Qualification: 
Ph.D, M.E, BE
Email: 
k_rameshkumar@cb.amrita.edu

K. Ramesh Kumar currently serves as Professor at the Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore.

Educational Qualification

  • 2007: Ph.D. in Production Engineering
    PSG College of Technology, Coimbatore
    Affiliated to Bharathiar University, Coimbatore, India 
  • 1991: Master of Engineering (M.E.) in Mechanical Engineering (Specialization in Production Engineering)
    Bharathiar University
  • 1990: Bachelor of Engineering (B.E.) in Mechanical Engineering
    Bharathiar University, Coimbatore.

Career Profile

Academic and Research
Year/Duration Affiliation
September 1, 2016 - Present  Professor, Department of Mechanical Engineering, School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham
July 1, 2013 - June 30, 2015 Principal and Professor, Department of Mechanical Engineering, Asian College of Engineering and Technology, Coimbatore
October 1, 2007 - July 1, 2013 Professor, Department of Mechanical Engineering, School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham
January 1, 2004 - September 30, 2007 Assistant Professor, Department of Mechanical Engineering, School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham
May 22, 2000 - December 31, 2003 Sr. Lecturer, Department of Mechanical Engineering, School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham
2 Years Faculty, Department of Engineering, College of Technology (Muscat & Ibra), Ministry of Manpower, Sultanate of Oman (On Sabbatical leave)
3 Years Sr. Lecturer, Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore
Industry
Year/Duration Affiliation
1 Year and 3 months Engineer- Mechanical (Design of Antenna components and fabrication) at Clear Vision Cables
4 Years and 3 months Project Engineer (Technical Services: 150 TPD staple fiber manufacturing plant) at SIV Industries Limited, Coimbatore

Research Interest

  • Machine tool condition monitoring using machine learning algorithms – Vibration and Acoustic Emission (AE) techniques – Signal processing in time, frequency and wavelet domains
  • Weld quality assessment using voltage and current signatures – Development of real-time weld quality monitoring system
  • High speed machining – Machining dynamics
  • Solving discrete and continuous optimization problems using evolutionary algorithms
  • System modelling and optimization using discrete event simulation packages

Funded Projects

  1. All India Council for Technical Education (AICTE), MODROB, Modernizing Metallurgy lab (Completed), (Collaborating faculty members : Dr.R.Sellamuthu – ASE, Coimbatore)
  2. Defence Research & Development nization (DRDO), Process Monitoring and Control for Ultra Precision Machining of Titanium Alloys, (Completed) – Co-Principal Investigator. (Collaborating faculty members : Dr.K.I.Ramachandran (PI), and P.Krishnakumar – ASE, Coimbatore)
  3. Defence Research & Development Organization (DRDO), Fault diagnosis of dynamic mechanical systems (gear box) based on signal processing using machine learning techniques. (Completed) - Co-Principal Investigator. (Collaborating faculty members: Dr.K.I.Ramachandran (PI), Dr.M.Sai Murugan, and P.Krishnakumar – ASE, Coimbatore).
  4. Aeronautical Research and Development Board (AR&DB) - Investigations into the surface integrity of titanium alloys during High Speed Machining – Co-Principal Investigator. (Collaborating faculty members : Dr.K.I.Ramachandran (PI), and P.Krishnakumar – ASE, Coimbatore)

Awards

  • Best Paper Award: Rameshkumar, K., Rajendran, Karthi, R., (2011). Modified Discrete Particle Swarm Optimization Algorithm (MDPSOA) for static permutation flowshop scheduling to minimize total flow time of jobs, Proceedings in Emerging Trends in Industrial Engineering, ETIE NIT Calicut.

