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

Dr. T. Mohanraj currently serves as an Assistant Professor (Sr. Gr.) at the Department of Mechanical Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore. He received B.E., M.E.  and Ph. D. Degrees from Anna University, Chennai.

T. Mohanraj received the “Certified LabVIEW Associate Developer” certification from National Instruments, Bangalore, and received “Best Faculty Award – 2016” from Kongu Engineering College, Erode.

He is an Associate Member of Institution of Engineers (India) –IE(I) and a Member of International Association for Engineers (IAENG).

Research Expertise

Condition Monitoring, Tribology, Robotics & Automation, Control Engineering, Manufacturing Systems, Optimization Techniques, Surface Coatings.

Funded Projects

  • UGC – MRP : Wear prediction of multipoint cutting tool using sensor fusion model based on adaptive neuro fuzzy inference system (Completed)

Seminar / Workshop / FDP Organized

  • Organized a Seminar on “Engineering in Bio medical Devices” funded by DBT (Rs.25,000.00) on January 8, 2016.
  • Organized a Seminar on “Evolutionary Humanoid Robotics” funded by DST-SERB (Rs.50,000.00), from October 30-31, 2015.
  • Organized a Faculty Development Programme on “Emerging Trends and Advances in Programmable Automation Controllers” funded by AICIE (Rs.5.50 Lakhs) from April 20- May 3, 2015.

Teaching

  • Mechatronics
  • Design of Machine Elements
  • Industrial Robotics
  • Automotive Electronics
  • Fluid Power systems
  • Rapid prototyping and Tooling
  • Virtual Instrumentation
  • Microfluidics and BioMEMS

Publications

Publication Type: Journal Article

Year of Publication Title

2019

T. Mohanraj, Tamilarasi, T., Rajasekar, R., Saminathan, K., and Ravichandran, K., “Experimental Investigation on the Influence of Carbon-based Nanoparticle Coating on Heat Transfer Characteristics of the Microprocessor”, Journal of Composite Materials , 2019.

2019

A. SHANMUGAM, KRISHNAMURTHY, K., and T. Mohanraj, “Experimental study of surface roughness and taper angle in abrasive water jet machining of 7075 Aluminium composite using Response Surface Methodology”, Surface Review and Letters (Accepted), p. 1950112, 2019.[Abstract]


Surface roughness and taper angle of an abrasive waterjet machined surface of 7075 Aluminum metal matrix composite were deliberately studied. Response surface methodology design of experiments and analysis of variance were used to design the experiments and to identify the effect of process parameters on surface roughness and taper angle. The jet traverse speed and jet pressure were the most significant process parameters which influence the surface roughness and taper angle, respectively. Increasing the pressure and jet traverse speed results in increasing the surface roughness and taper angle. At the same time, decreasing the standoff distance and jet traverse speed possibly enhances both the responses. The optimal process parameters of 1mm as standoff distance, 192MPa as water pressure and 30mmmin−1 as jet traverse speed were identified to obtain the minimum value of surface roughness and taper angle. Based on the optimal parameters, the confirmation test was conducted. The mathematical equation was obtained from the experimental data using regression analysis; it was observed that the error was less than 5% of the experimentally measured values.

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2019

S. Shankar, T. Mohanraj, and Pramanik, A., “Tool Condition Monitoring While Using Vegetable Based Cutting Fluids During Milling of Inconel 625”, Journal of Advanced Manufacturing Systems, 2019.

