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

Dr. Saravanamurugan S. currently serves as Assistant Professor (Sg. Gr.) 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.

Thrust Area of Research

  • Machining Dynamics and Optimization

Awards and Achievements

  • Received the "Best Paper Award" in the International Conference on Manufacturing, Material Science and Engineering 2020 (ICMMSE 2020) organized at CMR, Hyderabad December 18-19, 2020.
  • Received the best "Best Paper Award" in 2nd International Conference on Materials, Manufacturing, and Machining for Industry 4.0 (ICMMM 2.0) organized at BIT, March 9-10, 2020.

Teaching/Research Interests

Major Subjects Taught

  • Mechanics of Materials
  • Design of Machine Elements
  • Mechanical Vibrations
  • Theory of Metal Cutting

Research Interests

  • Vibration Analysis and Control
  • Finite Element Method

Publications

Publication Type: Journal Article

Year of Publication Title

2021

S. Saravanamurugan, B. Sundar, S., R. Pranav, S., and Shanmugasundaram, A., “Optimization of cutting tool geometry and machining parameters in turning process”, Materials Today: Proceedings, vol. 38, pp. 3351-3357, 2021.[Abstract]


Vibrations play a major role in increasing the cost of production as they lead to a poor surface finish of the machined product and can also damage the cutting and machine tool. In this work, the influence of cutting tool geometry of a turning tool and machining parameters used in turning operations on regenerative chatter vibration during orthogonal cutting was studied. The optimum values of orthogonal rake angle, clearance angle, cutting tool overhang, feed rate and depth of cut are found by developing a model using design of experiments and the model was analysed using a statistical technique known as response surface method (RSM). The objective is to optimize cutting tool geometry and machining parameters of turning process by minimizing the real part of frequency response function (FRF) which is a response of a cutting tool subjected to a harmonic force. The harmonic analysis, carried out using software ANSYS, was used to get the peak value of real part of frequency response function (RFRF) of the cutting tool and its magnitude has to be minimized to improve the machining stability against regenerative chatter during machining. It may be inferred from the results that the tool rake angle, clearance angle, overhang of the cutting tool, feed rate and depth of cut are interacting with each other and influence the machining stability simultaneously. Hence, it may be advisable to pick an optimum set of machining parameters for the specific combination of cutting tool geometry to have vibration-free machining.

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2020

C. Sri, Saravanamurugan, S., Shanmugasundaram, A., and Mohapatra, S., “Effect of SiC and Gr particles on the mechanical properties and dynamic characteristics of AA 7075 hybrid metal matrix composite”, Materials Today: Proceedings, 2020.[Abstract]


One of the objectives of this research work is to fabricate the AA 7075 - Silicon Carbide (SiC) and Graphite (Gr) hybrid Metal Matrix Composite (MMC) containing 5 wt% of SiC and 2 and 4 wt% of graphite using the stir-casting technique. The other objective of this study is to know the effect of reinforcements on mechanical and dynamic characteristics of 7075 hybrid Composite. The 7075-SiC-Gr MMC is characterized by Scanning electron microscope (SEM), Energy dispersive X-ray (EDS) spectroscopy and X-ray diffraction (XRD) analysis. Microhardness tests are conducted on both the varieties of MMC and found that the microhardness was improved considerably in the case of 2% of Graphite when compared to 4% of Graphite with respect to the base metal. Dry sliding wear tests were conducted using Pin-on-Disc machine as per ASTM G99-95A standard. The results reveal that the wear resistance of 7075 hybrid MMC is improved considerably with respect to the base metal. Dynamic characteristics such as natural frequency and damping ratio of both composites and unreinforced AA 7075 are evaluated at room temperature. Damping ratio and natural frequency corresponding to the fundamental mode of vibration are found using an impulse hammer test. The results reveal that reinforcing AA 7075 matrix with SiC and graphite particles improves mechanical and dynamic characteristics. The 2 wt% of graphite composite exhibits better dynamic characteristics than that of the 4 wt% of graphite composite and aluminium alloy 7075.

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2017

S. Saravanamurugan, Thiyagu, S., Dr. Sakthivel N.R., and Nair, B. B., “Chatter Prediction in Boring Process using Machine Learning Technique”, International Journal of Manufacturing Research, vol. 12, no. 4, 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). [Received 12 August 2016; Accepted 19 May 2017]

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2016

Dr. Sakthivel N.R., Saravanamurugan, S., Dr. Binoy B. Nair, 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