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

2018

Journal Article

D. Thomas Thekkuden, Santhakumari, A., Sumesh, A., Mourad, A. - H. I., and Rameshkumar, K., “Instant Detection of Porosity in gas metal arc welding by using probability density distribution and control chart”, The International Journal of Advanced Manufacturing Technology, 2018.[Abstract]


A novel porosity detection technique from the voltage and current transients is introduced in this paper. An online weld monitoring that detects the porosity at an earlier stage is much demanding in the industry due to their adverse effects on structural integrity. In this research work, control chart and probability density distribution have been employed as tools to detect arc instability and weld porosity. The results showed that the pattern of probability density distribution changes for the defect and defect-free welds significantly. The mean and standard deviation control charts plotted with voltage clearly distinguished the quality of the weld based on sample points spread within or outside the control limits. For minute internal porosities, the sample points at the corresponding region in the standard deviation control chart were outside the limits whereas it is well within the control limits in the mean control chart. Inspector can predict the presence and near location of porosity using these tools by simple mathematical calculations easily and instantly. The results proved that the developed approach is successful and promising for the weld inspection.

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2018

Journal Article

K. Rameshkumar, Krishnakumar, P., and Ramachandran, K. I., “Machine Learning Based Tool Condition classification using AE and Vibration data in a High Speed Milling Process Using Wavelet Features”, Intelligent Decision Technologies: An International Journal, 2018.

2017

Journal Article

A. Sumesh, Rameshkumar, K., Raja, A., Mohandas, K., Santhakumari, A., and Shyambabu, R., “Establishing Correlation Between Current and Voltage Signatures of the Arc and Weld Defects in GMAW Process”, Arabian Journal for Science and Engineering, vol. 42, pp. 4649-4665, 2017.[Abstract]


Welding is one of the major metal-joining process employed in fabrication industries, especially in manufacturing of boilers and pressure vessels. Control of weld quality is very important for such industries considering the severe operating conditions. Industries are looking for some kind of real-time process monitoring/control that will ensure the weld quality online and prevent the occurrence of defects. In this paper an attempt is made to establish a correlation between the current and voltage signatures with the good weld and weld with porosity and burn through defect during the welding of carbon steel using gas metal arc welding (GMAW) process. Experimental setup has been established and experiments were conducted using a welding robot integrated with GMAW power source. The experimental setup includes online current and voltage sensors, data loggers, and signal processing hardware and software. Welding conditions are carefully designed to produce good weld and weld with defects such as burn through and porosity. Current and voltage signatures are captured using data acquisition system (DAS). Software has been developed to analyze the data captured by the DAS. Statistical methods are employed to study the transient data. The probability density distributions of the current and voltage signature demonstrates a good correspondence between the current and voltage signatures with the welding defect. © 2017, King Fahd University of Petroleum & Minerals. More »»

2017

Journal Article

K. Rameshkumar and Syed, S., “Construction of a Low Cost Cutting Tool Dynamometer and Static Calibration of Measuring Cutting Force in a CNC Milling Machine ”, SAE Technical Paper , 2017.[Abstract]


In this work an attempt is made to design and fabricate a low cost dynamometer for measuring cutting forces in three directions in a CNC vertical milling machine. The dynamometer is designed and fabricated to withstand load up to 5000 N along ‘X’, ‘Y’ and ‘Z’ axis. Milling dynamometer developed in this work, consists of four octagonal rings as an elastic member on which strain gauges are mounted for measuring the cutting forces. Suitable materials for the fixture and for the octagonal rings are chosen for constructing the dynamometer. Structural analysis has been carried out to check the safe design of the dynamometer assembly consisting of fixture and the octagonal rings for the maximum loading conditions. Static calibration of the dynamometer is carried out using slotted weight method by simulating the actual conditions. Calibration chart was prepared for three directions by relating load and corresponding strain. The proposed arrangement has been interfaced using NI data acquisition system for measuring the cutting forces. The dynamometer developed in this work is validated by conducting experimental trials. This low cost dynamometer is capable of measuring cutting forces in all three directions.

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2017

Journal Article

K. Rameshkumar, Thenarasu, M., and Prakash, M., “Simulation Modeling and Development of Analytic Hierarchy Process (AHP) based Priority Dispatching Rule (PDR) for a Dynamic Press Shop”, International Journal of Industrial and systems engineering, vol. 27, no. 3, 2017.

