Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
Scopus
Toplam 119 atıf DOI
Sensors are the main equipment of the data-based enterprises for diagnosis of the health of system. Offering time- or frequency-dependent systemic information provides prognosis with the help of early-warning system using intelligent signal processing systems. Therefore, a chain of data-based information improves the efficiency especially focusing on the determination of remaining useful life of a machine or tool. A broad utilization of sensors in machining processes and artificial intelligence–supported data analysis and signal processing systems are prominent technological tools in the way of Industry 4.0. Therefore, this paper outlines the state of the art of the mentioned systems encountered in the open literature. As a result, existing studies using sensor systems including signal processing facilities in machining processes provide important contribution for error minimization and productivity maximization. However, there is a need for improved adaptive control systems for faster convergence and physical intervention in case of possible problems and failures. On the other hand, sensor fusion is an innovative new technology that makes decisions using multi-sensor information to determine tool status and predict system stability. It is currently not a fully accepted and practiced method. In a nutshell, despite their numerous advantages in terms of efficiency, time saving, and cost, the current situation of sensors used in the industry is not a sufficient level due to the investment cost and its increase with additional signal acquisition hardware and software equipment. Therefore, more studies that can contribute to the literature are needed.
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Atıf Yapan Yayın
Investigation of the Effects of Cooling and Lubricating Strategies on Tribological Characteristics in Machining of Hybrid Composites
Scopus
Havuzumuzda Open Access 58 atıf almış
Engineering materials are expected to contain physical and mechanical properties to meet the requirements and to improve the functionality according to their application area. In this direction, hybrid composites stand as an excellent option to fulfill these requests thanks to their production procedure. Despite the powder metallurgy method that allows for manufacturing products with high accuracy, machining operations are still required to obtain a final product. On the other hand, such materials are characterized with uncertainties in the structure and extremely hard reinforcement particles that aggravate the machinability. One of the prominent solutions for better machinability of composites is to use evolutionary cooling and lubricating strategies. This study focuses on the determination of tribological behavior of Cu-based, B-Ti-SiCP reinforced, about 5% wt. hybrid composites under milling of several environments, such as dry, minimum quantity lubrication (MQL)-assisted and cryogenic LN2-assisted. Comprehensive evaluation was carried out by considering tool wear, temperature, energy, surface roughness, surface texture and chips morphology as the machinability characteristics. The findings of this experimental research showed that cryogenic cooling improves the tribological conditions by reducing the cutting temperatures, flank wear tendency and required cutting energy. On the other hand, MQL based lubricating strategy provided the best tool wear index and surface characteristics, i.e., surface roughness and surface topography, which is related to spectacular ability in developing the friction conditions in the deformation zones. Therefore, this paper offers a novel milling strategy for Cu-based hybrid composites with the help of environmentally-friendly techniques.
Atıf Yapan Makale Bilgileri
Kurumlar (4)
Bingöl Üniversitesi
Bingol, Turkey
Inönü Üniversitesi
Malatya, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey
South Ural State University
Chelyabinsk, Russian Federation