Прегледај по Аутор "Tabaković, Slobodan"
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- СтавкаCalibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy(MDPI, 2024-06-18) Tabaković, Slobodan; Zeljković, Milan; Živanović, Saša; Budimir, Alexander; Dimić, Zoran; Košarac, AleksandarThe development of machine tools in the last twenty years includes, among other things, the application of mechanisms with a non-linear kinematic structure as the mechanical basis of machines. This results in significant improvements in kinematic characteristics and problems related to non-linear dependencies of the accuracy of the drive elements and the realization of movement in the machine’s external coordinates. The paper presents an approach to machine tool calibration based on the original O-X glide mechanism based on the ISO 230-4 standard with the mono- and bi-directional compensation of systematic errors and adaptation to the specifics of the mechanism’s kinematics. A machine tool prototype was designed and built for the research presented in the paper. The obtained results indicate the possibility of applying the existing recommendations and standards for testing the accuracy of machine tools with the need to correct the methodology by using linear and non-linear kinematic structures in machine tools.
- 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.
- СтавкаNeural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset(MDPI, 2022) Košarac, Aleksandar; Mlađenovic, Cvijetin; Zeljković, Milan; Tabaković, Slobodan; Knežev, MilošSurface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training
- 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.