several commonly used techniques for handling small datasets and regression problems. The best results indicate that the widely used machine learning algorithm Random Forest excels in handling regression problems and small datasets.