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Öğe Advance monitoring of hole machining operations via intelligent measurement systems: A critical review and future trends(Elsevier Sci Ltd, 2022) Binali, Rustem; Kuntoglu, Mustafa; Pimenov, Danil Yu.; Usca, Usame Ali; Gupta, Munish Kumar; Korkmaz, Mehmet ErdiThis review paper summarizes the application of smart manufacturing systems utilized in drilling and hole machining processes. In this perspective, prominent sensors such as vibration, cutting forces, temperature, current/power and sound used in the contemporary indirect and direct tool condition monitoring systems are handled one-by-one according to their applications during machining of holes. Thus, it is aimed to show several operations with the application stages and literature papers which utilize the sensorial data such as grinding, reaming, broaching, boring, tapping, drilling and countersinking. The novel side of this paper is summarizing the all-hole machining processes utilizing sensor systems while benefitting their predictive ability for improved machinability characteristics such as surface integrity, tool wear, dimensional accuracy, chip morphology.Öğe 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, MuratCutting 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.Öğe Development of lattice structure with selective laser melting process: A state of the art on properties, future trends and challenges(Elsevier Sci Ltd, 2022) Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Robak, Grzegorz; Moj, Kevin; Krolczyk, Grzegorz M.; Kuntoglu, MustafaLattice structures are vital for biological applications because of its numerous benefits (for example, faster and stronger binding to bone tissue). Consequently, processing of lattice structure is a particularly popular area of study currently. In this study, additive manufacturing technologies utilized in several engineering disciplines were collated and their merits and shortcomings were examined. Numerous sectors and disciplines view lattice structured additive manufacturing as a prototyping technique. In recent years, additive manufacturing tech-nology has also progressed toward the fabrication of useable final goods. The objective of this review is to classify the produced systems under the headings of aviation, automotive, and military technologies within the context of engineering and to compare them by examining the research and technology firms in this sector. In this cate-gorization, lattice-structured additive manufacturing techniques are categorized as an engineering production technology, and examples of this field are investigated. Technologies, which are examples of diverse engineering applications, are categorized under four primary headings: additive manufacturing knowledge, selective laser melting (SLM), lattice structure, and changeable porosity cellular structures.Öğe Hot Deformation Behavior and Strain Rate Sensitivity of 33MnCrB5 Boron Steel Using Material Constitutive Equations(Springer India, 2022) Teker, Emre; Danish, Mohd; Gupta, Munish Kumar; Kuntoglu, Mustafa; Korkmaz, Mehmet ErdiIn this paper, the constitutive equation parameters (Johnson-Cook parameters) of the 33MnCrB5 material were determined with the help of tensile tests. Initially, Johnson-Cook (JC) model was used for performing the simulations of the sample with finite element analysis with the help of ANSYS software. For these operations, the sample was first used at a certain temperature (24 degrees C) and low strain rates (10(-1), 10(-2), 10(-3) s(-1)) and quasi-static tensile tests were performed. Then, high temperature tensile tests were performed with strain rate values of 10(-3) s(-1) at temperatures of 300 degrees C, 600 degrees C, and 900 degrees C, respectively. Finally, JC parameters belonging to test materials were found in accordance with the results obtained from the high temperature tensile and quasi-static tests. In the last stage, the results obtained from the simulation software for the yield stress, maximum stress, and elongation values were compared with the experimental results. As a result, deviation values for quasi-static tests are calculated as 5.04% at yield stress, 5.57% at maximum stress, and 5.68% at elongation, while for high temperature, yield stress is 9.42%, maximum stress is 11.49% and the elongation value is 7.63%. The accuracy of JC parameters was verified with the comparison made with the obtained data.Öğe Indirect monitoring of machining characteristics via advanced sensor systems: a critical review(Springer London Ltd, 2022) Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Li, Zhixiong; Krolczyk, Grzegorz M.; Kuntoglu, Mustafa; Binali, Rustem; Yasar, NafizOn-line monitoring of the machining processes provides to detect the amount and type of tool wear which is critical for the determination of remaining useful lifetime of cutting tool. According to Industry 4.0 revolution, the machining performance in terms of cutting forces, surface roughness, power consumptions, tool wear, tool life, etc. needs to be automatically monitored because the unfavorable conditions in machining cause chatter vibrations, tool breakage, and dimensional accuracy. Therefore, the usage of advanced sensor systems plays a key role in achieving the improved machining characteristics in terms of less human effort, errors, production time, etc. and fulfills the requirement of Industry 4.0. Hence, this review presents the holistic knowledge of online detection systems including sensors and signal processing software preferred in mechanical machining operations. Initially, this paper is starting with the up-to-date literature introduction section followed by type of sensors used in machining, online detection methods in machining, challenges and suggestions, etc. Eventually, the article concluded the findings and future remarks especially focused on the theme of Industry 4.0. In the end, it is worthy to mention that this review paper is very helpful for researchers and academicians working in the industrial sectors.