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

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 Proceedings

Year of Publication Title

2021

V. V Krishna, Saravanamurugan, S., P Kishore, S., Yedhu, K. J., Iswar, G. K., and Shanmugasundaram A., “Vibration control in boring process using a constrained viscoelastic layer damper”, IOP Conference Series Materials science and Engineering (2nd International Conference on Materials and Manufacturing and Machining for Industry – 4.0, ICMMM 2020), vol. 1059. IOP Publishing, Bannari Amman Institute of Technology, October 9 – 10, 2020, Sathyamangalam, 2021.[Abstract]


sandwiched between main steel boring bar and aluminium tube, known to be constrained viscoelastic layer damper (CVLD), is designed and developed in order to reduce the vibration produced during the machining operations. The low density aluminium tube and natural rubber as viscoelastic layer are used in the boring bar to increase its natural frequency and damping property respectively. The finite element simulation of boring bar with and without CVLD are carried out using ANSYS and the results are used to develop a model to predict the influence of thickness of viscoelastic layer and aluminium tube on natural frequency of boring bar using full factorial design of experiments (DOE) and regression analysis. The DOE and regression analysis, carried using Minitab package, provides optimum thickness of viscoelastic layer and aluminium tube. These optimum thickness values are used to fabricate CVLD and its effectiveness to control boring bar vibration is tested by conducting machining experiments. The surface roughness of the machined components is also measured and the results show that the boring bar with CVLD is more efficient than conventional boring bar in controlling vibrations during machining. Moreover, stability lobes, which are plots of spindle speed vs. depth of cut of a machining process, are also constructed. Stability lobes indicate that the machining stability may be improved by 15 % by using boring bar with CVLD.

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Publication Type: Conference Paper

Year of Publication Title

2019

H. V. Ganapath Ram and Saravanamurugan, S., “Regenerative Chatter Control in Turning Process using Constrained Viscoelastic Vibration Absorber”, in IOP Conference Series: Materials Science and Engineering, 2019, vol. 577, p. 012152.[Abstract]


This paper focuses on utilization of viscoelastic material to control regenerative chatter in turning process. Self-induced vibrations can lead to the condition called regenerative chatter which produces violent relative vibrations between cutting tool and workpiece and reduces tool life and productivity. The regenerative chatter in turning processes can be controlled by maximizing negative real part of the frequency response function of cutting tool structure. In order to achieve this, a constrained viscoelastic vibration absorber (CVVA) is used. A CVVA consists of a viscoelastic material, such as natural rubber, which is sandwiched between two similar or dissimilar metallic layers. The CVVA is used as a cantilever beam whose fundamental natural frequency is tuned to the natural frequency of the dominant mode of cutting tool/tool holder. The optimum stiffness and damping coefficient of the CVVA are found using a numerical optimization technique and these optimal values are used to find the dimensions of CVVA. The resulting natural frequency of CVVA is verified using finite element simulation software ANSYS. The effectiveness of CVVA in controlling regenerative chatter in a compact CNC lathe is also analysed by constructing stability lobes which are plots of depth of cut vs spindle speed.

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2018

P. Sugapriya, Ramkrishnan R., G. Keerthana, and Saravanamurugan, S., “Experimental Investigation on Damping Property of Coarse Aggregate Replaced Rubber Concrete”, in IOP Conference Series: Materials Science and Engineering, 2018, vol. 310, p. 012003.[Abstract]


Rubber has good damping and vibrational characteristics and can reduce cracking significantly due to its elastic nature. This property of rubber can be incorporated in concrete to control vibrations and create better pavements. Crumb Rubber on being dumped in landfills has serious repercussions and causes soil and land pollution. An innovative use of waste tires is shredding them into small pieces and using them as a replacement for coarse aggregate. Crumb rubber is obtained by chopping scrap tires, and in this study it was added in two different sets named SET 1 - Treated Crumb Rubber and concrete, and SET 2 - Treated Crumb rubber with Ultra Fine GGBS as admixture in concrete. Coarse aggregate replaces Rubber in each of the 2 SET's in proportions of 5, 10, 15 and 20%. Properties like Compressive Strength, Young's Modulus, Direct and Semi direct Ultrasonic Pulse Velocity, Sorptivity, Damping ratio and Frequency were found out. Deformation and mode shape were studied with modal analysis and static analysis by applying a uniform pressure corresponding to the highest compressive strength of the slab, using ANSYS. © Published under licence by IOP Publishing Ltd.

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2015

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

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.

2006

S. Saravanamurugan, .I, R., .B, K., and .R, S., “Design of Dynamic Vibration Absorber to Control Chatter in Turning Operations using FEM”, in Ineternational conference on Global Manufacturing and Innovation, Coimbatore Institute of Technology, Coimabtore, 2006.

2003

S. Saravanamurugan, .T, A., and .R, B., “Analysis of Dynamic Behavior of Tool Holder using FEM”, in National conference on Computer Integrated Design and Manufacturing, Amrita Institute of Technology,Coimbatore, 2003.

List of Ph. D. Students

Current

  • Ravishankar - CB.EN.D*MEE20002-PT

Key Responsibilities at Amrita Vishwa Vidyapeetham

  • B. Tech. 2017-21 batch - Batch Coordinator and UG Project Coordinator