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Experimental Investigation of Microstructure, Mechanical and Wear Characteristics of Cu-Ni/ZrC Composites Synthesized through Friction Stir Processing

Velmurugan T., Subramanian R., Priyadharshini G. Suganya, Raghu R.

Archives of Metallurgy and Materials

2020

Vol. 65, iss. 2

565--574

Friction Stir Process (FSP) was employed to develop Cupro-Nickel/Zirconium Carbide (Cu-Ni/ZrC) surface composites. Five different groove widths ranging from 0 to 1.4 mm were made in CuNi alloy plate to incorporate different ZrC volume fraction (0, 6, 12, 18 and 24 %) to study its influence on the structure and properties of Cu-Ni/ZrC composite. Processing was performed at a Tool Rotational Speed (TRS) of 1300 rpm, Tool Traverse Speed (TTS) of 40 mm/min with a constant axial load of 6 KN. The study is performed to analyse the influence of ZrC particles and the volume fraction of ZrC particles on the microstructural evolution, microhardness, mechanical properties, and tribological characteristics of the Cu-Ni/ZrC composite. The fracture and worn-out surfaces are analysed using Field Emission Scanning Electron Microscope (FESEM) to identify the fracture and wear mechanisms. The results demonstrated a simultaneous increase in microhardness and tensile strength of the developed composite because of grain refinement, uniform dispersion, and excellent bonding of ZrC with the matrix. Besides, the wear resistance increases with increase in volume fraction of ZrC particles in the composite. The surface morphology analysis revealed that the wear mechanism transits from severe wear regime to mild wear regime with increase in volume fraction of ZrC particles.

Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data

Manimaran J., Velmurugan T.

Bio-Algorithms and Med-Systems

2017

Vol. 13, no. 4

201--213

Background: Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms. Methods: Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyCon- TextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm. Results: This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm. Conclusions: The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.