Qualification: 
Ph.D, MBA, M.Tech, BE
m_elangovan@cb.amrita.edu
Phone: 
099942 79654

Dr. Elangovan M. currently serves as Professor at Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. He pursed his Ph. D. in the area of Machine Learning using Data Mining applications for prediction of surface roughness of turned components and also relating it to the tool wear. He has also developed expertise in Product Design using DFMA concepts and extension of CAD/CAM applications to Product Lifecycle Management.

Qualification

  • Ph.D.    Mechanical Engineering, Amrita Vishwa Vidyapeetham in the year 2012.                                                                                              
  • M.Tech  Mechanical Engineering , IIT Madras in the year 1991
  • MBA in Operations Management from IGNOU in the year 2005
  • BE Distinction in Mechanical Engineering, B.M.S College of Engineering, Bangalore University in the year 1982 (with Two University Ranks)
     

Experience

  • Professor, Department of Mechanical Engineering, AVVP, Ettimadai, Coimbatore-105, from August 2000, till date.
  • Manager, CAD/CAM, Amrita Institute of Advanced Computing, Nov 1997 to July 2000.
  • Manager, Central R & D Watches, HMT, Bangalore from 3rd Nov 1985 to Oct 1997.
  • Part of the three member University NAAC Team of Amrita Vishwa Vidyapeetham that applied for NAAC accreditation for the first time and obtained an A Grade.
  • Ex- BOS member of Anna University Coimbatore for a period of 3 years
  • Current BOS member of Amrita Vishwa Vidyapeetham,  Karpagam University ,  MCET Pollachi (Autonomous)
  • SDRC’s IDEAS Certified Trainer
     

Areas of Research

  1. Machine Condition Monitoring using Machine Learning
  2. Product Design and Product Lifecycle Management
  3. Design for Manufacture & Assembly
     

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

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.

More »»

2015

Journal Article

K. Sa Shalet, Sugumaran, Va, Jegadeeshwaran, Ra, and Dr. Elangovan M., “Condition monitoring of single point cutting tool using arma features and SVM classifiers”, International Journal of Applied Engineering Research, vol. 10, pp. 8401-8416, 2015.[Abstract]


<p>Single point cutting tool (SPCT) is one of the most significant machine tools which has been used in the present industrial era. Tool wear and tool life are the principle areas to be focused on. This paper manifests the condition monitoring which was done on SPCT in the interest of perfect surface finish. Closer and effective observations were made while in operation. The developed failure in the form of vibration signal had been revealed. From the vibration signals, ARMA features were extracted. The extracted features were then classified by using a supervised learning model called Support Vector Machine (SVM). A case study has been done for various types and range of problems in this particular tool, in a cross reference with the extracted feature set. The obtained results were compared. Unscheduled outages, machine performance optimization, repair time reduction and maintenance cost can be avoided with the help of this paper. © Research India Publications.</p>

More »»

2015

Journal Article

Dr. Elangovan M., Muralidharan, K., Pradeep Kumar K. A., and Sridhar, G., “Design and development of low-cost rice harvesting machine”, International Journal of Applied Engineering Research, vol. 10, pp. 24893-24904, 2015.[Abstract]


<p>A Rice harvesting machine is designed and prototyped with a prime objective of making it affordable to small farmers. It is designed to reap two rows of rice crop at a time powered by an AC motor which in turn helps cutting and crop pushing away from the cutter. Three phase AC induction motor with the power of 0.5 HP is used. The time taken to reap one hectare area of paddy is around 6 hours. This is an alternative to the existing fully fledged rice harvester and is expected to be available in every farmer’s house. The focus is on simplicity and low cost in machinery, and independent of manpower availability. This paper brings out the design, development and analysis of a rice harvesting product by making it affordable for poor and small time farmers. © Research India Publications.</p>

More »»

