Qualification: 
Ph.D
Email: 
j_gokul@cb.amrita.edu

Dr. Gokulachandran J. joined Amrita in the year 1996 with a year of experience in Academic as well as Industry and Research. He received his B. E. in Mechanical Engineering and M. E. in Industrial Engineering from Bharathiyar University, Coimbatore. He did his doctoral dissertation from Amrita Vishwa Vidyapeetham. His doctoral dissertation work was focused on: Prediction of Remaining Useful Life of Used Components of Systems for Reuse.

He is interested in teaching as well as in research. He has published ten papers in international journals and conferences and he has served as reviewer for journals International Journal of Quality & Reliability Management and Journal of Engineering Science & Technology.

He is a Life Member of Indian Society for Technical Education (ISTE). He has guided several M. Tech. and B. Tech. projects. And his research interest includes Green manufacturing, Prognostics, Risk Management, Agile Manufacturing, Optimisation using soft computing techniques, Reliability analysis.

Education

YEAR DEGREE/PROGRAM INSTITUTION
2013 PhD Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore,Tamil Nadu, India.
1999 M. E., Industrial Engineering PSG College of Technology, Bharathair University,Coimbatore,Tamil Nadu,India.
1993 B. E., Mechanical Engineering Govt. College of Technology,Bharathair University Coimbatore,Tamil Nadu,India.

Work Experience

Year Affiliation
August 2021 – till date Professor
Department of Mechanical Engineering
Amrita VishwaVidyapeetham
July 2013 – August 2021 Associate Professor
Department of Mechanical Engineering
Amrita VishwaVidyapeetham
July 2005 – June 2013 Assistant Professor
Department of Mechanical Engineering
Amrita VishwaVidyapeetham
Nov 2001-June 2005 Senior Lecturer
Department of Mechanical Engineering
Amrita VishwaVidyapeetham
Aug 1996 – Oct 2001 Lecturer
Department of Mechanical Engineering
Maharaja Engineering College, Coimbatore
Dec 1994 - Aug 1996 Lecturer
Department of Mechanical Engineering
Maharaja Engineering College, Coimbatore
Jan 1994 – Dec 1994 Associate Lecturer
Arulmigu Chandikeswrarer Polytechnic,Coimbatore

Courses Handled

  • Engineering drawing
  • Machine drawing
  • Design of machine elements
  • Process planning and cost estimation
  • Total quality management
  • Enterprise management
  • Managerial statistics
  • Quality control and reliability engineering
  • Optimization techniques in engineering
  • Simulation modeling of manufacturing
  • Theory of machines
  • Reliability Engineering
  • Quantitative methods
  • Simulation of manufacturing systems

Reviewer – Journals

  1. Multi discipline modeling in materials and structure
  2. International journal of quality and Reliability Management

Thrust Area of Research

Green Manufacturing

Teaching/ Research Interests

MajorSubjects Taught

  • Operations Research
  • Product Cost Estimation
  • Reliability Engineering
  • Product Design and Quality Management
  • Engineering Drawing

Research Interests

  • Green Manufacturing
  • Reliability Engg

List of Ph.D. Students

  • Two

Key Responsibilities at AmritaVishwa Vidyapeetham

  • Lab and workshop management
  • NAAC coordinator
  • Member in NBA committee
  • Safety committee coordinator

Membership in Professional Bodies

  1. Life member –Institution of Engineers

Others

  1. BOS member in Amrita Vishwa Vidyapeetham
  2. BOS member in Anna University (KIT)
  3. Doctoral committee member in Anna University

Publications

Publication Type: Journal Article

Year of Publication Title

2020

K. K. Natarajan and Dr. Gokulachandran J., “Artificial Neural Network Based Machining Operation Selection for Prismatic Components”, International Journal of Advanced Science, Engineering and information Technology, vol. 10, no. 2, pp. 618–628, 2020.[Abstract]


Abstract— Computer-aided process planning systems are used to assist human planners in producing better process plans. New artificial intelligence techniques play a significant role in CAPP. CAPP research includes neural network approaches, knowledgebased techniques, Petri nets, agent-based, fuzzy set theory, genetic algorithm, Standard for the Exchange of Product model data (STEP)-Compliant CAPP, and Internet-based techniques. This study deals with the application of the Artificial Neural Network techniques (ANN) in CAPP because of their learning ability and massive potential toward dynamic planning. This study focuses on the usage of artificial neural networks machining operation selection and sequences of operations for prismatic components. The intelligent CAPP system suggests the best machining operation and its sequences for the prismatic components using tolerances, material requirements, and surface finish details. The process planning of machining features in part is the starting point. An enormous amount of knowledge is required for part feature process planning, like selecting proper material, size, stock, dimensional tolerance, and surface finish. In this work, various prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face are taken and details like material, size, stock, dimensional tolerance and surface finish are properly normalized and given as input to neural networks to find the required sequence of machining operation. LevenbergMarquidt algorithm was used to train the networks and was found very effective in operation sequence selection. A sample prismatic component with nine features have been analyzed and found to be more productive. Levenberg Marquidt algorithm is then compared with the conjugant space algorithm, and it is found that the former produces less error in outputs compared to them later.

