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

K. Ramesh Kumar currently serves as Vice Chairperson and 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
Amrita School of Engineering, Amrita Vishwa Vidyapeetham
September 1, 2016 - Present  Professor, Department of Mechanical Engineering, School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham
September 1, 2016 - August 10, 2018 Professor Department of Mechanical Engineering, Bangalore.
July 1, 2013 - October 1, 2017 Professor Department of Mechanical Engineering.
January 1, 2004 - September 10, 2007 Assistant Professor Department of Mechanical Engineering.
May 22, 2000 - December 31, 2003 Sr. Lecturer Department of Production Engineering
Higher College of Technology, Ministry of Manpower, Sultanate of Oman, Muscat & Ibra
October 10, 2008 - January 6, 2010 Faculty member in the Department of Engineering, Mechanical Section, On sabbatical leave from Amrita Vishwa Vidyapeetham
October 2015 - August 2016 Faculty member in the Department of Engineering, Mechanical Section.
Asian College of Engineering and Technology, Coimbatore
July 1, 2013 - June 30, 2015 (2 Years) Principal and Professor in the Department of Mechanical Engineering.
Kumaraguru College of Technology, Coimbatore
3 Years Sr. Lecturer in the Department of Mechanical Engineering.
Industry
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

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

Funded Projects

Completed

  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 Organization (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 (Completed) – Co-Principal Investigator. (Collaborating faculty members: Dr.K.I.Ramachandran (PI), and P. Krishnakumar – ASE, Coimbatore)

On-going

  1. Development, field trials, pilot production and technology demonstration of sintered brake pads with improved performance for wind turbine applications suitable to India specific wind characteristics. Co-PI, 42.56 Lakhs – DST (Collaborating faculty members: Dr.M. Govindaraju (PI), and Dr. Ravikumar)

Best Paper Award

  • 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: Book Chapter

Year of Publication Title

2020

D. Mouli and K. Ramesh Kumar, “Acoustic Emission-Based Grinding Wheel Condition Monitoring Using Decision Tree Machine Learning Classifiers”, in Mechanical Engineering, Singapore: Springer, 2020, pp. 353-359.

2009

R. Karthi, Arumugam, S., and K. Ramesh Kumar, “Discrete Particle Swarm Optimization Algorithm for Data Clustering”, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2008) , Springer-Verlag, Berlin, Heidel berg, vol. 236, 2009, pp. 75 - 88.[Abstract]


In this paper, a novel Discrete Particle Swarm Optimization Algorithm (DPSOA) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form. The DPSOA algorithm uses of a simple probability approach to construct the velocity of particle followed by a search scheme to constructs the clustering solution. DPSOA 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 results obtained by the proposed algorithm have been compared with the published results of Basic PSO (B-PSO) algorithm, Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Combinatorial Particle Swarm Optimization (CPSO) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.

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Publication Type: Journal Article

Year of Publication Title

2020

S. P. Krishnan, K. Ramesh Kumar, and Krishna Kumar P., “Hidden Markov Modelling of High-Speed Milling (HSM) Process Using Acoustic Emission (AE) Signature for Predicting Tool Conditions”, Advances in Materials and Manufacturing Engineering, 2020.[Abstract]


Tool condition monitoring is an important activity to monitor and maintain the quality of products manufactured in any machining process without any manual intervention. Hidden Markov models (HMM) are developed in this study for predicting tool conditions in a High-Speed Milling of titanium alloy using a carbide tool. Tool conditions are predicted using AE signatures captured during the metal cutting operation. A correlation between AE features and tool conditions were established using Baum-Welch and Viterbi algorithms. HMM models proposed in this study are integrated with the K-means clustering algorithm. The clustered data has been represented as an integer sequence and is divided into 3 tool states such as `sharp', `intermediate' and `worn-out'. Three HMM models are created for each state of the tool. Two AE features namely `Root Mean Square (RMS)' and `Rise' were used for developing HMMs. The performance of the HMMs is evaluated using log-likelihood measure.

