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    Fiber-Reinforced Lightweight Calcium Aluminate Cement-Based Concrete: Effect of Exposure to Elevated Temperatures
    (Mdpi, 2023) Bideci, Ozlem Salli; Yilmaz, Hakan; Gencel, Osman; Bideci, Alper; Comak, Bekir; Nodehi, Mehrab; Ozbakkaloglu, Togay
    Calcium aluminate cements (CACs) are a group of rapid-hardening hydraulic binders with a higher aluminum composition and lower ecological footprint compared to their ordinary Portland cement (CEM) counterparts. CACs are commonly known to have higher thermo-durability properties but have previously been observed to experience a major strength loss over time when exposed to thermal and humidity conditions due to the chemical conversion of their natural hydrated products. To address this, in this study, silica fume is added to induce a different hydration phase path suggested by previous studies and utilized in conjunction with fiber-reinforced lightweight pumice to produce lightweight concrete. To closely evaluate the performance of the produced samples with CAC compared to CEM, two different types of cement (CEM and CAC) with different proportions of pumice and crushed stone aggregate at temperatures between 200 and 1000 degrees C were tested. In this context, sieve analysis, bulk density, flowability, compressive and flexural strength, ultrasonic pulse velocity and weight loss of the different mixes were determined. The results of this study point to the better mechanical properties of CAC samples produced with pumice aggregates (compared to crushed stone) when samples are exposed to high temperatures. As a result, it is found that CACs perform better than CEM samples with lightweight pumice at elevated temperatures, showing the suitability of producing lightweight thermal-resistant CAC-based concretes.
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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.