Прегледај по Аутор "Aleksić, Anđelko"

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    Industry 4.0 and Multi-tasking machining
    (SCIENTIFIC TECHNICAL UNION OF MECHANICAL ENGINEERING “INDUSTRY 4.0”, 2023) Sekulić, Milenko; Savković, Borislav; Aleksić, Anđelko; Košarac, Aleksandar; Moljević, Slaviša; Anić, Jelica; Popov, Georgi
    A clear trend of Industry 4.0 and smart manufacturing is to promote the increased use of multi-tasking machine tools. These machines are easily integrated into digital production systems and enable real-time data acquisition, remote monitoring and adaptive cutting processing. The integration of multi-tasking machine tools and digital manufacturing systems will improve the implementation of predictive maintenance, process optimization and production efficiency. Moreover, the development of robotics and AI along with the adoption of Industry 4.0 will increase the market share of these machine tools. The market demand is expected to increase further due to the rise of Industry 4.0. The paper analyzes the main advantages of multi-tasking machining, focusing on the advanced features of multi-tasking machine tools, as well as the latest trend of integrating additive manufacturing processes with CNC machining.
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    PREDICTING SURFACE ROUGHNESS IN THE MILLING OF BIOCOMPATIBLE ALLOY TI–6AL–4V
    (University Politehnica Timisoara, Faculty of Engineering Hunedoara, 2024-05) Babić, Igor; Šikuljak, Lana; Košarac, Aleksandar; Moljević, Slaviša; Sekulić, Milenko; Savković, Borislav; Aleksić, Anđelko; Kiss, Imre
    Mechanical engineering plays an essential role in the development of medical devices, implants, prostheses, and medical equipment, with precise machining of bio–compatible materials being of considerable importance. Among the various traditional and modern machining methods, milling stands out as a widely employed technique. This study is dedicated to optimizing milling parameters to achieve a minimal average surface roughness (Ra) during the milling of the biocompatible alloy Ti–6Al–4V. This paper presents the optimization of machining parameters: cutting speed, feed rate and depth of cut. Each parameter is explored at three distinct levels, resulting in the initial design of 27 experimental runs. The paper further demonstrates how an extensive set of experiments can be streamlined to a more manageable 9 runs through the application of the Taguchi methodology. This paper points out the significant impact that varying levels can exert on the obtained results, highlighting the importance of a comprehensive and systematic approach to experimentation and optimization.