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2020

A. Kuppusamy, K. Ramesh Kumar, A. Sumesh, and Premkumar, S., “GMAW Process Parameter Optimization to Reduce Porosity Defect in a Longitudinal Seam Welding of Pressure Vessels”, SAE Int. J. Mater. Manuf., vol. 13, no. 1, 2020.[Abstract]


Pressure vessels are critical equipment used in industries for storing liquids or gases at a pressure significantly different from ambient conditions. Porosity is one of the major weld defects in pressure vessels that leads to failure during inspection and as well as during its service. Gas Metal Arc Welding (GMAW) process is widely used in industries to fabricate pressure vessels using carbon steel “IS 2062 E250BR” material for storing compressed air. The main objective of this article is to reduce the porosity defect in the longitudinal seam (LS) welding of the pressure vessels. Detailed analysis is carried out to identify the parameters which are influencing the porosity defect. Central Composite Design (CCD) and Response Surface Methodology (RSM) approaches are used to find the optimum value of the weld parameters which produce weld without porosity or any major defects in the pressure vessel. An experimental setup has been established and welding experiments have been conducted under a controlled environment. Experiments were conducted without any external disturbances ensuring clean weld surface and filler wire without any moisture, rust, oil, and the presence of any organic materials. For all the weld specimens, visual and radiography examinations were carried out to identify the severity of porosity. A porosity index is proposed in this study for conducting statistical analysis. Statistical analysis shows current, travel speed, gas flow rate, and torch angle have a linear relationship and stickout distance has a nonlinear relationship with porosity. In square term, stickout distance has a significant influence on porosity defect. In two-way interaction studies, current and gas flow rate, current and torch angle, and travel speed and torch angle have a significant influence on porosity. Confirmatory tests were carried out to validate the optimum weld parameters obtained in this study.

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2020

K. K. Dhayanandh, K. Ramesh Kumar, A. Sumesh, and Lakshmanan, N., “Influence of oil injection parameters on the performance of diesel powered screw air compressor for water well application”, Measurement, vol. 152, p. 107323, 2020.[Abstract]


In this paper, a study has been carried out to find the influence of oil injection parameters on the performance of diesel powered screw air compressor which is used in water well application. Oil injection parameters considered in this study are oil injection orifice size, engine speed and oil injection pressure. Performance of the screw compressor is evaluated by measuring the fuel consumption, discharge oil temperature and free air delivery. An experimental design is created using Central Composite Design (CCD). The lower and upper bound of input parameters were fixed based on the application requirement and theoretical estimation. The experiments were performed as per the experimental design and output responses were recorded. The significance of the input parameters on output responses was studied using Analysis of Variance (ANOVA). Response Surface Methodology (RSM) is employed to study the optimum range of parameters to obtain the best performance of the compressor in drilling and idling cycles. The best performance is achieved by setting engine speed value of 1490 rpm, injection orifice diameter of 9.5 mm and an oil injection pressure of 135 psi(g) in the drilling cycle. For idling cycle, an engine speed of 1260 rpm, oil injection orifice diameter of 8.5 mm and an oil injection pressure of 150 psi(g) result in the best performance of compressor in idling cycle. The optimized parameters are validated by conducting confirmatory tests. The water well screw compressor operated with optimum parameters results in considerable savings in the fuel consumption. A lubrication circuit is designed incorporating the optimum nozzle size obtained in this study.

