Yazar "Gupta, Munish Kumar" seçeneğine göre listele

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    Performance evaluation of whisker-reinforced ceramic tools under nano-sized solid lubricants assisted MQL turning of Co-based Haynes 25 superalloy
    (Elsevier Sci Ltd, 2021) Sarikaya, Murat; Sirin, Senol; Yildirim, Cagri Vakkas; Kivak, Turgay; Gupta, Munish Kumar
    Ceramics are widely used in machining of high temperature alloys i.e., Co-based Haynes 25 alloy due to its superior characteristics. The present paper is focused on the performance of whisker-reinforced ceramic cutting tool (WRCCT) under nano-sized solid lubricants dispersed in MQL (nanofluid-MQL) during turning of Co-based Haynes 25 alloy. The turning experiments were performed under several cutting environments (dry, base fluid MQL (BF-MQL), hBN based nanofluid MQL (hBN-NMQL), MoS2 based nanofluid MQL (MoS2-NMQL), graphite based nanofluid MQL Gr-NMQL) by varying cutting speed (200 and 300 m/min) and feed rate (0.1 and 0.15 mm/ rev) values. Initially, the viscosity and thermal conductivity of nanofluids were evaluated and then the prepared nanofluids were used for machining experiments. The results reveal that the rate of increase in thermal conductivity coefficient relative to base cutting fluid was 11.90% in hBN-nanofluid, 16.29% in MoS2-nanofluid and 14.12% in Gr-nanofluid. In terms of machining performance, on the one hand, the minimum surface roughness was obtained from Gr-NMQL assisted machining, on the other hand, the hBN-NMQL has been successful in limiting of notch wear and nose wear values. Compared to dry turning, the temperature was reduced up to 27.18% with hBN doped nanofluids, while it was 34.95% with MoS2 doped nanofluids and 29.32% with graphene doped nanofluids.
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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.