Yazar "Kuntoglu, Mustafa" seçeneğine göre listele

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    A review on conventional and advanced minimum quantity lubrication approaches on performance measures of grinding process
    (Springer London Ltd, 2021) Gupta, Munish Kumar; Khan, Aqib Mashood; Song, Qinghua; Liu, Zhanqiang; Khalid, Qazi Salman; Jamil, Muhammad; Kuntoglu, Mustafa
    Grinding is one of the important machining processes that are widely applied in precision manufacturing. In the beginning, studies mostly focused on dry machining. In time, emerging technologies have led to change in the development of the machining process. New techniques and tools have been developed over the last decade that has brought the process to an advanced place. At first, flood cooling has removed the burning problems in the grinding process. After that, a new technique was developed which is known as minimum quantity lubrication (MQL). This technique is a recognized opportunity to eliminate environmental concerns. This paper reviews some of the common as well as advanced MQL systems specifically used in grinding operations. The effect of MQL and other cutting parameters on cutting forces, surface roughness of the machined workpiece, tool wear, temperature, specific cutting energy, and residual stress is outlined. This paper also addressees the recent trend of cooling systems in the grinding process. After reading this research paper, one can easily get an overview of the previously conducted research to find the output parameter trends in MQL condition. The reader can infer from this paper in which direction the development trend in grinding is in the machining process.
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    A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
    (Springer London Ltd, 2021) Kuntoglu, Mustafa; Salur, Emin; Gupta, Munish Kumar; Sarikaya, Murat; Pimenov, Danil Yu
    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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    A state-of-the-art review on tool wear and surface integrity characteristics in machining of superalloys
    (Elsevier, 2021) Sarikaya, Murat; Gupta, Munish Kumar; Tomaz, Italo; Pimenov, Danil Yu; Kuntoglu, Mustafa; Khanna, Navneet; Yildirim, Cagri Vakkas
    Today, superalloys (also known as hard-to-cut materials) such as nickel, titanium and cobalt based cover a wide range of areas in engineering applications. At the same time, challenging material properties namely high strength and low thermal conductivity cause low quality in terms of cutting tool life and surface integrity of the machined part. It is important to improve the machinability of this type of materials by applying various methods in the perspective of sustainability. Therefore, current study presents surface integrity, tool wear characteristics and initiatives to improve them during the machining of superalloys. In this manner, it is outlined the surface integrity characteristics containing surface defects, surface roughness, microstructure alterations and mechanical properties. Also, tool wear mechanisms for example abrasive, adhesive, oxidation, diffusion and plastic deformation are investigated in the light of literature review. Finally, possible improvement options for tool wear and surface integrity depend on machining parameters, tool modifications, cooling methods and trade-off strategies are highlighted. The paper can be a guide for the researchers and manufacturers in the area of sustainable machining of hard-to-cut materials as explaining the latest trends and requirements. (C) 2021 CIRP.
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    Comparison of Tool Wear, Surface Morphology, Specific Cutting Energy and Cutting Temperature in Machining of Titanium Alloys Under Hybrid and Green Cooling Strategies
    (Korean Soc Precision Eng, 2023) Gupta, Munish Kumar; Nieslony, P.; Korkmaz, Mehmet Erdi; Kuntoglu, Mustafa; Krolczyk, G. M.; Guenay, Mustafa; Sarikaya, Murat
    Cutting energy must be reduced in order to make machining processes more eco-friendly. More energy was expended for the same amount of material removed, hence a higher specific cutting energy (SCE) implies inefficient material removal. Usually, the type of coolants or lubricants affects the SCE, or the amount of energy needed to cut a given volume of material. Therefore, the present work deals with a study of SCE in the turning of Ti-3Al-2.5V alloy under green cooling strategies. In spite of this, the research effort is also focused on the mechanism of tool wear, surface roughness, and cutting temperature under hybrid cooling, i.e., minimum quantity lubrication (MQL) and cryogenic. The tool wear rate, were explored with tool mapping analysis, and the results were compared with dry, MQL, and liquid nitrogen (LN2) conditions. The tool wear rate analysis claims that the dry condition causes more built up edge (BUE) formation. In addition, the hybrid cooling conditions are helpful in reducing the SCE while machining titanium alloys.