2015

Journal Article

A. Sumesh, Thekkuden, D. Thomas, Dr. Binoy B. Nair, Rameshkumar, K., and Mohandas, K., “Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms”, Advances in Mechanical Engineering, vol. 813-814, pp. 1104-1113, 2015.[Abstract]


The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.

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2015

Journal Article

P. Krishnakumar, Rameshkumar, K., and Ramachandran, K. I., “Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy”, Procedia Computer Science, vol. 50, pp. 270 - 275, 2015.[Abstract]


Ti-6Al-4V is extensively used in aerospace and bio-medical applications. In an automated machining environment monitoring of tool conditions is imperative. In this study, Experiments were conducted to classify the tool conditions during High Speed Machining of Titanium alloy. During the machining process, vibration signals were monitored continuously using accelerometer. The features from the signal are extracted and a set of prominent features are selected using Dimensionality Reduction Technique. The selected features are given as an input to the classification algorithm to decide about the condition of the tool. Feature selection has been carried out using J48 Decision Tree Algorithm. Classifications of tool conditions were carried out using Machine Learning Algorithms namely J48 Decision Tree algorithm and Artificial Neural Network (ANN). From the analysis, it is found that ANN is producing comparatively better results. The methodology adopted in this study will be useful for online tool condition monitoring.

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2015

Journal Article

A. Sumesh, Rameshkumar, K., Mohandas, K., and R. Babu, S., “Use of Machine Learning Algorithms for Weld Quality Monitoring using Acoustic Signature”, Procedia Computer Science, vol. 50, pp. 316 - 322, 2015.[Abstract]


Welding is one of the major joining processes employed in fabrication industry, especially one that manufactures boiler, pressure vessels, marine structure etc. Control of weld quality is very important for such industries. In this work an attempt is made to correlate arc sound with the weld quality. The welding is done with various combinations of current, voltage, and travel speed to produce good welds as well as weld with defects. The defects considered in this study are lack of fusion and burn through. Raw data points captured from the arc sound were converted into amplitude signals. The welded specimens were inspected and classified into 3 classes such as good weld and weld with lack of fusion and burn through. Statistical features of raw data were extracted using data mining software. Using classification algorithms the defects are classified. Two algorithms namely, J48 and random forest were used and classification efficiencies of the algorithms were reported.

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2013

Journal Article

P. K Marimuthu, Krishna Kumar P., Rameshkumar, K., and Dr. K. I. Ramachandran, “Finite element simulation of effect of residual stresses during orthogonal machining using ALE approach”, International Journal of Machining and Machinability of Materials, vol. 14, pp. 213–229, 2013.[Abstract]


This paper presents a finite element model that has been developed to predict the effect of residual stress induced in the work material during multiple pass turning of AISI 4340 steel. Chip morphology and force variation during machining are also quantified using the FE model. Finite element model was developed using arbitrary Lagrangian-Eulerian formulation along with Johnson-Cook material model and Johnson-Cook damage model. The finite element model developed in this study was validated experimentally by studying the chip morphologogy and cutting force variation during the machining. Results indicate that there is good correlation existing between numerical results and experimental results.

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2012

Journal Article

K. Rameshkumar, Rajendran, Cb, and Mohanasundaram, K. Mc, “A novel particle swarm optimisation algorithm for continuous function optimisation”, International Journal of Operational Research, vol. 13, pp. 1-21, 2012.[Abstract]


Particle swarm optimisation (PSO) algorithms are applied in a variety of fields. A good quality solution for a problem depends on the PSO parameters chosen for the problem under study. In this paper, a continuous particle swarm optimisation algorithm is proposed to solve the unconstrained optimisation problems. This paper proposes a novel and simple variation of the PSO algorithm by introducing dynamic updating of velocity without any parameters, such as inertia weight and constriction coefficients that are commonly used in the traditional PSO algorithms. The proposed algorithm is applied to well-known benchmark functions which are commonly used to test the performance of numeric optimisation algorithms, and the results are compared with the existing PSO algorithms. It is found that the proposed algorithm gives superior results in terms of speed of convergence and the ability of finding the solutions of excellent quality. Copyright © 2012 Inderscience Enterprises Ltd.

