09495460701/ 9486336526/ 0495 2260701

Sumesh A. currently serves as Assistant Professor at Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. His areas of research include Optimization and System Simulation.


  • Currently Pursuing : Doctorate Ph. D.
    Specialization : Welding Signature Analysis
    University : Amrita Vishwa Vidyapeetham
  • 2006 : M. Tech.
    Specialization : Manufacturing Engineeing
    University : Amrita Vishwa Vidyapeetham
  • 2004 : B. Tech.
    Specialization : Mechanical Engineering
    University : Pondicherry University
  • 2000 : Diploma
    Specialization : Automobile Engineeing
    Board : State Board of Technical Education - Kerala
  • 1997 : THSLC
    Specialization : Maintenance of Two & Three wheeler
    Board : State Board of Technical Education - Kerala


  • August 23, 2008 - Present :

Assistant Professor Sr.Gr.
Amrita Vishwa Vidyapeetham

  • July 16, 2008 - August 18, 2008 :

NIT Calicut

  • August 20, 2007 - July 15, 2008 :

AWH Engineering College

  • February 20, 2006 - August 20, 2007 :

Calicut University Institute of Engineering & Technology

  • July 19, 2004 - August 16, 2004 :

Guest Lecturer
Govt. Polytechnic Calicut


  • PG Thesis : Process Parameter Optimization for Machining of ZrB2 +SiC Composites using Electrical Discharge Machining
  • Ph.D. Thesis : Weld quality control using signature analysis for GMAW process (Tentative)

Major Student Projects Guided/ Co -Guided

Year (10 years) Title of the Project/Thesis Number of Students/Group Industry Project / In-house Outcome
2018 -2019 Development of cutting fluid assisted machining system for machining hard to cut materials. 4 In-house Ongoing
Weld quality study using acoustics emission signals. 4
2017 -2018 roductivity improvement and defect analysis in a valve manufacturing Plant 4 Industry Completed
2016 -2017 An investigation into machining characteristics of Titanium alloy using Regression analysis and Hidden Markovian model. 4 In-house Completed
Simulation analysis to improve productivity of a Tyre Manufacturing plant 3 Industry
2015 -2016 Process Analysis Using Discrete Event Simulation To Improve Mould Shop Productivity 3 Industry Completed
2014 -2015 Optimization of process parameters for pipe welding of SA106 Grade B steel by DOE approach 4 In-house Completed
2013 -2014 Design and fabrication of a blanking die for a cup drawing  operation 4 In-house Completed
2010-11 Design and fabrication of pipe bending machine 4 In-house Completed
Modelling a job shop using arena 4
2009-10 Optimization of process parameters in machining of titanium alloys 3
Year (10 years) Title of the Project/Thesis Industry Project / In-house Outcome
2018 -2019 Developing an intelligent statistical model using fuzzy rules for predicting fuel consumption parameters / fuel saving in diesel powered screw air compressor, Dhayanandh K Industry ongoing
Optimize and correlate welding parameters by DOE to reduce porosity in GMAV welding process, Kuppusamy. A
2017 -2018 Study on weldability and mechanical properties of aluminium alloy – stainless steel weld”, SACHIN.R (CB.EN. P2MFG16018). In-house Completed
Experimental investigation and optimizing process parameters of a-tig & tig welding for aisi 316l austenitic stainless steel using response surface methodology”, MERRIN JOHN VARKEY(CB.EN. P2MFG16015)
2016 -2017 Effect of process parameters on weld Quality using keyhole plasma arc welding ARUN KUMAR G, (CB.EN.P2MFG15003) Industry Completed
2015 -2016 NIL
2014 -2015 Research into inverter based gmaw power sources for low heat input applications; Ashidh K.(CB.EN.P2MFG13004).  Industry Completed
Detection of defects by statistical process control and probability density distribution in gas metal arc welding; DINU THOMAS THEKKUDEN (CB.EN.P2MFG13006).
2012-13 Current -voltage  Signature  analysis  of gmaw  process for identification  and  classification of weld defects; Shyam Babu R, CBEN.P2.IDM11014 Industry Completed
Fusion characteristic’s optimization for welding of chrome moly steels (T91) using gmaw process; G.V.SRIKANTH, (CB.EN.P2IDM11015)
Structural dynamic model update of aerospace  structures; PRASANTH S (CB.EN.P2IDM11008)
2011-12 Thin wall machining In-house Completed

Funded Research Grant


Publication Type: Conference Proceedings

Year of Publication Title


P. Deepak, V. M. Latheesh, A. Sumesh, Unnikrishnan D., and A. Santhakumari, “A Finite Element Analysis of Dissimilar Materials Diffusion Bonded Joints”, ICMMM2017. Materials Today: Proceedings, VIT, Vellore campus, India, pp. 12484-12489, 2018.


