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Öğe A new approach of measurement and analysis of PVD - TiAlN coated carbide tools in machining of Monel 400 alloy under hybrid cooling conditions(Elsevier Sci Ltd, 2023) Ross, Nimel Sworna; Gopinath, C.; Sivaraman, V.; Ananth, M. Belsam Jeba; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Jamil, MuhammadThis research work focuses on the efficacy of minimum quantity lubrication (MQL) and cryogenic carbon dioxide (CO2) cooling in high-speed machining of nickel-based alloy. CO2 and MQL supplied in the rake face were compared to CO2 and MQL in turning of Monel 400. Tool wear, temperature, surface roughness, and micro -structural assessments were done to compute the cooling influence of distinct approaches. Energy dispersive spectrometry (EDS) mapping is employed to investigate the abrasion and adhesion mechanisms. The wear levels were reduced under the use of the hybrid approach; the decrease in flank wear value relative to the hybrid condition is 78%, 36%, and 27%, and in terms of crater wear, the reduction was 78%, 54%, and 48% over dry, MQL, and cryo CO2 conditions, respectively. Findings have portrayed that the lessening of temperature at the cutting area with the hybrid condition reduces the roughness by 58%, 44%, and 20% over dry, MQL, and cryo CO2 cutting strategies, respectively. Moreover, verdicts of the investigation confirm smaller grain size and high hardness under cryo cooling condition.Öğe 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.Öğe Tool wear patterns and their promoting mechanisms in hybrid cooling assisted machining of titanium Ti-3Al-2.5V/grade 9 alloy(Elsevier Sci Ltd, 2022) Gupta, Munish Kumar; Nieslony, P.; Sarikaya, Murat; Korkmaz, Mehmet Erdi; Kuntog, Mustafa; Krolczyk, G. M.; Jamil, MuhammadHybrid lubri-cooling is a latest technology that provides synergistic cooling and lubrication effect in the machining area especially in the cutting of titanium and its alloys. In this current study, cryogenic-LN2, minimum quantity lubrication (MQL), and hybrid cryogenic LN2-MQL are applied and compared against dry medium in perspective of in-depth analysis of tool flank wear, EDS mapping, and intensity of tool wear. Experimental results showed that in comparison with dry, hybrid LN2-MQL substantially reduced the tool flank and rake wear fol-lowed by LN2, MQL, and dry conditions, respectively. Additionally, the SEM and EDS analysis depicted relatively less severe wear and chemical elements adhesion on the tool's main cutting edge, while turning titanium alloy under a hybrid LN2-MQL lubri-cooling environment. In addition, the dry condition has maximum value of tool wear progressions i.e., 1.04 mm and hybrid LN2-MQL have 0.06 mm while machining titanium alloys. When tool wear is evaluated from a tribological point of view, the reduction in flank wear value compared to dry machining is 89.4 %, 92.3 % and 94.2 % owing to MQL, LN2, MQL and hybrid LN2-MQL cutting strategies. In terms of crater wear, the improvement was 87.7 %, 90.4 % and 90.8 % thanks to MQL, LN2, MQL and hybrid LN2-MQL.Öğe Tribological characteristics of additively manufactured 316 stainless steel against 100 cr6 alloy using deep learning(Elsevier Sci Ltd, 2023) Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Shibi, C. Sherin; Ross, Nimel Sworna; Singh, Gurminder; Demirsoz, Recep; Jamil, MuhammadUnder different working conditions, the tribological characteristics of materials show a complicated and nonlinear relation. As a result, it is crucial to advance tribology by prioritising a data-driven strategy to estimate service capability in order to expedite the material design and preparation. With this aim, the present work firstly deals with the implementation of novel deep learning technologies in predicting tribological characteristics of additively manufactured and casted 316 stainless steel against 100 cr6 alloy. The coefficient of friction and frictional forces data from ball-on-flat experiments were used to develop the different deep learning models i.e., CNN, CNN-LSTM, and ATTENTION based CNN. Then, the wear tracks of tested samples were analysed with the SEM analysis. According to the findings of the wear rate, the AM material wears with an average of 58% less intensity than the casted material. In addition, the performance of the CNN Attention model demonstrated higher levels of accuracy and lower loss metrics in comparison to the CNN and CNN-LSTM classifiers.