2015

Journal Article

Dr. Elangovan M. and Sreeram, N., “Study on influence of lattice structures on product strength in a rapid prototyping environment”, International Journal of Applied Engineering Research, vol. 10, pp. 33065-33070, 2015.[Abstract]


Rapid prototyping is used to manufacture parts for easy validation and proving of new design concepts. Fused Deposition Modelling is an additive manufacturing process and most commonly used technique in rapid prototyping, which generates a three dimensional physical part from a 3D CAD model. Many a times, bulky components are made using this method. However, it takes a lot of time and unnecessary filling of the solid resulting in larger usage of proprietary material. One method to reduce weight and time could be by making it hollow. Hollow parts do not have the same strength and they require to be reinforced. This paper explores the different kinds of inner filling of these bulky parts and how they compare among themselves in the strength of the component. In the present study, a plastic gear used in Leyland wiper motor is replaced by a prototyped gear made by fused deposition modelling method. Analysis was carried out to determine the bending and contact stress. The analysis results were compared with theoretical results. © Research India Publications.

More »»

2015

Journal Article

Dr. Elangovan M., Kumar, S. S., and Ganesh, H. B. Bharathi, “Condition monitoring of a valve in a reciprocating compressor using machine learning approach”, International Journal of Applied Engineering Research, vol. 10, pp. 33078-33081, 2015.[Abstract]


Reciprocating compressors are used in industries to provide pressurized air, which in turn is used for a variety of production processes. Compressors are expected to be made available as and when required and any delay or downtime of the same will affect the production process. Machine Learning based fault diagnosis of a compressor-valve is proposed in this paper.In reciprocating compressors, valves contribute to a greater percentage of failure and a diagnostic method to detectthe cause of failure is required. Fault diagnosis followed by a remedial measure is widely welcome in industry to improve the productivity. Faulty conditions are classified using machine learning algorithms like LogisticRegression (LR), Support Vector Machine (SVM) and Random Forest Tree (RFT). Accuracy of classification of different valve conditions is improved by identifying the best statistical feature selection from Random Forest Tree.The results confirm that the proposed method can classify the valveconditions withgreater accuracy nearing 75% and reliability. © Research India Publications.

More »»

2015

Journal Article

Dr. Elangovan M. and .K.Philip, A., “Study on Parameters Influencing Fill Time In A Multi-Cavity Mold”, International Journal of Applied Engineering Research , vol. 10, pp. 27817-27826., 2015.

2015

Journal Article

Dr. Elangovan M. and M, A. T., “Design and Analysis of Low Cost Lift System for Disabled Citizens at Railways Stations”, International Journal of Applied Engineering Research, vol. 10, pp. 34084-34088, 2015.

2015

Journal Article

N. S.Saravanamurugan, Sakthivel, R., Dr. Elangovan M., and .B.Nair, B., “Detection of Chatter Using Pattern Recognition in Turning Process”, International Journal of Data Mining, Modelling and Management(SCOPUS), 2015.

2014

Journal Article

Dr. Elangovan M., “Analysis and Design of Mold for Plastic Side Release Buckle using Moldflow Software IJRET”, International Journal of Research in Engineering and Technology , vol. 03, no. 05, 2014.[Abstract]


Injection Molding is a crucial segment of the plastics industry mainly due to its ability and flexibility to manufacture intricate components at high production rates and also with easy process flow. Injection molding has laid its hands over right from small parts to big machine parts. Plastic side release buckles, commonly employed in standard bags, suitcases and pouches, and are manufactured exclusively by the injection molding process all around the world. The process involved should be perfect enough to produce parts without defects and low quality. Gate location is one of the important in this process which is necessary for the filling purposes. In view of the significance of a perfect gate location for the plastic injection mold for the side release buckle, this paper focusses on the complete analysis of the buckle using a moldflow software and the determination of optimum gate locations for it. The analysis is complete look into the various quality features important for the mold, it considers identification and improvement of parameters such as fill time, quality, extent of packing and reduced defects and warpage. Utilization of the optimized gate locations for the mold lead to reduced production costs, higher quality and enhanced competitive power of mold enterprises

More »»

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.

