Qualification: 
Ph.D, M.E
s_saravana@cb.amrita.edu

Dr. Saravanamurugan S. currently serves as Assistant Professor at the Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. He received his Ph. D. in Mechanical Engineering from Anna University in 2016. His dissertation was titled "Chatter Control in Machining Processes using Passive Vibration Absorbers". His areas of research include Vibration Analysis and Control, Machining dynamics and Finite Element Method.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

S. Saravanamurugan, Thiyagu, S., Sakthivel, N. R., and Nair, B. B., “Chatter prediction in boring process using machine learning technique”, International Journal of Manufacturing Research, vol. 12, pp. 405-422, 2017.[Abstract]


Chatter is the main reason behind the failure of any part in the machining centre and lowers the productivity. Chatter occurs as a dynamic interaction between the tool and the work piece resulting in poor surface finish, high-pitch noise and premature tool failure. In this paper, the chatter prediction is done by active method by considering the parameters like spindle speed, depth of cut, feed rate and including the dynamics of both the tool and the workpiece. The vibration signals are acquired using an accelerometer in a closed environment. From the acquired signals discrete wavelet transformation (DWT), features are extracted and classified into three different patterns (stable, transition and chatter) using support vector machine (SVM). The classified results are validated using surface roughness values (Ra). Copyright © 2017 Inderscience Enterprises Ltd

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2016

Journal Article

Dr. Sakthivel N.R., Saravanamurugan, S., Nair, B. B., Dr. Elangovan M., and Sugumaran, V., “Effect of Kernel Function in Support Vector Machine for the Fault Diagnosis of Pump”, Journal of Engineering Science and Technology, vol. 11, pp. 826–838, 2016.[Abstract]


Pumps are widely used in a variety of applications. Defects and breakdown of these pumps will result in significant economic loss. Therefore, these must be under continuous observation. In various applications, the role of pump is decisive and condition monitoring is crucial. A completely automated on-line pump condition monitoring system which can automatically inform the operator of any faults, promising reduction in maintenance cost with a greater productivity saving both time and money.This paper presents the application of support vector machine for classification using statistical features extracted from vibration signals under good and faulty conditions of a pump. Effectiveness of various kernel functions of C-SVC and -SVC models are compared. The study gives some empirical guidelines for selecting an appropriate kernel in a classification problem.

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2015

Journal Article

S. Saravanamurugan, Alwarsamy, T., and Devarajan, K., “Optimization of Damped Dynamic Vibration Absorber to Control Chatter in Metal Cutting Process”, Journal of Vibration and Control, vol. 21, no. 5, pp. 949-958, 2015.[Abstract]


This paper deals with finding the optimum parameters of a damped dynamic vibration absorber (DVA) to control chatter in metal cutting systems. The performance of conventional damped DVA is compared with the proposed skyhook damper in which the damper of the absorber system is connected between the absorber mass and an inertial reference in the sky, referred to as a skyhook damper. The damped DVA is optimized by reducing the magnitude in the positive side and increasing it in the negative side of the real part of the frequency response function of the main system. The optimum frequency ratio and the damping ratio of the damped DVA for the undamped and damped main system are obtained using analytical solutions and a numerical optimisation technique, viz genetic algorithm, respectively. The performance of the proposed skyhook damper is marginally better than the conventional type of damped DVA in controlling the vibration of the main system. This is verified by analyzing both the proposed and conventional models using finite element method-based commercial software ANSYS.

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2014

Journal Article

Dr. Sakthivel N.R., Dr. Binoy B. Nair, Dr. Elangovan M., Sugumaran, V., and Saravanamurugan, S., “Comparison of Dimensionality Reduction Techniques for the Fault Diagnosis of Mono Block Centrifugal Pump Using Vibration Signals”, Engineering Science and Technology, an International Journal, vol. 17, pp. 30 - 38, 2014.[Abstract]


Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination.

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Publication Type: Conference Paper

Year of Publication Publication Type Title

2015

Conference Paper

Dr. Elangovan M., Dr. Sakthivel N.R., Saravanamurugan, S., Dr. Binoy B. Nair, and Sugumaran, V., “Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning”, in Procedia Computer Science, 2015, vol. 50, pp. 282–288.[Abstract]


Abstract Prediction of surface roughness is always considered important in the manufacturing field. A product may require a particular roughness that may be specified by the designer for various reasons, either functional requirement or aesthetic appeal. While modern manufacturing systems and machines have always contributed towards better control of surface quality, better computational facilities and the availability of newer algorithms attract researchers to understand the prediction of quality in a better manner. In this paper, prediction of surface roughness by multiple regression analysis is presented. The predictors are cutting parameters, tool wear and the statistical parameters extracted from the vibration signals of a turning centre. The contribution of various statistical parameters in prediction of surface roughness is studied. A Machine learning approach using feature reduction using principle component analysis is attempted to achieve higher predictability and low computational effort.

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2014

Conference Paper

S. Saravanamurugan, “Chatter Control in Shaping Process Using Dynamic Vibration Absorber”, in International conference on advances in design and manufacturing, ICAD&M-2014, NIT, Tiruchirappalli., 2014.

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