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2012

Journal Article

R. S. Kadadevaramath, Chen, J. C. H., B. Shankar, L., and Rameshkumar, K., “Application of Particle Swarm Intelligence Algorithms in Supply Chain Network Architecture Optimization”, Expert Systems with Applications, vol. 39, pp. 10160 - 10176, 2012.[Abstract]


In today’s globalization, the success of an industry is dependent on cost effective supply chain management under various markets, logistics and production uncertainties. Uncertainties in the supply chain usually decrease profit, i.e. increase total supply chain cost. Demand uncertainty and constraints posed by the every echelon are important factors to be considered in the supply chain design operations. Optimization is no longer a luxury but has become the order of the day. This paper specifically deals with the modeling and optimization of a three echelon supply chain network using the particle swarm optimization/intelligence algorithms.

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2011

Journal Article

Ra Karthi, Rajendran, Cb, and Rameshkumar, K., “Neighborhood search assisted particle swarm optimization (NPSO) algorithm for partitional data clustering problems”, Communications in Computer and Information Science, vol. 192 CCIS, pp. 552-561, 2011.[Abstract]


New variant of PSO algorithm called Neighborhood search assisted Particle Swarm Optimization (NPSO) algorithm for data clustering problems has been proposed in this paper. We have proposed two neighborhood search schemes and a centroid updating scheme to improve the performance of the PSO algorithm. NPSO algorithm has been applied to solve the data clustering problems by considering three performance metrics, such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the proposed algorithm have been compared with the published results of basic PSO algorithm, Combinatorial Particle Swarm Optimization (CPSO) algorithm, Genetic Algorithm (GA) and Differential Evolution (DE) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems. © 2011 Springer-Verlag.

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2011

Journal Article

K. Rameshkumar, Rajendran, Cb, and Mohanasundaram, K. Mc, “Discrete particle swarm optimisation algorithms for minimising the completion-time variance of jobs in flowshops”, International Journal of Industrial and Systems Engineering, vol. 7, pp. 317-340, 2011.[Abstract]


In this paper, the problem of scheduling in the permutation flowshop scheduling problem is considered with the objective of minimising the completion-time variance of jobs (CTV). Two particle swarm optimisation algorithms (PSOAs) are proposed and analysed. The first PSOA is inspired from the solution construction procedures that are used in ant colony optimisation algorithms. The second algorithm is a newly developed one. The proposed algorithms are applied to a set of benchmark flowshop scheduling problems, and performances of the algorithms are evaluated by comparing the obtained results with the results published in the literature. The performance analysis demonstrates the effectiveness of the proposed algorithms in solving the permutation flowshop sequencing problem with the CTV objective. © 2011 Inderscience Enterprises Ltd.

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2011

Journal Article

H. Ganesan, .G., M., Ganesan, K., and Rameshkumar, K., “Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental verification”, International Journal of Engineering Science and Technology, vol. 3, pp. 1091-1102, 2011.

2009

Journal Article

R. S. Kadadevaramath, Prasad, P. S. S., Mohanasundaram, K. M., Immanuel, E. A., and Rameshkumar, K., “Profit Optimisation in Three Echelon Supply Chain Revenue Management Through New heuristic Optimiser”, International Journal of Revenue Management, vol. 3, pp. 79-101, 2009.[Abstract]


In recent years, many of the developments in supply chain revenue management are connected to the need of information of efficient supply chain flow; modelling and optimisation, are most important in order to maximise the profit of supply chain because the cost of material, manufacturing and distribution and inventory accounts for 70-80% value of the product. Hence, tactical supply chain design has become a major challenge for firms so as to improve the revenue of the organisation. Particle swarm optimisation is used to optimise the supply chain operations with the objective of maximising the profit of the supply chain revenue.

