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RISS 인기검색어
- 검색키워드
- 저자 : K. Rajeswari (검색결과 3 건)
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Binder burnout and sintering kinetic study of alumina ceramics shaped using methylcellulose
K. Rajeswari,S. Chaitanya,P. Biswas,M. Buchi Suresh,Y.S. Rao,Roy Johnson 한양대학교 세라믹연구소 2015 Journal of Ceramic Processing Research Vol.16 No.1
Ceramic components are generally processed by the techniques such as compaction, extrusion, injection molding, casting etc., and the selection of forming method is generally based on the complexity of the shapes. Additives such as binders, plasticizers, surfactants and lubricants, which are generally organic in nature, play a significant role to ensure the flowability of the mass which is critical in shaping of ceramics. Alumina (α-Al2O3) powder was subjected to torque rheometric studies to identify Critical Volume Binder Concentrations (CBVC) corresponding to specific processing regimes. An initial torque value of 3-4Nm for compaction followed by 6-7N-m for extrusion and 2-3N-m for casting regimes were observed. Viscosities of the pre-mixes prepared by the addition of methyl cellulose (C6H7O2 (OH)x(OCH3)y, (where x = 1.0-1.5 and y = 2-1.45) as a binder were also found to exhibit a similar trend in viscosity, corresponding to CBVC torque values and are shaped into green specimens. Green strength of the standard specimens (45 × 4 × 3 mm) was estimated through 3-point bend tests and exhibited a good correlation with the binder content. Binder burnout characteristics were also elucidated by TG/DSC technique and activation energy estimated is 75-110 kJ/mol for the thermal degradation of methylcellulose binder. Activation energy of 883 ± 45 kJ/mol was estimated through kinetic analysis of sintering by the construction of the Master Sintering Curves (MSC).
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Diagnosis of Non-invasive Glucose Monitoring by Integrating IoT and Machine Learning
V. K. R. Rajeswari Satuluri,Vijayakumar Ponnusamy 대한전자공학회 2022 IEIE Transactions on Smart Processing & Computing Vol.11 No.6
Diabetes Mellitus (DM) is a term collectively used for all types of diabetes. DM increases the risk factor for health complications if not treated early. The Internet of Things (IoT) and artificial intelligence (AI) in healthcare have become a huge benefit for managing DM. The selfsupervision of healthcare has become convenient because of IoT-enabled devices. This paper reviews the management of diabetes, such as invasive, non-invasive, and minimally invasive methods. Justification for the need for non-invasive monitoring of glucose is discussed. Different AI and IoT-enabled management for non-invasive diabetes are also briefed. This review aims at the type of machine learning algorithms applied to non-invasive glucose monitoring. The following are to be considered to achieve an effective non-invasive method of monitoring glucose: Near Infrared spectroscopy (NIR) and Machine learning algorithms(ML). IoT in glucose monitoring has empowered doctors and caretakers to deliver outstanding care. Self-care by every person has become essential, which can be achieved by handheld or wearable IoT devices. Using current technologies, the possibility of making a wearable to monitor the glucose level is becoming closer to reality and has enormous potential.
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