2019

T. Mohanraj, Shankar, S., Rajasekar, R., Deivasigamani, R., and Arunkumar, P. M., “Tool Condition Monitoring in the Milling Process with Vegetable based Cutting Fluids using Vibration Signatures”, Materials Testing, vol. 61, no. 3, pp. 282-288, 2019.[Abstract]


The major difficulty faced in a machining process is predicting the failure of cutting tools and analyzing the stipulated time for tool replacement. The former and latter can be achieved through a monitoring system that surveys the effective condition. This present research work is focused on analyzing tool condition by adopting a vibration signature during the machining of a hybrid aluminum alloy composite using various coolants. The experiments were conducted employing various tools under optimum process parameters utilizing vegetable based cutting oil as a coolant. During the machining process, a vibration signature from the workpiece was acquired using an NI 6221 M series DAQ card allowing for various time domain features to be extracted. The arithmetic mean and skewness significantly increased for dull tools. Based on the extracted features, a decision making algorithm for tool condition monitoring system has been proposed. The result shows that the features extracted increased consecutively with an increase in flank wear.

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2019

S. Shankar, T. Mohanraj, and Rajasekar, R., “Prediction of Cutting Tool Wear during Milling Process using Artificial Intelligence Techniques”, International Journal of Computer Integrated Manufacturing, vol. 32, pp. 174-182, 2019.[Abstract]


An efficient tool condition monitoring system was designed for keyway milling of 7075-T6 hybrid aluminium alloy composite with resultant machining force and sound acquired while the milling process. During the milling process, sound pressure and machining force were measured using a microphone and milling tool dynamometer with NI USB 6221 DAQ card and monitored using LabVIEW. The resultant cutting force for fresh and dull tool varies up to 1 kN and 1.8 kN respectively. The sound pressure for fresh, working and dull tool varies up to 1.9 Pa, 2 Pa and 2.5 Pa respectively. The tool condition was estimated from the Artificial Intelligence techniques based on the acquired signals. The acquired signals were given as an input signal to the expert system. The predictor output varies from 0 to 3 to indicate the progression of flank wear and it was utilised to evaluate the tool condition. When the output exceeds the value of 3, it indicates that the tool has to be replaced for the machining process. The Mean Squared Error (MSE) for a feedforward backpropagation neural network and ANFIS model were 2.06517e-9 mm and 0.487505e-3 mm respectively. The neural network had the regression coefficient of 0.99 which shows the accuracy of the model. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

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2017

T. Mohanraj and Shankar, S., “Experimental Investigation and Process Parameter Optimization in Milling of 7075 – T6 Hybrid Aluminium Metal Matrix Composite using esponse Surface methodology”, Journal of the Balkan Tribological Association, vol. 23, pp. 124–138, 2017.

2017

T. Mohanraj, Shankar, S., and Ponappa, K., “Influence of Vegetable Based Cutting Fluids on Cutting Force and Vibration Signature During Milling of Aluminium Metal Matrix Composites”, Jurnal Tribologi, vol. 12, pp. 1-17, 2017.

2016

T. Mohanraj, Shankar, S., and Thangarasu, S. K., “Multi-Response Milling Process Optimization using the Taguchi Method Coupled to Grey Relational Analysis”, Materials Testing, vol. 58, no. 5, pp. 462-470, 2016.[Abstract]


An efficient method based on Taguchi's design of experiment coupled with the grey relational analysis was studied, concentrating on the optimization of process parameters over surface roughness, cutting force and tool wear rate in milling of mild steel. This study consists of three stages: experimental work, single response optimization using Taguchi's S/N value and multi-response optimization using grey relational analysis. In the first stage, the experimental work was carried out using Taguchi's design of experiments. The effects of process parameters (spindle speed, feed rate and depth of cut) on surface roughness, cutting force and tool wear rate were investigated using analysis of variance. In the second stage, Taguchi's signal-to-noise ratio was used to optimize the responses. Finally, multi-response optimization was carried out using grey relational analysis. Additionally, the analysis of variance (ANOVA) was applied to determine the most significant factor for the optimal response for milling of mild steel. From the ANOVA table, the most significant factor is the spindle speed. This proposed method can be an effective approach to enhance the multi-response optimization for milling process.

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2015

T. Mohanraj, Saravanan, B., and Ameen, M. Al, “Optimization of Process Parameter for Surface Roughness in Milling Process using Response Surface Methodology”, IOSRD International Journal of Engineering, vol. 2, no. 2, pp. 11-16, 2015.