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2019

N. Rakesh, K. Ramesh Kumar, Mohanan, A., K Valsan, V., Sandeep, S., and Prasad, H., “Numerical modeling of Flux assisted Gas Tungsten Arc Welding (F-GTAW) process of Duplex Stainless Steel (DSS)”, Journal of Physics: Conference Series, vol. 1172, p. 012019, 2019.[Abstract]


A three dimensional transient Finite Element Model (FEM) of Flux assisted Gas Tungsten Arc Welding (F-GTAW) to predict the temperature cycle at the weld lineof Duplex Stainless Steel (DSS2205) is proposed in this study using gaussian moving source. Theproposed finite element model is validated using theexperimental results published in the literature. The temperature history plot obtained from the FEM is compared with the experimental data. The results shows that the peak temperature value at a surface node of the FEM is matching with the maximum temperature obtained at the surface of the weld. The proposedmodel has the capability to predict the temperature history of the entire welding cycle. The temperature distribution profile obtained from the FEM of F-GTAW is also compared with the conventional GTAW model. The proposed FEM model has been developed using Ansys software.

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2019

M. Thenarasu, K. Ramesh Kumar, and Anbuudayasankar, S. P., “Multi-criteria decision making approach for minimizing makespan in a large scale press-shop ”, International Journal of Industrial Engineering: Theory Applications and Practice, vol. 26, no. 6, pp. 962-985, 2019.

2018

P. Krishnakumar, K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features”, Intelligent Decision Technologies, vol. 12, pp. 1-18, 2018.[Abstract]


It is important to develop an intelligent tool condition monitoring system to increase productivity and promoting automation in metal cutting process. Many attempts have been made in the past to develop such systems using signals from various sensors such as dynamometer, current, accelerometer, acoustic emission, current and voltage, etc. But the successes of different sensor based systems are limited due to the complexity of tool wear process. The research is still ongoing for improved tool condition monitoring system with applications of advance signal processing techniques and artificial intelligent models. In this study, tool conditions are monitored using the vibration and acoustic emission signatures during high speed machining of titanium alloy (Ti-6Al-4V). Using discrete wavelet transforms wavelets coefficients of vibration and acoustic emission signals are extracted using haar, daubechies, biorthogonal and reverse biorthogonal wavelets. Machine learning algorithms such as decision tree, naive bayes, support vector machine and artificial neural networks are used to predict the tool condition. Results indicate the effectiveness of acoustic emission and vibration data using wavelets for classifying the tool conditions with the aid of machine learning algorithms. A correlation is established between the tool conditions and sensor data. Support vector machine trained by vibration data appears to be predicting the tool conditions with good accuracy compared to decision trees, naive bayes and artificial neural network. Results obtained in this study will be useful to develop an intelligent on-line tool condition monitoring system.

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2018

P. Krishnakumar, K. Ramesh Kumar, and Ramachandran, K. I., “Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach”, International Journal of Computational Intelligence and Applications, vol. 17, p. 1850017, 2018.[Abstract]


Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

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2018

S. Krishnan, A. Dev, S., Suresh, R., A. Sumesh, and K. Ramesh Kumar, “Bottleneck identification in a tyre manufacturing plant using simulation analysis and productivity improvement”, Materials Today: Proceedings, vol. 5, pp. 24720 - 24730, 2018.[Abstract]


It is vital to improve productivity with cutting edge technology for any manufacturing or service industry. Quality with quantity helps a company to stay in competition. Technology has developed greatly to meet the customers’ demand. The elimination of waste with increased productivity leading to hike in profit should be the target of the manufacturing sector. The tyre manufacturing company in which the study was carried out requires reduction in non-value added activities, bottlenecks and processing time to increase productivity. The paper presents the current process simulation undergone in a tyre manufacturing plant. The simulation study found that the actual figures produced in the plant matched the 1170 units per day throughput. The simulation reveals the calendering process as the bottleneck present in the plant. The Pareto analysis used confirms the bottleneck simulation analysis result. Root causes for problems were identified using 5 why analysis and the cause-effect diagram. Solutions were suggested to reduce process times viz the bottleneck experimented, which show an increase in throughput of the tyre manufacturing plant. The bottleneck can be eliminated by modifying the calendering machine to increase productivity by 15.81%. The breakeven time for installation costing 3.5crores will be for 88 days. By going with an automated temperature measuring system, the total productivity can be increased by 18.8% from the existing scenario.

