Informatics Publishing Limited

V. Muthuraman ¹, R. Ramakrishnan ², L. Karthikeyan ³

Affiliations
1 Research Scholar, Mechanical Engg. Department Satyabama University, Jeppiaar Nagar, Chennai, - 600119, IN
2 Dept. of Sports Technology, Tamilnadu Physical Education and Sports University
3 Dept. of Mechanical Engg., Panimalar Engineering College, Chennai 600123, IN

Abstract

Wire electro discharge machining (WEDM) was applied to machine tungsten carbide-cobalt (WC-CO) metal matrix composite. It finds increasing application in auto, aeronautical and oil drill application. The material is extremely hard, brittle and tool grade. The present work is undertaken on 10% cobalt, 90% tungsten grade in which keen study is lacking. Amongst many variables, On-time, Off-time and ignition current were shortlisted as critical input parameters. The goal is to identify the input parameter level that maximizes the material removal rate. The input parameters for the experiments were selected after considering previous related work, manufacturer's catalog and industrial expert's opinion. From Taguchi L27 experimental plan, the Box-Behnken trials for 14 experiments were derived in order to model and to predict the output response with good accuracy. The investigation was carried out on the high speed Sodick machine so that it aligns with industry requirement of higher productivity. Response surface modeling and regression analysis was done. Analysis of variance was used to isolate parameters that are critical from pooled data. The experimental results were verified by running at recommended setting of the findings. The outcome was encouraging. Output response improves with on-time at 8 μ- Sec, off -time 15 μ- Sec and ignition current of 16 amperes. It was found that overall metal removal rates increased by 13%, to 17.42 mm3/min from the process average rate of 15.15 mm3/min. A mathematical equation was derived to predict performance. Surface, response contour plots were utilized to analyze performance. The validity of derived model was verified. Since error obtained was 4.5%, higher coefficient of determination is 87% and adequate precision was >4, the model is valid.

Keywords

WEDM, Material Removal Rate, WC-CO, Response Surface, Regression Analysis

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B. Senthilkumar ¹, R. Ramakrishnan ¹

Affiliations
1 National Institute of Epidemiology, Ayyapakkam, Chennai – 600 077, IN

Abstract

Hospital length of stay is an important performance indicator for hospital management and a key measure of efficiency in health care. However, it is difficult to analyze as most distributions pertaining to hospital length of stay are asymmetric due to outliers. The objective of the present study is to suggest a new method to estimate average length of stay (ALOS). Data on LOS in the year 2010 from a corporate hospital was considered for analysis. ALOS was calculated using generalized robust statistics method (GRSM) and other existing methods. The comparison of results shows that GRSM is a better alternative over other existing methods.

Keywords

Health Management, Hospital Stay, Health Care

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S. Uma Maheswari ¹, R. Ramakrishnan ²

Affiliations
1 Tamilnadu Physical Education and Sports University, Chennai, IN
2 Department of Advanced Sports Training & Technology, Tamilnadu Physical Education and Sports University, Chennai, IN

Abstract

Objectives: In order to achieve convenient sports video accessing without sequential scanning, automated sports video categorization is presented in this study. Methods/Analysis: In order to build efficient sports video categorizing system, edge features obtained from Non Subsampled Shearlet Transform (NSST) are taken into account. Then, sports genre categorization is done by Nearest Neighbor (NN) classifier due to its discriminative learning approach. The five sports category; tennis, cricket, volleyball, basketball and football are considered. Findings: To validate the proposed system based on NSST, experiments are carried using internal database video at frame level. Totally, 500 video clips are collected in which 100 video clips are gathered for each sports genre. The proposed system achieves maximum average classification accuracy of 94.80% at 4 directions of 2-scale NSST features while using city block distance measure in KNN classifier. For the same NSST features, the Euclidean, cosine and correlation distance measures gives an accuracy of 93.20%, 92.80% and 92% respectively. Conclusion/Application: The effectiveness of the system is clearly demonstrated by the experimental assessment. The proposed framework can adequately classify the sports video into one of the five predefined genre.