2015

T. Mohanraj, S., S., Thangarasu, S. K., and Pravien, D. S., “Prediction of Cutting Force in Turning Process: An Experimental and Fuzzy Approach”, Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1785-1793, 2015.[Abstract]


This paper presents a comparison of experimental results and a fuzzy rule based system model for calculating the cutting force in the turning operation. A full bridge dynamometer was used to measure the cutting forces over the mild steel work piece and Cemented Carbide Insert tool for different combinations of cutting velocity, feed rate and depth of cut. The rake angle, approach angle and nose radius of the cutting tool insert is kept constant throughout the experiment. This fuzzy model consists of 27 rules and Mamdani Max-min inference mechanism was used. The Taguchi designs of experiments were used to determine the number of experiments. Also, an attempt had been made to analyze the influence of the parameters using the regression analysis which yields a maximum error of 3.214% at the time of prediction which was smaller. The experiments are planned based on Taguchi's design and the measured cutting forces were compared with the predicted forces in order to validate the feasibility of the proposed design. The percentage contribution of each process parameter had been analyzed using Analysis of Variance (ANOVA). Experimental results were compared with the regression analysis and predicted fuzzy model. The difference between experimental and predicted results was obtained as around 98.84%.

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2014

T. Mohanraj, Arunkumar, S., Raghunath, M., and Anand, M., “Mobile Robot Path Planning using Ant Colony Optimization”, International Journal of Research in Engineering and Technology, vol. 3, no. 11, pp. 1-16, 2014.[Abstract]


Currently Mobile Robot has been widely used in examination and navigation particularly where static and unknown surroundings are involved. Path planning is a crucial problem in mobile robotics. Path planning of robot refers to the determination of a path, a robot takes in order to carry out the necessary task with a given set of key parameters. To find best possible path from starting point to target point, that reduces time and distance, in a given environment, avoiding collision with obstacles is a current potential research area. This paper presents SACO and ACO-MH algorithm to solve the problem of mobile robot path planning such that to reach the target station from source station without collision. The SACO and ACO-MH algorithm will give the collision free optimal path. The result obtained with ACO-MH was compared with SACO. The mobile robot environment is treated as a grid based environment in which each grid can be represented by an ordered pair of row number and column number. The mobile robot is considered as a point in the environment, to reduce the computational complexities. The ACO-MH results show better convergence speed and reduction in computational time than that of SACO through multiple MATLAB experiments.

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2009

S. C. M. Amie and T. Mohanraj, “Automation and Emerging Technology Development of 2d Seed Sowing Robo”, Journal of Agricultural Science (Toronto), vol. 1, no. 1, pp. 33-52, 2009.[Abstract]


In the current scenario most of the countries do not have sufficient skilled manpower specifically in agricultural sector and it affects the growth of developing countries. So it’s a time to automate the sector to overcome this problem. An innovative idea of our project is to automate the process of sowing crops such as sunflower, baby corn, groundnut and vegetables like beans, lady’s finger, pumpkin and pulses like black gram, green gram etc to reduce the human effort and increase the yield. The plantations of seeds are automatically done by using DC motor. The distance between the two seeds are controlled and varied by using Microcontroller. It is also possible to cultivate different kinds of seeds with different distance. When the Robo reaches the end of the field we can change the direction with the help of remote switches. The whole process is controlled by Microcontroller.

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Publication Type: Conference Proceedings

Year of Publication Title

2017

T. Mohanraj, Sathishranganathan, C., Rajasekar, R., Saravanan, N., and Maheshkumar, K. V., “Investigation on Mechanical Properties of Aluminium 6063 with Basalt Powder”, International Conference on Nanotechnology:Ideas, Innovations and initiatives - 2017. IIT Roorkee, 2017.