Öğe Machine learning models for online detection of wear and friction behaviour of biomedical graded stainless steel 316L under lubricating conditions(Springer London Ltd, 2023) Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Singh, Gurminder; Kuntoglu, Mustafa; Patange, Abhishek; Demirsoz, Recep; Ross, Nimel SwornaParticularly in sectors where mechanisation is increasing, there has been persistent effort to maximise the use of existing assets. Since maintenance management is accountable for the accessibility of assets, it stands to acquire prominence in this setting. One of the most common methods for keeping equipment in good working order is predictive maintenance with machine learning methods. Failures can be spotted before they cause any downtime or extra expenses, and with this aim, the present work deals with the online detection of wear and friction characteristics of stainless steel 316L under lubricating conditions with machine learning models. Wear rate and friction forces were taken into account as reaction parameters, and biomedical-graded stainless steel 316L was chosen as the work material. With more testing, the J48 method's accuracy improves to 100% in low wear conditions and 99.27% in heavy wear situations. In addition, the graphic showed the accuracy values for several models. The J48 model is the most precise amongst all others, with a value of 100% (minimum wear) and an average of 98.92% (higher wear). Amongst all the models tested under varying machining conditions, the J48's 98.92% (low wear) and 98.92% (high wear) recall scores stand out as very impressive (higher wear). In terms of F1-score, J48 performs better than any competing model at 99.45% (low wear) and 98.92% (higher wear). As a result, the J48 improves the model's overall performance.Öğe Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models(Elsevier Sci Ltd, 2023) Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Kuntoglu, Mustafa; Patange, Abhishek D.; Ross, Nimel Sworna; Yilmaz, Hakan; Chauhan, SumikaMachine learning has numerous advantages, especially in the rapid digitization of the manufacturing industry that combines data from manufacturing processes and quality measures. Predictive quality allows manufacturers to make informed predictions about the quality of their products by analyzing data gathered during production. The quality of the machining, the total cost and the computation time need to be improved using contemporary production processes. With this concern, a series of experiments were carried out on Bohler steel both in dry, Minimum Quantity Lubrication (MQL) and nano-MQL conditions in varying quantities to explore the tool wear. In comparison to dry conditions, the utilization of MQL in machining processes demonstrates significantly enhanced efficacy in mitigating flank wear. The reduction in flank wear ranges from around 5% to 20% to 25%, contingent upon the application of MQL on the flank face, rake face, or both faces simultaneously. After that, the results of the tests were evaluated with the models of machine learning (ML) to determine which environment was optimal for cutting under both real and artificial circumstances.Öğe A review on microstructure, mechanical behavior and post processing of additively manufactured Ni-based superalloys(Emerald Group Publishing Ltd, 2024) Kuntoglu, Mustafa; Salur, Emin; Gupta, Munish Kumar; Waqar, Saad; Szczotkarz, Natalia; Vashishtha, Govind; Korkmaz, Mehmet ErdiPurposeThe nickel-based alloys Inconel 625 and Inconel 718 stand out due to their high strength and corrosion resistance in important industries like aerospace, aviation and automotive. Even though they are widely used, current techniques of producing materials that are difficult to cut pose several problems from a financial, ecological and even health perspective. To handle these problems and acquire improved mechanical and structural qualities, laser powder bed fusion (LPBF) has been widely used as one of the most essential additive manufacturing techniques. The purpose of this article is to focus on the state of the art on LPBF parts of Inconel 625 and Inconel 718 for microstructure, mechanical behavior and postprocessing.Design/methodology/approachThe mechanical behavior of LPBF-fabricated Inconel is described, including hardness, surface morphology and wear, as well as the influence of fabrication orientation on surface quality, biocompatibility and resultant mechanical properties, particularly tensile strength, fatigue performance and tribological behaviors.FindingsThe postprocessing techniques such as thermal treatments, polishing techniques for surface enhancement, mechanical and laser-induced peening and physical operations are summarized.Originality/valueThe highlighted topic presents the critical aspects of the advantages and challenges of the LPBF parts produced by Inconel 718 and 625, which can be a guideline for manufacturers and academia in practical applications.Öğe A short review on thermal treatments of Titanium & Nickel based alloys processed by selective laser melting(Elsevier, 2022) Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Waqar, Saad; Kuntoglu, Mustafa; Krolczyk, Grzegorz M.; Maruda, Radoslaw W.; Pimenov, Danil Yu.Selective laser melting (SLM) has been considered as a well-researched technology used to produce near net shape metallic components with superior mechanical and physiochemical characteristics. Recently SLM has recently been used for the production of components made by high-performance alloys such as Titanium and Nickel. However, the microstructural and physical anisotropy along with the defects have negative impact on the performance and behavior of SLM-produced alloys. Therefore, to improve the material characteristics, extra attention must be paid to the source(s), identification, and removal of these deviations. Thermal heat treatments are often considered as an effective approach to deal with aforementioned issues. stress-relieving, quenching/hardening, annealing/ recrystallization, and thermo-mechanical heat treatments are among prominent procedures adopted for the post processing of SLM fabricated metallic components and hence considered in this article. Since these procedures have substantial impact on the microstructure and mechanical behavior therefore an assessment of their influence and resulting changes is significantly important. Therefore, this short review provides the said knowledge on thermal treatments used to improve the SLM fabricated titanium and nickel based alloys. This review can significantly help in designing and selecting appropriate post processing thermal treatments keeping in mind the expected outcome. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Öğe 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.