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2018

Sathishkumar V. R., S. P. Anbuudayasankar, and K. Ramesh Kumar, “Optimizing Bi-objective, Multi-echelon Supply Chain Model using Particle Swarm Intelligence Algorithm”, IOP Conference Series: Materials Science and Engineering, vol. 310, p. 012025, 2018.[Abstract]


In the current globalized scenario, business organizations are more dependent on cost effective supply chain to enhance profitability and better handle competition. Demand uncertainty is an important factor in success or failure of a supply chain. An efficient supply chain limits the stock held at all echelons to the extent of avoiding a stock-out situation. In this paper, a three echelon supply chain model consisting of supplier, manufacturing plant and market is developed and the same is optimized using particle swarm intelligence algorithm.

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2018

K. Ramesh Kumar and Rajendran, C., “A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan”, IOP Conference Series: Materials Science and Engineering, vol. 310, p. 012143, 2018.[Abstract]


In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

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2018

K. Ramesh Kumar, “Extension of PSO and ACO-PSO algorithms for solving Quadratic Assignment Problems”, IOP Conference Series: Materials Science and Engineering, vol. 377, p. 012192, 2018.[Abstract]


In this paper, PSO and Ant Colony Optimization inspired PSO (ACO-PSO) algorithms were adopted to solve the Quadratic Assignment Problems. A hybrid approach is adopted in this paper by combining assignment construction with local-search. In the PSO algorithm, solution construction has been carried out by assigning weights to current, particle’s best and global best solutions associated with assignment of resources. Velocities which are used to construct the assignments in this approach are similar to the trail intensities considered in the ant colony algorithms. The proposed algorithms have been applied to a set of benchmark problems and the performance of the algorithm is evaluated by testing the obtained results with the results published in the literature. The computational results show that good quality solutions are obtained using the PSO and ACO inspired PSO algorithm.

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2018

Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Acoustic emission based tool condition classification in a precision high speed machining of titanium alloy (Ti-6Al-4V): A machine learning approach”, International Journal of Computational Intelligence and Applications, vol. 17, 2018.[Abstract]


Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains. © 2018 World Scientific Publishing Europe Ltd.

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2018

Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers”, International Journal of Prognostics and Health Management, vol. 9, no. 8, pp. 2153-2648, 2018.[Abstract]


To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature. © 2018, Prognostics and Health Management Society. All rights reserved.

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2018

K. Ramesh Kumar, Krishna Kumar 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.

2018

Dinu Thomas Thekkuden, Santhakumari, A., A. Sumesh, Abdel-Hamid I. Mourad, and K. Ramesh Kumar, “Instant Detection of Porosity in gas metal arc welding by using probability density distribution and control chart”, The International Journal of Advanced Manufacturing Technology, vol. 95, no. 9-12 , pp. 4583–4606, 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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2017

K. Ramesh Kumar, M. Thennarasu, 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, pp. 340-364, 2017.

2017

K. Ramesh Kumar 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

A. Sumesh, K. Ramesh Kumar, Raja, A., K. Mohandas, 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.

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2015

A. Sumesh, K. Ramesh Kumar, 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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2015

A. Sumesh, Dinu Thomas Thekkuden, Dr. Binoy B. Nair, K. Ramesh Kumar, and K. Mohandas, “Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms”, Applied Mechanics and Materials , 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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2013

P. K Marimuthu, Krishna Kumar P., K. Ramesh Kumar, 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

R. S. Kadadevaramath, Chen, J. C. H., B. Shankar, L., and K. Ramesh Kumar, “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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2012

K. Ramesh Kumar, 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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2011

H. Ganesan, .G., M., Ganesan, K., and K. Ramesh Kumar, “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.

2011

K. Ramesh Kumar, 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

R. Karthi, Rajendran, Cb, and K. Ramesh Kumar, “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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2009