More »»

2011

Journal Article

Dr. Elangovan M., Sugumaran, V., Ramachandran, K. I., and Ravikumar, S., “Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool”, Expert Systems with Applications, vol. 38, no. 12, pp. 15202–15207, 2011.[Abstract]


The studies on tool condition monitoring along with digital signal processing can be used to prevent damages on cutting tools and workpieces when the tool conditions become faulty. These studies have become more relevant in today’s context where the order realization dates are crunched and deadlines are to be met in order to catch up with the competition. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is extensively used to probe into structural health of the tool and the process. This paper discusses condition monitoring of carbide tipped tool using Support Vector Machine and compares the classification efficiency between C-SVC and ν-SVC. It further analyses the results with other classifiers like Decision Tree and Naïve Bayes and Bayes Net. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better features-classifier combination. More »»

2011

Journal Article

Dr. Elangovan M., S Devasenapati, B., Dr. Sakthivel N.R., and Ramachandran, K. I., “Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm”, Expert Systems with Applications, vol. 38, no. 4, pp. 4450–4459, 2011.[Abstract]


Tool wear and tool life are the principle areas are focus in any machining activity. The production rate, surface finish of machined component and the machine condition are directly related to the tool condition. This work on tool condition monitoring delves into data mining approach to discover the hidden information available in the tool vibration signals. The use of statistical features derived from the vibration data is used as the primary feature and Principle Component Analysis (PCA) transformed statistical features are evaluated as an alternative. In order to increase the robustness of the classifier and to reduce the data processing load, feature reduction is necessary. The feature reduction using (a) decision tree and (b) feature transformation and reduction using PCA are evaluated independently and the results are compared. The effective combination of feature reducer and classifier for designing the expert system is studied and reported. More »»

2010

Journal Article

Dr. Elangovan M., Ramachandran, K. I., and Sugumaran, V., “Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features”, Expert Systems with Applications, vol. 37, no. 3, pp. 2059–2065, 2010.[Abstract]


Various methods of tool condition monitoring techniques are used to control the tool wear during machining in CNC machine tools. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is used to probe into the structural information hidden in the signals acquired. This paper discusses machine tool condition monitoring of carbide tipped tool by using Naïve Bayes and Bayes Net classifiers and compares the results of histogram features with the statistical features to establish better classification among the two. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better feature–classifier combine. The results are discussed. More »»

Publication Type: Conference Paper

Year of Conference 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.

More »»

Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2014

Conference Proceedings

J. Sai Praneeth, Sreedharala, A. Kumar, Darisi, N. Kumar, and Dr. Elangovan M., “Design of Whitworth Quick Return Mechanism Using Non-Circular Gears”, IRF International Conference. Pune, India, 2014.

2011

Conference Proceedings

Dr. Elangovan M. and Ramachandran, K. I., “Prediction of Roughness Based on Tool Condition and Cutting Parameters using Regression Analysis”, International Conference on Recent Advances in Mechanical Engineering. Dr. M.G.R University, Chennai., 2011.

2011

Conference Proceedings

Dr. Elangovan M., Sugumaran, V., and Ramachandran, K. I., “Tool Wear Classification using Statistical Features and Fuzzy classifier in Turning”, Second International Conference on Simulation Modelling and Analysis”, (COSMA2011). Amrita Vishwa Vidyapeetham, Coimbatore., 2011.

2011

Conference Proceedings

Dr. Elangovan M., Sugumaran, V., and Ramachandran, K. I., “Condition monitoring of Single point cutting tool using Discrete Wavelet Transform and classification using Bayes functions”, Second International Conference on Simulation Modelling and Analysis (COSMA2011) . Amrita Vishwa Vidyapeetham, Coimbatore, 2011.