A. A., Rameshkumar, K., 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.


A. Arun, Kumar, R., Dr. Saimurugan M., and A. Sumesh, “Experimental Evaluation of Grinding Wheel Wear Using Vibration Based Technique”, 2nd International Conference on Intelligent Robotics, Automation and Manufacturing (IRAM 2013). Emerald group, IIT- Indore, p. 364, 2013.

Publication Type: Journal Article

Year of Publication Title


A. Sumesh, Dr. Binoy B. Nair, Rameshkumar, K., Santhakumari, A., Raja, A., and Mohandas, K., “Decision tree based weld defect classification using current and voltage signatures in GMAW process”, Materials Today: Proceedings , vol. 5, no. 2, pp. 8354-8363, 2018.[Abstract]

The quality of the weld is most important in industries manufacturing boilers and pressure vessels which will work in severe operating conditions. In an automated environment, developing a process monitoring and control system will ensure the weld quality and prevent the occurrence of defects. In this paper, an attempt is made using the decision tree algorithm to establish a correlation between the current and voltage signatures with the quality of the weld. Carbon steel plates are welded using GMAW processes and experimental design is established to obtain weld without any defects (good weld) and weld with porosity and burn-through defects. “KUKA” robotic GMAW welding setup integrated with “Fronius” power source is used in this study for experimentation. “TVC” data acquisition system is used to capture the current and voltage signatures. Statistical features are extracted from the current and voltage signatures. Decision tree algorithm with split criterions such as “gini index”, “towing”, and “deviance” are used to classify the weld defects. Results indicate the effectiveness of decision tree algorithms in classifying the weld defects using the current and voltage signatures.

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P. Deepak, V. M. Latheesh, A. Sumesh, Unnikrishnan D., and A. Santhakumari, “A Finite Element Analysis of Dissimilar Materials Diffusion Bonded Joints”, Materials Today: Proceedings, vol. 5, pp. 12484-12489, 2018.[Abstract]

Diffusion bonding is one of the solid state joining process, in which two clean metallic surfaces intended for joining were brought into contact at elevated temperatures under optimum pressure. In this work, an attempt is made to study the analysis and simulation of the two dissimilar materials Titanium alloy (Ti-6Al-4V) and Stainless Steel (SS304) which is having a wide application in the area of aerospace. Structural analysis was carried out to determine the equivalent stress, elongation and total deformation of the welded joint. Explicit Dynamics analysis using Ansys was used to predict the strength of Ti-6Al-4V/SS304 diffusion bonded joint. The result indicates that Equivalent stress is attained at 1.393GPa and total deformation is 0.00275m obtained at a time 0.000041seconds. The analysis shows that the fracture occurs in the region of titanium alloy and not in the region of HAZ.

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A. Arun, Rameshkumar, K., 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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P. R. Nithin, Gopikrishnan, S., and A. Sumesh, “A case study on Simulation and Design optimization to improve Productivity in cooling tower manufacturing industry”, IOP Conference Series: Materials Science and Engineering, vol. 310, 2018.[Abstract]

Cooling towers are the heat transfer devices commonly found in industries which are used to extract the high temperature from the coolants and make it reusable in various plants. Basically, the cooling towers has Fills made of PVC sheets stacked together to increase the surface area exposure of the cooling liquid flowing through it. This paper focuses on the study in such a manufacturing plant where fills are being manufactured. The productivity using the current manufacturing method was only 6 to 8 fills per day, where the ideal capacity was of 14 fills per day. In this plant manual labor was employed in the manufacturing process. A change in the process modification designed and implemented will help the industry to increase the productivity to 14. In this paper, initially the simulation study was done using ARENA the simulation package and later the new design was done using CAD Package and validated using Ansys Mechanical APDL. It's found that, by the implementation of the safe design the productivity can be increased to 196 Units. © Published under licence by IOP Publishing Ltd.