R. .S., Mohanasudaram, K. M., K. Ramesh Kumar, 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

R. S. Kadadevaramath, Prasad, P. S. S., Mohanasundaram, K. M., Immanuel, E. A., and K. Ramesh Kumar, “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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Publication Type: Conference Proceedings

Year of Publication Title

2018

A. Arun, K. Ramesh Kumar, Unnikrishnan, D., and A. Sumesh, “Tool Condition Monitoring of Cylindrical Grinding Process Using Acoustic Emission Sensor”, Materials Today: Proceedings, vol. 5, no. 5. pp. 11888-11899, 2018.[Abstract]


In this work, an experimental setup has been established consisting of a cylindrical grinding machine with piezo-electric sensor for capturing acoustic emission and its related hardware and software for signal processing. Acoustic signals are captured for the entire grinding cycle until the abrasive grains of the girding wheel become dull. Surface roughness produced by the process is recorded at fixed time intervals from the beginning to the end of the grinding cycle. Various features of the acoustic emission signatures such as root mean square, amplitude, ring-down count, average signal level are extracted from the time-domain are compared and correlated with the surface roughness generated by the grinding wheel on the work-piece. Good condition and dull condition of the grinding wheel is predicted using machine-learning techniques such as decision tree, artificial neural network, and support vector machine. Results indicate that there is a strong correlation exiting between the acoustic emission features and the surface roughness produced by the grinding process. Support vector machine trained with cubic kernel is appears to be predicting the grinding tool condition with greater accuracy comparing with decision tree algorithm and artificial neural network considered in this study.

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2017

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


In the current globalized scenario, business organizations are more dependent on cost effective supply chain to enhance profitability and better handle competition. Demand uncertainty is an important factor in success or failure of a supply chain. An efficient supply chain limits the stock held at all echelons to the extent of avoiding a stock-out situation. In this paper, a three echelon supply chain model consisting of supplier, manufacturing plant and market is developed and the same is optimized using particle swarm intelligence algorithm.

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2017

A. A., K. Ramesh Kumar, Unnikrishnan D., and A. Sumesh, “Tool Condition Monitoring of Cylindrical Grinding Process Using Acoustic Emission Sensor”, ICMMM2017. Materials Today: Proceedings, VIT, Vellore campus, India, pp. 11888-11899, 2017.[Abstract]


In this work, an experimental setup has been established consisting of a cylindrical grinding machine with piezo-electric sensor for capturing acoustic emission and its related hardware and software for signal processing. Acoustic signals are captured for the entire grinding cycle until the abrasive grains of the girding wheel become dull. Surface roughness produced by the process is recorded at fixed time intervals from the beginning to the end of the grinding cycle. Various features of the acoustic emission signatures such as root mean square, amplitude, ring-down count, average signal level are extracted from the time-domain are compared and correlated with the surface roughness generated by the grinding wheel on the work-piece. Good condition and dull condition of the grinding wheel is predicted using machine-learning techniques such as decision tree, artificial neural network, and support vector machine. Results indicate that there is a strong correlation exiting between the acoustic emission features and the surface roughness produced by the grinding process. Support vector machine trained with cubic kernel is appears to be predicting the grinding tool condition with greater accuracy comparing with decision tree algorithm and artificial neural network considered in this study.

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2009

R. Karthi, Arumugam, S., and K. Ramesh Kumar, “A Novel Discrete Particle Swarm Clustering Algorithm for Data Clustering”, COMPUTE '09 Proceedings of the 2nd Bangalore Annual Compute Conference, vol. 16. ACM, New York, NY, USA, 2009.[Abstract]


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.

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2005

K. Ramesh Kumar, 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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Publication Type: Conference Paper

Year of Publication Title

2017

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

2017

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

2017

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

2017

A Sumesh, K. Ramesh Kumar, ,, 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.

2013

A. Sumesh, Srikant. G. V., K. Ramesh Kumar, Rajasekaran. N, and Shyambabu. R, “Weld parameter optimization for SMAW process using Central Composite Design ”, in IWSCWS, 2013.