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2009

Journal Article

R. .S., Mohanasudaram, K. M., Rameshkumar, K., and Chandrasekar, B., “Production and Distribution Scheduling of Supply Chain Structure Using Intelligent Particle Swarm Optimization”, International Journal of Intelligent Systems Technology &Applications (IJISTA), vol. 6, pp. 249-269, 2009.[Abstract]


In today's global market, managing the entire Supply Chain (SC) becomes a key factor for the successful business and businesses have to be more adaptive to change. World class organisations now realise that non-integrated manufacturing processes, non-integrated distribution processes and poor relationship with suppliers and customers are inadequate for their success. Recently, however, there has been increasing attention placed on the performance, design and analysis of the SC as a whole. This paper specifically deals with the modelling and optimisation of a four-stage SC using the Particle Swarm Optimisation (PSO) algorithm and the problem was solved for optimal distribution of components and products made by them using PSO algorithm. And it was found that the PSO algorithm has been successfully applied to solve problems in SC network optimisation and gives quality results.

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2009

Journal Article

R. Karthi, Arumugam, S., and Rameshkumar, K., “Discrete Particle Swarm Optimization Algorithm for Data Clustering, Nature inspired cooperative strategies for optimization”, Springer-Verlag, Berlin, Heidel berg, vol. 236, pp. 75 - 88, 2009.

Publication Type: Conference Paper

Year of Conference Publication Type Title

2017

Conference Paper

A. Arun, Rameshkumar, K., Unnikrishnan, D., and Sumesh, A., “Tool Condition Monitoring of Cylindrical Grinding Process Using Acoustic Emission Sensor”, in ICCM 2017, VIT Chennai, 2017.

2017

Conference Paper

A. Sumesh, Rameshkumar, K., ,, Shanthakumari, S., and Mohandas, K., “Decision Tree Based Weld Defect Classification using Current and Voltage Signatures in GMAW Process”, in IMME 2017, NIT Trichirapalli, 2017.

2017

Conference Paper

K. Rameshkumar and Rajendran, C., “A novel Discrete PSO Algorithm for Solving Job Shop Scheduling Problem to Minimize Makespan”, in IConAmma 2017, 2017.

2017

Conference Paper

V. R. Sathish Kumar, Anbuudayasankar, S. P., and Rameshkumar, K., “Optimizing Bi-objective, Multi-echelon Supply Chain Model Using Particle Swarm Intelligence Algorithm”, in IConAmma 2017, 2017.

2017

Conference Paper

S. Krishnan, Dev, S., ,, Sumesh, A., and Rameshkumar, K., “Bottleneck Identification in a Tyre Manufacturing Plant Using Simulation Analysis and Productivity Improvement”, in IConAmma 2017, 2017.

2017

Conference Paper

K. Rameshkumar, “Extension of PSO and ACO-PSO Algorithms for Solving Quadratic Assignment Problems”, in ICMMRE 2017, 2017.

2009

Conference Paper

R. Karthi, Arumugam, S., and Rameshkumar, K., “A Novel Discrete Particle Swarm Clustering Algorithm for Data Clustering”, in Proceedings of the 2Nd Bangalore Annual Compute Conference, New York, NY, USA, 2009.[Abstract]


<p>In this paper, a novel Discrete Particle Swarm Clustering algorithm (DPSC) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form and an efficient approach is developed to move the particles for constructing new clustering solutions. DPSC algorithm has been applied to solve the data clustering problems by considering two performance metrics, such as TRace Within criteria (TRW) and Variance Ratio Criteria (VRC). The result obtained by the proposed algorithm has been compared with the published results of Combinatorial Particle Swarm Optimization (CPSO) algorithm and Genetic Algorithm (GA). The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.</p>

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Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2005

Conference Proceedings

K. Rameshkumar, Suresh, R. K., K., M., and .M, K., “Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makespan”, Lecture Notes in Computer Science (LNCS), vol. 3612. pp. 572 – 581, 2005.[Abstract]


In this paper a discrete particle swarm optimization (DPSO) algorithm is proposed to solve permutation flowshop scheduling problems with the objective of minimizing the makespan. A discussion on implementation details of DPSO algorithm is presented. The proposed algorithm has been applied to a set of benchmark problems and performance of the algorithm is evaluated by comparing the obtained results with the results published in the literature. Further, it is found that the proposed improvement heuristic algorithm performs better when local search is performed. The results are presented.

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207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
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