2017

S. .Shankar and T. Mohanraj, “Experimental Analysis and Process Parameter Optimization in Milling of Inconel 625 using Response Surface Methodology”, International Conference on Manufacturing Technology and Simulation 2017. IIT Madras, 2017.

2017

T. Mohanraj, .Shankar, S., ,, Dhamodharan,, and Aravindh, A., “Experimental Analysis and Process Parameter Optimization in Milling of AISI 304 Austenitic Stainless Steel Sing Response Surface Methodology”, International Conference on Advances in Metallurgy, Materials and Manufacturing (ICAMMM-2017 GCE Salem). 2017.

2016

T. Mohanraj, .Shankar, S., .Rajasekar, R., and .M.Arunkumar, P., “Optimization of Process Parameters using Taguchi's DOE for Cutting Force in Milling of 7075-T6 Composite Aluminium Alloy”, International conference on Sustainable Materials design and applications ICSMDA 2016. Kongu Engineering College, 2016.

2016

T. Mohanraj, .Saravanan, B., M. Asfar, M., and .G.Sastikumar, T., “Automatic Pineapple Peeler”, International Conference on Advanced Engineering and technology for sustainable development, ICAETSD 2016. 2016.

2016

T. Mohanraj, .Shankar, S., .Saravanan, B., and .G.Sastikumar, T., “Design, Development and Testing of a Strain Gauge Based Milling Dynamometer for Measurement of Cutting Force”, International Conference on Materials, Design and Manufacturing Process, ICMDM ‘16. Anna University, Chennai , 2016.

2016

T. Mohanraj, .Arunkumar, S., ameen, J. Mal, Kumar, J. A., and .Janarthanan, G., “Intelligent Motorisd Shopping Droit”, International Conference on Advances in Materials and Manufacturing - INTCOMM 2016. Hindusthan College of Engineering and Technology, Coimbatore, 2016.

2016

T. Mohanraj, Rajasekar, D. R., and .M.Arunkumar, P., “Influence of Bio-oils as Cutting Fluid in Tool Wear Prediction for Milling Operation”, International Conference on Advances in Materials and Manufacturing - INTCOMM 2016. Hindusthan College of Engineering and Technology, Coimbatore, 2016.

2016

T. Mohanraj and .Saravanan, B., “Throughput Time Reduction in OHT (Off Highway Trucks) Main Assembly line Through Fixture and Modularity”, International Conference on Applied Mathematical Models. PSG College of Technology, Coimbatore, 2016.

2015

T. Mohanraj and .Saravanan, B., “Automatic Pesticide Spraying Machine”, International Conference on Mathematical Computer Engineering ICMCE 2015. VIT University, Chennai, 2015.

2015

T. Mohanraj and Saravanan, B., “Design and Fabrication of Automatic Dhoop Making Machine”, International Conference on Modern, Intelligent and Green Manufacturing. Erode Sengundhar Engineering College, Erode, 2015.

2015

T. Mohanraj and S.Shankar, 4., “Tool Condition Monitoring in Milling using Sensor Fusion Technique”, Malaysian International Tribology Conference. 2015.

2014

T. Mohanraj, .Shankar, S., and .Eswararaj, S., “Optimization of Process Parameters for Surface Roughness in Milling based on Response Surface Methodology”, International Conference on Advances in Mechanical and Mechatronics Engineering. Sri Krishna College of Engineering and Technology Coimbatore, 2014.

2014

T. Mohanraj, .MuthuKrishnan, A., C. Kumar, N., and Prasannababu, R., “Thermo Mechanical Analysis of Single Point Cutting Tool using Fem Approach”, International Conference on Advances in Mechanical and Mechatronics Engineering. Sri Krishna College of Engineering and Technology Coimbatore, 2014.

2014

T. Mohanraj, .Anandakumar, P., .K.Boopathi, M., and .Elango, B., “Some Studies on Mobile Robot Path Planning – A Review”, International Conference on Advances in Mechanical and Mechatronics Engineering. Sri Krishna College of Engineering and Technology Coimbatore, 2014.