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Fabrication and Characterization of Fibrous Polycaprolactone Blended with Natural Green Tea Extracts Using Dual Solvent Systems

Mohd Rashid Ainatul Aqilah Binti, Jaganathan Saravana Kumar, Mohd Khudzari Ahmad Zahran, Ismail Ahmad Fauzi

Autex Research Journal

2022

Vol. 22, no. 4

404--410

Nanofibrous dressings serve as an impeccable candidate in the management of wounds. Nanofibrous composites composed of polycaprolactone (PCL) and green tea using dual solvent systems at different ratios were fabricated through electrospinning. Pure PCL electrospun fibers along with composites were characterized by using scanning electron microscopy (SEM), wettability, water uptake analysis, and Fourier transform infrared spectroscopy (FTIR). SEM indicated that fibrous morphology and the diameter of PCL/green tea were smaller for chloroform/ dimethylformamide (DMF) (601 nm) and acetone/DMF (896 nm) than the pure PCL (673 nm and 1,104 nm for chloroform/DMF and acetone/DMF, respectively). Wettability of the fabricated composites was increased, and pure PCL fibers were slightly more hydrophobic (100°) than PCL/green tea (94°). Water uptake of the composites was enhanced compared with PCL significantly in acetone/DMF. The PCL/green tea nanofibrous wound dressing with enhanced physicochemical properties serves as an indispensable candidate for wound healing applications.

A Frontier Statistical Approach Towards Online Tool Condition Monitoring and Optimization for Dry Turning Operation of SAE 1015 Steel

Moganapriya Chinnasamy, Rajasekar Rathanasamy, Gobinath Velu Kaliyannan, Prabhakaran Paramasivam, Jaganathan Saravana Kumar

Archives of Metallurgy and Materials

2021

Vol. 66, iss. 3

901--909

This research study intends to develop an online tool condition monitoring system and to examine scientifically the effect of machining parameters on quality measures during machining SAE 1015 steel. it is accomplished by adopting a novel microflown sound sensor which is capable of acquiring sound signals. The dry turning experiments were performed by employing uncoated, TiAlN, TiAlN/WC-C coated inserts. The optimal cutting conditions and their influence on flank wear were determined and predicted value has been validated through confirmation experiment. During machining, sound signals were acquired using Ni DAQ card and statistical analysis of raw data has been performed. Kurtosis and I-Kaz coefficient was determined systematically. The correlation between flank wear and I-Kaz coefficient was established, which fits into power-law curve. The neural network model was trained and developed with least error (3.7603e-5). It reveals that the developed neural network can be effectively utilized with minimal error for online monitoring.