Yazar "Krolczyk, Grzegorz M." seçeneğine göre listele

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    Performance and wear analysis in machining of Co-based Haynes 25/L605 superalloy using sustainable cooling/lubrication agencies
    (Elsevier, 2025) Sarikaya, Murat; Yildirim, cagri Vakkas; Sirin, Senol; Kara, Muhammed Ikbal; Sirin, Emine; Kivak, Turgay; Krolczyk, Grzegorz M.
    The cobalt-based Haynes 25 superalloy is a key material in sectors such as aerospace, medical, and energy, known for its outstanding high-temperature strength, wear and corrosion resistance. However, its low thermal conductivity and rapid work hardening rate make it inherently difficult to machine, highlighting the need for new cooling and lubrication methods. This work investigates the machinability of Haynes 25 under various sustainable cooling and lubrication techniques, including dry conditions, minimum quantity lubrication (MQL), nanofluids, and cryogenic COQ. Additionally, hybrid systems combining cryogenic COQ with nanofluids are also being investigated. The effectiveness of these approaches was ascertained by thorough investigations of surface roughness, cutting temperature, tool wear, and its mechanisms, and power consumption. Experimental results show that hybrid cooling systems especially those including nanofluids and cryogenic COQ significantly improve machining performance. Compared to dry machining, these methods minimized tool wear by 38 % and achieved up to a 44 % reduction in cutting temperature and a 32 % reduction in power usage. These results were a result of the enhanced thermal and tribological characteristics of nanofluids along with COQ's fast cooling capacity. This work provides a route toward sustainable and high-performance manufacture of challenging-to-machine materials by highlighting the possibilities of hybrid cooling strategies to maximize machining efficiency, extend tool life, and lower environmental impact.
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    Real-time monitoring and measurement of energy characteristics in sustainable machining of titanium alloys
    (Elsevier Sci Ltd, 2024) Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Yilmaz, Hakan; Sirin, Senol; Ross, Nimel Sworna; Jamil, Muhammad; Krolczyk, Grzegorz M.
    The development of cutting-edge monitoring technologies such as embedded devices and sensors has become necessary to ensure an industrial intelligence in modern manufacturing by recording machine, process, tool, and energy consumption conditions. Similarly, machine learning based real time systems are popular in the context of Industry 4.0 and are generally used for predicting energy needs and improving energy utilization efficiency and performance. In addition, sustainable and energy-efficient machining technologies that can reduce energy consumption and associated negative environmental effects have been the latest topic of much study in recent years. Concerning this regard, the present work firstly deals with the real time monitoring and measurement of energy characteristics while machining titanium alloys under dry, minimum quantity lubrication (MQL), liquid nitrogen (LN2) and hybrid (MQL + LN2) conditions. The energy characteristics at different stages of machine tools were monitored with the help of a high end energy analyser. Then, the energy signals from each stage of machining operation were predicted and classified with the help of different machine learning (ML) models. The experimental results showed that MQL, LN2, and hybrid conditions decreased the total energy consumption by averagely 2.6 %, 17.0 %, and 16.3 %, respectively, compared to dry condition. The ML results demonstrated that the accuracy of the random forest (RF) approach obtained higher efficacy with 96.3 % in all four conditions. In addition, it has been noticed that the hybrid cooling conditions are helpful in reducing the energy consumption values at different stages.