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2019

Sreejyothi, M. Thennarasu, and Dr. Gokulachandran J., “The Engine Testing Work-flow Analysis through Value Stream Mapping and Simulation”, International Journal of Mechanical and Production Engineering Research and Development (IJMPERD), vol. 9, no. 2, pp. 477-484, 2019.

2019

Dr. Ilangovan S., Vaira Vignesh R., Dr. Padmanaban R., and Dr. Gokulachandran J., “Effect of composition and aging time on hardness and wear behavior of Cu-Ni-Sn spinodal alloy”, Journal of Central South University, vol. 26, no. 10, pp. 2634-2642, 2019.[Abstract]


Copper alloyed with various compositions of nickel and tin were cast into molds under argon atmosphere. The cast rods were homogenized, solution heat treated, followed by aging for different time duration. The specimens were characterized for microstructure and tested for microhardness and wear rate. A hybrid model with a linear function and radial basis function was developed to analyze the influence of nickel, tin, and aging time on the microhardness and tribological behavior of copper-nickel-sin alloy system. The results indicate that increase in the composition of nickel and tin increases the microhardness and decreases the wear rate of the alloy. The increase in the concentration of nickel and tin decreases the peak aging time of the alloy system. © 2019, Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature.

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2018

Dr. Gokulachandran J. and Dr. Padmanaban R., “Prediction of Remaining Useful life of Cutting Tools: A Comparative Study using Soft Computing Methods”, International Journal of Process Management and Benchmarking, vol. 8, no. 2, pp. 156-181, 2018.[Abstract]


Predicting the remaining useful life of the partially degraded components and putting them to use will help to save natural resources to a great extent. This will reduce the overall cost, energy and protects environment. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. This paper presents the use of three soft computing methods, namely, artificial neural network, neuro fuzzy logic and support vector regression methods for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid three soft computing methods and RUL obtained from these values are compared. It is found that the predictive neuro fuzzy method is capable of giving a better prediction of remaining useful tool life than the other methods.

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2015

Dr. Gokulachandran J. and K. Mohandas, “Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools”, Journal of Intelligent Manufacturing, vol. 26, pp. 255–268, 2015.[Abstract]


Reuse of partially worn-out materials and parts is a philosophy now being applied in all manufacturing industries to achieve the goal of green manufacturing. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. This exercise will in turn reduce the overall cost and help achieve enhanced productivity. This paper reports the use of two soft computing techniques, namely, neuro fuzzy logic technique and support vector regression technique for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid two soft computing techniques and RUL obtained from these values are compared. More »»

2015

Dr. Gokulachandran J. and Mohandas, K., “Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques”, International Journal of Quality & Reliability Management, vol. 32, pp. 270-290, 2015.[Abstract]


Purpose – The accurate assessment of tool life of any given tool is a great significance in any manufacturing industry. The purpose of this paper is to predict the life of a cutting tool, in order to help decision making of the next scheduled replacement of tool and improve productivity. Design/methodology/approach – This paper reports the use of two soft computing techniques, namely, neuro-fuzzy logic and support vector regression (SVR) techniques for the assessment of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Findings – The analysis is carried out using the two soft computing techniques. Tool life values are predicted using aforesaid techniques and these values are compared. Practical implications – The proposed approaches are relatively simple and can be implemented easily by using software like MATLAB and Weka. Originality/value – The proposed methodology compares neuro – fuzzy logic and SVR techniques. More »»

2015

M. Nithin and Dr. Gokulachandran J., “Risk assessment and management in a manufacturing industry”, International Journal of Applied Engineering Research, vol. 10, pp. 17303-17314, 2015.[Abstract]


An industry is always prone to hidden risks. This study provides an overview on the identification, assessment and management of risks in a manufacturing industry. A conceptual model was developed for the effective Risk Management. A case study was performed for Risk identification through observation and interview with company personnel and Risk assessment by a) Risk classification and b) Risk prioritization using Analytical Hierarchy Process (AHP). Suggestions for risk control were also put forward to the company along with the conceptual model. In this study, suggestions were also made to document the Risk identification, Risk assessment, and Risk control continuously for every update. The suggestions for effective risk control, were made based on the severity of each risk and cost effectiveness. © Research India Publications. More »»