Keywords

Edge Features, Nearest Neighbor Classifier, NSST, Shearlet Transform, Sports Video Classification.

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R. Manikandan ¹, R. Ramakrishnan ²

Affiliations
1 Tamilnadu Physical Education and Sports University, Chennai, IN
2 Department of Advanced Sports Training and Technology, Tamilnadu Physical Education and Sports University, Chennai, IN

Abstract

Segmenting out foreground object from its background is an interesting and important research problem in the video based applications. It has more importance in the field of computer vision due to its applications such as sports, security systems, video surveillance, etc. The background subtraction algorithms are used to analyze the player‟s activity in sports, to improve the performance of player by detecting the motion of the players in video sequences. The various algorithms like frame difference, approximate median, mixture of gaussian are compared and analyzed with real time sports videos. Mixture of Gaussian turns out to be best in reliability of extraction of moving objects, robust to noise, whereas the conventional algorithms result in noise and poor extraction of objects. The parametric analyzes of metric such as recall, precision, etc., gives the complete behavior of player.

Keywords

Approximate Median, Frame Difference, Mixture of Gaussian, Motion Detection.

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R. Ramakrishnan ¹, L. Karunamoorthy ²

Affiliations
1 Dept. of Mechanical Engineering, Panimalar Engineering College, Chennai, IN
2 Dept. of Mechanical Engineering, Anna University, Chennai, IN

Abstract

This paper describes the development of a mathematical model and multi response optimization for predict and select the best cutting parameters of wire electro discharge machining (WEDM) process. To predict the performance characteristics namely material removal rate and surface roughness mathematical model using non-linear regression models were applied. AISI 02 Tool steel was selected as work material to conduct experiments. Experiments were planned as per Taguchi'sL16 orthogonal array. Each experiment has been performed using different cutting conditions of pulse on time, wire tension, delay time, wire feed speed, and ignition current. The responses were optimized concurrently using multi response signal to noise (MRSN) ratio in addition to Taguchi's traditional parametric design approach. Analysis of variance (ANOVA) was employed to identify the level of importance of the machining parameters on the multiple performance characteristics. Finally experimental confirmations were carried out to identify the effectiveness of this proposed method. A good improvement was obtained.

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R. Ramakrishnan ¹, L. Karunamoorthy ²

Affiliations
1 Dept. of Mechanical Engineering, EVP Engineering College, Chennai-602103, IN
2 Dept. of Mechanical Engineering, Anna University, Chennai-600025, IN

Abstract

In Wire electrical discharge machining (WEDM) process the removal of metal takes place due to successive sparks generated between the wire electrode and the gap of the work-piece. WEDM machines are widely applied to produce complex and intricate shapes of components in hard and conductive materials. In order to predict the performance of the WEDM process many researchers have attempted using various experimental parameters such as applied voltage, pulse on time, delay time, ignition current, dielectric pressure, wire tension, servo reference mean voltage, wire feed speed. However the results of these studies are limited to certain process parameters and materials. In WEDM process there is no direct contact between the toot electrode and work-piece. Therefore there is no shear stress exerted during machining. In this present work, the study and development of a surface roughness prediction model for machining of Oil Hardened Non Shrinkage (OHNS) steel, using response surface methodology (RSM) was developed. A five axis Robofil 290 CNC WEDM manufactured by Charmilles Technologies is applied to carry out experiments. Experiments have been planned according to Taguchi's L16 fractional factorial orthogonal array. A 0.25 mm diameter zinc coated brass wire was selected as toot electrode. A first order mathematical model for surface roughness has been developed, in terms of pulse on time, wire tension, delay time and ignition current intensity. The effect of these cutting parameters on the surface roughness has been carried out using design of experiments and RSM. In addition, the analysis of variance (ANOVA) is applied to identify the significance of the developed model. The test results confirm the validity and adequacy of the developed RSM model.

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