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Dinu Thomas Thekkuden, Santhakumari, A., A. Sumesh, Abdel-Hamid I. Mourad, 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, 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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A. Sumesh, Rameshkumar, K., 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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A. Sumesh, Ramnadh, L. V. Sai, Manish, P., Harnath, V., and Lakshman, V., “A Computational approach in optimizing process parameters of GTAW for SA 106 Grade B steel pipes using Response surface methodology”, IOP Conference Series: Materials Science and Engineering, vol. 149, no. 1-9, p. 012038, 2016.[Abstract]

Welding is one of the most common metal joining techniques used in industry for decades. As in the global manufacturing scenario the products should be more cost effective. Therefore the selection of right process with optimal parameters will help the industry in minimizing their cost of production. SA 106 Grade B steel has a wide application in Automobile chassis structure, Boiler tubes and pressure vessels industries. Employing central composite design the process parameters for Gas Tungsten Arc Welding was optimized. The input parameters chosen were weld current, peak current and frequency. The joint tensile strength was the response considered in this study. Analysis of variance was performed to determine the statistical significance of the parameters and a Regression analysis was performed to determine the effect of input parameters over the response. From the experiment the maximum tensile strength obtained was 95 KN reported for a weld current of 95 Amp, frequency of 50 Hz and peak current of 100 Amp. With an aim of maximizing the joint strength using Response optimizer a target value of 100 KN is selected and regression models were optimized. The output results are achievable with a Weld current of 62.6148 Amp, Frequency of 23.1821 Hz, and Peak current of 65.9104 Amp. Using Die penetration test the weld joints were also classified in to 2 categories as good weld and weld with defect. This will also help in getting a defect free joint when welding is performed using GTAW process.

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N. Rajasekaran, Ashidh, K., A Kumari, S., and A. Sumesh, “Influence of Stick-Slip Effect on Gas Metal Arc Welding”, Applied Mechanics and Materials , vol. 813, pp. 438-445, 2015.[Abstract]

A key factor in the performance of Gas Metal Arc welding (GMAW) is the feedability of the filler wire. Variations in the wire feed speed (WFS) are caused by adverse conditions during welding. These include damaging effects such as "stick-slip" motion of the welding wire, premature wear of the contact tube and, in general, the conduit cable usage during welding. Experiments were conducted to study these issues. The welding parameters such as arc current and voltage were captured by using data logger and the stick-slip effect was analyzed. It was observed that arc waveforms were found to have significant influence because of the stick-slip effect. The corresponding fusion characteristics of the weldments were studied. The voltage change was suggested to specific conduit cable configuration.

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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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A. Sumesh, Dinu Thomas Thekkuden, Dr. Binoy B. Nair, Rameshkumar, K., 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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Publication Type: Conference Paper

Year of Publication Title


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


A. Sumesh, Shyambabu. R, Rameshkumar., K., Santhakumari. A, and Srikant. G. V, “Signature analysis using arc sound for SMAW process ”, in IWSCWS, 2013.


A. Sumesh and K. Ramesh Kumar, “PROCESS PARAMETER OPTIMIZATION IN MACHINING OF ZRB2+SIC COMPOSITES”, Amrita Vishwa Vidyapeetham -Coimbatore, 2011.


A. Sumesh, K. Ramesh Kumar, Austin, C. I. B. Y., and Krishnakumar, P., “Productivity Improvement of a Manufacturing Industry Using Value Stream Mapping (VSM) Approach: A case Study in a Discrete Ma1nufacturing Sector”, in ORSI , 2010.


A. Sumesh, K. Ramesh Kumar, and Krishnakumar, P., “SIMULATION OPTIMIZATION IN A KANBAN CONTROLLED FLOW SHOP”, in ORSI – 2008/TIRUPATHI, 2008.

Seminars, Workshops and Conferences Organised

S. No. Name of the International Seminar / Conference Organised Source of Funding
1 Recent Trends In Machining- 2011 ISRO & DRDO
2 Recent trends in Manufacturing -2014 ISRO
3 RTM –Condition Monitoring -2015  
4 RTM – Welding signature analysis -2016  
5 COSMA 2011  

Guest Lecture

  • Government Engineering College, Calicut – Design of Experiments

Subjects Taught

  • Metallurgy and Material Science
  • Manufacturing Process 1 & 2
  • Operations Research
  • Manufacturing Technology,
  • Manufacturing System Simulation
  • Automobile Engineering,
  • Instrumentation & Control,
  • Engineering Thermodynamics,
  • Design of Experiments etc.


  • Class Advisor : 2017 - 21 Batch, 13 -17 Batch
  • Class Counsellor : 2009 -13 Batch
  • Course Mentor : Manufacturing Process 1, Metallurgy and Material science, Operations research, Automobile Engineering. , instrumentation & control etc.
  • Coordination Work during Celebrations like Gurupurnima, Gokulastami : DEPARTMENT COORDINATOR, FLOAT IN CHARGE, PROCESSION CONTROL. NAAC Accreditation work,
  • Student Welfare and Discipline : COLLEGE DAY, AMRITOLSAVAM, Department student asosation.
  • Professional Organization of Teachers : ISTE
  • University or Department Administration Work
  • Alumni Support : Department Coordinator