2015

K. S. Sangeeth Kumar and Dr. Gokulachandran J., “Implementation of cleaner production strategies in a manufacturing industry”, International Journal of Applied Engineering Research, vol. 10, pp. 17291-17302, 2015.[Abstract]


Cleaner Production (CP) strategies are basically concerned with environmental sustainability, maximization of waste reduction, recycling, and reuse. This research has the purpose to identify the use of resources and sources of pollution in a cleaning equipments manufacturing and testing factory. The principles of clean technology are applied at the same factory for reducing air pollution and waste water standardization. The research focuses on the effect of Clean Technology (CT) implementation on different causes of air pollution and wastewater. The main application of clean technology is to minimize air pollution in the welding area (production area), and to develop a better system for wastewater treatment. Installation of local exhaust ventilation with a proper design can prevent the emission of welding fumes to the atmosphere and implementation of a treatment plant will help for the reuse of water. Both can give the company a better environment friendly and working atmosphere. A case study is carried out in a cleaning equipment manufacturing company. © Research India Publications. More »»

2013

Dr. Gokulachandran J. and Mohandas, K., “Application of artificial neural network and fuzzy logic method for remaining useful life assessment of cutting tools”, International Journal of Logistics and Supply Chain management, vol. 5, pp. 9-19, 2013.

2013

Dr. Gokulachandran J., K. Mohandas, and Dr. Padmanaban R., “Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools”, Journal of Intelligent Manufacturing, vol. 26, pp. 255-268, 2013.[Abstract]


Reuse of partially worn-out materials and parts is a philosophy now being applied in all manufacturing industries to achieve the goal of green manufacturing. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. This exercise will in turn reduce the overall cost and help achieve enhanced productivity. This paper reports the use of two soft computing techniques, namely, neuro fuzzy logic technique and support vector regression technique for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid two soft computing techniques and RUL obtained from these values are compared. © 2013 Springer Science+Business Media New York.

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2012

Dr. Gokulachandran J. and Mohandas, K., “Application of Regression and Fuzzy Logic Method for Prediction of Tool Life”, Procedia Engineering, vol. 38, pp. 3900 - 3912, 2012.[Abstract]


This paper presents a model for predicting tool life when end milling IS2062 steel using P30 uncoated carbide tipped tool under various cutting conditions. A tool life model is developed from regression model obtained by using results of the experiments conducted based on Taguchi's approach. A second model is developed based on fuzzy logic method for predicting tool life. The results obtained from fuzzy method are compared with regression model. The results of the fuzzy model is found to be more closer to experimental values More »»

2012

Dr. Gokulachandran J. and Mohandas, K., “Tool life prediction model using regression and artificial neural network analysis”, International Journal of Production and Quality Engineering, vol. 3, no. 1, pp. 9-16, 2012.

2012

Dr. Gokulachandran J. and K. Mohandas, “Predicting remaining useful life of cutting tools with regression and ANN analysis”, International Journal of Productivity and Quality Management, vol. 9, pp. 502-518, 2012.[Abstract]


In manufacturing industry, cutting tools are often discarded when much of their potential life still remains. Predicting the remaining useful life of the partially degraded components and putting them to use will help to save natural resources to a great extent. This saves manufacturing cost and protects environment. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. In this work, based on Taguchi approach, experiments are conducted and tool life values are obtained. The analysis is carried out in two stages. In the first stage, a regression model is proposed for the prediction of remaining life of carbide-tipped tools. In the second stage, an artificial neural network model is developed for predicting tool life. The results of both models are compared. © 2012 Inderscience Enterprises Ltd.

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2005

S. Ra Devadasan, Goshteeswaran, Sb, and Dr. Gokulachandran J., “Design for quality in agile manufacturing environment through modified orthogonal array-based experimentation”, Journal of Manufacturing Technology Management, vol. 16, pp. 576-597, 2005.[Abstract]


Purpose - To provide a modified orthogonal array-based model for enabling the researchers and practitioners to exploit the technique, "design of experiments" in an agile manufacturing environment. Design/methodology/ approach - The characteristics of Taguchi's off-line models and agile manufacturing were studied. A theoretical model of modified orthogonal array-based experimentation was designed. This model was subjected to implementation study in an Indian pump-manufacturing company. Findings - The model contributed in this paper has shown its feasibility in achieving quality in agile manufacturing environment. Research limitations/implications - The authors are residing in an Indian city where the majority of the companies have not adopted agile manufacturing criteria. Hence, it was not possible to carry out implementation study in an agile manufacturing company. Future researchers should examine the practical validity of the proposed model in agile manufacturing companies. Practical implications - Since the manufacturing organizations are fast becoming agile, due to the customers' dynamic demands coupled with competition, the traditional quality improvement techniques are becoming obsolete. The model contributed in this paper is found to be useful in achieving continuous quality improvement in AM environment. Hence the model would be a useful technique for today's practitioners whose activities are increasingly focused towards achieving agility in manufacturing. Originality/value - The literature survey covering articles on agile manufacturing indicates that no researcher or practitioner has contributed a model that would exploit the technique, "design of experiments" in an agile manufacturing environment. Hence the proposed model is expected to be of high value for researchers and practitioners to explore the way of achieving continuous quality improvement in agile manufacturing environment. © Emerald Group Publishing Limited.