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    Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140
    (Mdpi, 2021) Kuntoglu, Mustafa; Acar, Osman; Gupta, Munish Kumar; Saglam, Haci; Sarikaya, Murat; Giasin, Khaled; Pimenov, Danil Yurievich
    The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi's S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.
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    Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy
    (Mdpi, 2021) Sap, Emine; Usca, Usame Ali; Gupta, Munish Kumar; Kuntoglu, Mustafa; Sarikaya, Murat; Pimenov, Danil Yurievich; Mia, Mozammel
    The features of composite materials such as production flexibility, lightness, and excellent strength put them in the class of materials that attract attention in various critical areas, i.e., aerospace, defense, automotive, and shipbuilding. However, the machining of composite materials displays challenges due to the difficulty in obtaining structural integrity. In this study, Cu/Mo-SiCP composite materials were produced by powder metallurgy with varied reinforcement ratios and then their machinability was investigated. In machinability experiments, the process parameters were selected as cutting speed (v(C)), feed rate (f), depth of cut (a(P)), and reinforcement ratio (R-R). Two levels of these parameters were taken as per the Taguchi's L8 orthogonal array, and response surface methodology (RSM) is employed for parametric optimization. As a result, the outcomes demonstrated that R-R = 5%, f = 0.25 mm/rev, a(P) = 0.25 mm, v(C) = 200 m/min for surface roughness, R-R = 0%, f = 0.25 mm/rev and a(P) = 0.25 mm and v(C) = 200 m/min for flank wear and R-R = 0%, f = 0.25 mm/rev, a(P) = 0.25 mm, v(C) = 150 m/min for cutting temperature for cutting temperature and flank wear should be selected for the desired results. In addition, ANOVA results indicate that reinforcement ratio is the dominant factor on all response parameters. Microscope images showed that the prominent failure modes on the cutting tool are flank wear, built up edge, and crater wear depending on reinforcement ratio.
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    Studies on Geometrical Features of Tool Wear and Other Important Machining Characteristics in Sustainable Turning of Aluminium Alloys
    (Korean Soc Precision Eng, 2023) Gupta, Munish Kumar; Nieslony, P.; Sarikaya, Murat; Korkmaz, Mehmet Erdi; Kuntoglu, Mustafa; Krolczyk, G. M.
    The aerospace and automotive industries make extensive use of aluminium and its alloys. Contrarily, machining of aluminium (Al) alloys presents a number of difficulties, including, but not limited to, poor surface finishing, excessive tool wear, decreased productivity etc. Therefore, it's very important to measure the machining characteristics during machining of aluminium alloy with sustainable cooling strategies. In this work, a new approach of measurement was adopted to measure the critical geometrical aspects of tool wear, surface roughness, power consumption and microhardness while machining AA2024-T351 alloy under dry, minimum quantity lubrication (MQL), liquid nitrogen (LN2) and carbon dioxide (CO2) cooling conditions. Initially, the various aspects of tool wear were studied with the help of Sensofar Confocal Microscope integrated with Mountains map software and then, the other results such as surface roughness, power consumption and microhardness were measured as per the ISO standards. The outcome of these measurement studies confirms that LN2 and CO2 cooling is helpful in improving the machining characteristics of AA2024-T351 alloy. When compared to dry conditions, the surface roughness values of MQL, LN2, and CO2 all have values that are lowered by 11.90%, 30.95%, and 39.28% respectively, and also power consumption values were lowered by 3.11%, 6.46% and 11.5% for MQL, CO2 and LN2 conditions, respectively.