Yazar "Binali, Rustem" seçeneğine göre listele

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    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 Erdi
    This 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.
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    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, Nafiz
    On-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.