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Publication Type: Book Chapter

Year of Publication Title

2018

Dr. Ilangovan S., Vaira Vignesh R., Dr. Padmanaban R., and Dr. Gokulachandran J., “Comparison of statistical and soft computing models for predicting hardness and wear rate of Cu-Ni-Sn alloy”, in Progress in Computing, Analytics and Networking, Advances in Intelligent Systems and Computing, vol. 710, , Ed. Springer Verlag, 2018, pp. 559-571.[Abstract]


Castings of Copper–Nickel–Tin alloy were produced by varying the composition of Ni and Sn. The cast specimens were subjected to homogenization and solution treatment. The specimens were characterized for microstructure, hardness and subjected to adhesive wear test. Statistical regression model, artificial neural network model and Sugeno fuzzy model were developed to predict the hardness and wear rate of the alloy based on %Ni, %Sn and ageing time of the specimens. As Sugeno Fuzzy logic model uses adaptive neuro-fuzzy inference system, an integration of neural networks and fuzzy logic principles, the prediction efficiency was higher than statistical regression and artificial neural network model. The interaction effect of %Ni, %Sn and ageing time on the hardness and wear rate of the specimens were analysed using the Sugeno Fuzzy model. © Springer Nature Singapore Pte Ltd. 2018.

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Publication Type: Conference Proceedings

Year of Publication Title

2016

Vaira Vignesh R., Dr. Padmanaban R., Arivarasu, M., Karthick, K. P., Sundar, A. A., and Dr. Gokulachandran J., “Analysing the strength of friction stir spot welded joints of aluminium alloy by fuzzy logic”, IOP Conference Series: Materials Science and Engineering (International conference on advances in Materials and Manufacturing Applications(ICONAMMA 2016)), vol. 149. Institute of Physics Publishing, p. 012136, 2016.[Abstract]


Friction stir spot welding (FSSW) is a recent joining technique developed for spot welding of thin metal sheets. This process currently finds application in automotive, aerospace, marine and sheet metal industry. In this work, the effect of FSSW process parameters namely tool rotation speed, shoulder diameter and dwell time on Tensile shear failure load (TSFL) is investigated. Box-Behnken design is selected for conducting experiments. Fuzzy based soft computing is used to develop a model for TSFL of AA6061 joints fabricated by FSSW. The interaction of the process parameters on TSFL is also presented. © Published under licence by IOP Publishing Ltd.

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2016

Vaira Vignesh R., Dr. Padmanaban R., Arivarasu, M., Dr. Thirumalini S., Dr. Gokulachandran J., and Ram, M. Sesha Saty, “Numerical modelling of thermal phenomenon in friction stir welding of aluminum plates”, IOP Conference Series: Materials Science and Engineering, vol. 149. p. 012208, 2016.[Abstract]


Friction stir welding (FSW) is a solid state welding process with potential to join materials that are non weldable by conventional fusion welding techniques. The study of heat transfer in FSW aids in the identification of defects like flash, inadequate heat input, poor material flow and mixing etc. In this paper, transient temperature distribution during FSW of aluminum alloy AA6061-T6 was simulated using finite element modelling. The model was used to predict the peak temperature and analyse the thermal history during FSW. The effect of process parameters namely tool rotation speed, tool traverse speed (welding speed), shoulder diameter and pin diameter of tool on the temperature distribution was investigated using two level factorial design. The model results were validated using the experimental results from the published literature. It was found that peak temperature was directly proportional to tool rotation speed and shoulder diameter and inversely proportional to tool traverse speed. The effect of pin diameter on peak temperature was found to be trivial.

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2012

Dr. Gokulachandran J. and K, M., “Prediction of tool life using fuzzy method”, International Conference on Recent Advances in Mechanical Engineering. MGR university, Chennai, pp. 7-15, 2012.

2011

Dr. Gokulachandran J. and K, M., “Assessment of reusability of cutting tools for reuse using machine learning approach”, International Conference on Simulation Modeling and Analysis (COSMA2011). Amrita School of Engineering, Coimbatore, pp. 663-673, 2011.

2010

Dr. Gokulachandran J. and K, M., “Tool life prediction model for uncoated carbide tool using regression analysis and artificial neural network”, International Conference on Technological Trends. College of Engineering, Trivandrum, pp. 666-671, 2010.