2011

Conference Proceedings

Dr. Elangovan M., Nitin, N., and Ramachandran, K. I., “Effects of Cutting Tool Coating on Surface Roughness in Turning Using Regression at National conference on Recent Trends in Communication”. Amrita Vishwa Vidyapeetham, Coimbatore., 2011.

2009

Conference Proceedings

Dr. Elangovan M., Sugumaran, V., and Ramachandran, K. I., “Condition Monitoring of Single Point Cutting Tool A Data Mining Approach”, International Conference on Operations Research applications in Engineering and Management (ICOREM - 2009) . Anna University, Trichy., 2009.

2009

Conference Proceedings

Dr. Elangovan M., S. Devasenapati, B., and Ramachandran, K. I., “Condition Monitoring of a Single Point Cutting Tool Using Support Vector Machine”, International Conference on Advances in Mechanical and Building Sciences (ICAMB-2009) . Vellore Institute of Technology, Vellore, 2009.

2008

Conference Proceedings

Dr. Elangovan M., Sugumaran, V., and Ramachandran, K. I., “Bayes Net Classifier for Condition Monitoring of Single point Carbide Tipped Tool”, International Conference on Recent Trends in Materials and Mechanical Engineering (ICMME 2008), . Mahalingam College of technology, Pollachi.INDIA , pp. 18-20, 2008.

Peer Reviewed International Journals(Under Review)

  1. Sanidhya Painuli, V. Sugumaran , M.Elangovan, Tool Condition Monitoring: System design and optimization using Ensemble Algorithms, Measurement. Elsevier publication
     

Invited Talks

  1. Invited to inaugurate FDP program on CAD at Karpagam College of Engineering, Coimbatore 21, on 15.05.2015 followed by a talk on Computer Integrated Manufacture
  2. Invited to inaugurate FDP program on CAD at Karpagam Institute of Technology, Coimbatore 21, on 22.05.2015 followed by a talk on Computer Aided Design.
     

Reviewer

  1. Journal of Intelligent Manufacturing (JIMS)
  2. Journal of Engineering Science and Technology (JESTEC)
     

Conferences

  1. COSMA 2011 – International Conference on Simulation, Modelling and Analysis. Co-cordinator. Conference was organized jointly with NIT Calicut in the year 2011.
     

Other Academic/Non-Academic Activities

  1. Chairman for ANOKHA 2014 Student TechFest
  2. President for Amrita Alumni Association from 2013 to 2014
     

Honors & Awards

  1. Gandhian Young Technological Innovation Award 2014 for my students under my guidance on “Cost Effective Vegetable Chiller For Rural Small Farmers”. Received One lakth grant towards development.
  2. Cash Award for contributions made to the Institution, Amrita Institute of Technology and Science 2002
     

Professional Bodies

  1. Member, ISAMPE
  2. Life member , ISTE
  3. Life Member- NIQR
     

Student Guidance

Master’s Students Partial List

  1. Study on Influence of Lattice Structures on Product Structure in a Rapid Prototyping environment. (Sreeram N - CB.EN.P2MFG13021)
  2. Condition Monitoring of a valve in a reciprocating compressor using Machine Learning . (Suraj Suresh Kumar - CB.EN.P2MFG13022)
  3. Studies on Mechanical Properties of Glass filled Polypropylene with Talc (Suraj Suresh Kumar - CB.EN.P2MFG13022)
  4. Design, Analysis and development of Low Cost Lift System for Disabled citizens at Railway Stations
  5. Effect of EDM process parameter on Fatigue life of Maraging Steel MDN 250
  6. Design of Flexible spot welding cell for Body-In- White (BIW) line.
  7. Study and Analysis of effect of forces on luggage rack of a metro train
  8. Damage Tolerance study of stiffened skin composite panel
  9. A Study on effect of process parameters on the quality using low cost 3D printer.
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS