Informatics Publishing Limited

T. Pushparaj ¹, C. T. Umamaheswari ¹, S. Ramabalan ²

Affiliations
1 Department of Mechanical Engineering, Kings College of Engineering, Thanjavur, Tamil Nadu, IN
2 Department of Mechanical Engineering, EGS Pillai Engineering College, Nagapattinam, Tamil Nadu, IN

Abstract

Diesel engines are widely used for their low fuel consumption and better efficiency. Ever increasing diesel consumption, large outflow of foreign exchange and concern for environment have prompted developing countries like India. Recently, because of increases in crude oil prices, limited resources of fossil oil focus on nano additives in diesel engines. In this study, performance tests were carried out on diesel engine with neat diesel fuel; diesel with 10ppm cerium oxide nanoparticle blend and 30ppm and 50ppm blend with as additive.

The effects of the additive cerium oxide nanoparticles on the engine performance and emissions are studied, and comparisons of the performance of the fuel with and without the additive are also presented. The experimental results revealed a substantial enhancement in the performance and a reduction in harmful emissions.

Keywords

Cerium Oxide Nano Additive, Ci Engine, Emissions Performance.

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N. Indira ¹, P. Muralidaran ², S. Ramabalan ³, C. Balamurugan ⁴

Affiliations
1 J.J. College of Engineering & Technology, Tiruchirapalli, IN
2 Alagappa Chettiyar College of Engineering & Technology, Karaikudi, IN
3 E.G.S. Pillay Engineering College, Nagapattinam, IN
4 M.A.M College of Engineering, Tiruchirapalli

Abstract

In this project optimal trajectory and path planning of mobile robot, which is working in a fixed & moving obstacles area was programmed. The problem has a multi-criterion character in which three objective functions were used. The objectives are optimal trajectory planning in a moving obstacles area, minimum mechanical energy of the actuators and optimal path planning in a fixed obstacle area. Existing algorithms are not able to perform efficiently in the moving obstacles area, it`s not able to select the optimum path, if it`s able to do that means it requires more memory storage and mechanical power. Then it has minimum capability of handling multi tasking problems which is required for practical application. In order to overcome the above drawbacks all the objectives want to be optimized, for that purpose non domination solution is required. So NSGA-2 (non-dominated sorting genetic algorithm-2) is used here to solve that domination problem by calculating the Pareto set values, best non dominated optimal solution is find out by applying it to the real problem best solution is obtained.

Keywords

Non Domination Solution,a Multi-Criterion Character.

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T. Pushparaj ¹, U. Om Ezhilan ², V. Arumugham ³, S. Ramabalan ⁴

Affiliations
1 Department of Mechanical Engineering. Kings College of Engineering, Thanjavur, Tamil Nadu, IN
2 Department of Mechanical Engineering. Kings College of Engineering, Thanjavur, Tamil Nadu, IN
3 Department of Mechanical Engineering, Kings College of Engineering, Thanjavur, Tamil Nadu, IN
4 E.G.S Pillay Engineering College, Nagapattinam, Tamil Nadu, IN

Abstract

The idea of using vegetable oils as fuel for diesel engines is not new. With the advent of cheap petroleum, appropriate crude oil fractions were reined to serve as fuel and diesel fuels and diesel engines evolved together. In the 1930s and 1940s vegetable oils were used as diesel fuels from time to time, but usually only in emergency situations. Recently, because of increases in crude oil prices, limited resources of fossil oil and environmental concern there has been a renewed focus on vegetable oils and animal fats to make biodiesel fuels. Diesel engines have proven their utility in the transportation, agriculture, and power sectors in India. They are also potential sources of decentralized energy generation for rural electrification. Concerns on the long-term availability of petroleum diesel and the stringent environmental norms have mandated the search for a renewable alternative to diesel fuel to address these problems. In this study, performance tests were carried out on diesel engine with neat diesel fuel and biodiesel mixture. Biodiesel was made by transesterification process. Karanja oil was selected for biodiesel production. Fuel blends were tested in a single cylinder, direct injection, water cooled diesel engine (agricultural). The effects of B30,B30 with Bio Fuel additive and commercial diesel on the engine power, engine torque, BSFCs and exhaust gasses temperature were ascertained by performance tests. The influence of blends on CO, NOx,and CO2 emission were investigated. The experimental results showed that the use of Biodiesel improve the performance parameters and decrease the CO emission as compared to diesel fuel. Bio Fuel Enhancer(BFE) not only improve the performance and also increase fuel saving and reduce the NOx.

Keywords

Transesterification, Karanja Oil, Bio Fuel Enhancer (BFE), Emissions.

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D. Elayaraja ¹, S. Ramabalan ²

Affiliations
1 Anna University of Technology, Trichy, IN
2 EGS Pillay Engineering College, Nagapattinam, IN

Abstract

Industrial robots are playing an increasingly important role in industry to meet out the demands of automated system and are expected to sense environmental information and also process that information and perform appropriate action for a wide variety of tasks. A major challenge for these robots is that the traditional control techniques generally require an accurate model of the system and its environment. Inaccurate modeling will hamper the mathematical optimization process and have a direct negative effect on their performance. For these reasons, computation intelligence technique is now regularly being employed, particularly evolutionary computation, fuzzy logic and neural computation etc.
Fuzzy controllers have been proven to be good tool for real time processes but the designer has to manually derive the “if-then rules by trail and error. On the other hand, Neural networks perform approximation of the system but cannot interpret the solution. Neurofuzzy concept combines the two approaches in which Neural networks brings the learning capabilities and Knowledge representation from fuzzy logic.
This paper considers the application of Neurofuzzy adaptive techniques in the design and development for Industrial robot PUMA 560. In real world situation, the environment around the robot is ever changing one. So this research work aims to develop a best controller design for online control of PUMA 560 robot.
For validating results various experiments are going to be conducted. The outcome from this research work will strengthen the field of robot controller design using Adaptive Neuro Fuzzy interface system.

Keywords

Puma 560 Robot, Robot Control, ANFIS Controller.

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T. Imran Hussian ¹, M. Kader Khan ¹, N. Indira ², S. Ramabalan ³, C. Balamurugan ⁴

Affiliations
1 Department of Mechanical Engineering, M.A.M. College of Engineering, Tiruchirappalli, IN
2 J.J. College of Engineering & Technology, Tiruchirappalli, IN
3 E.G.S. Pillay Engineering College, Nagapattinam, IN
4 M.A.M College of Engineering, Tiruchirappalli, IN

Abstract

Robot manipulators are programmable mechanical system designed to execute a great variety of tasks in repetitive way.In industrial environment, while productivity increases, cause reduction associated with robotic operation and maintenance can be obtained as a result of decreasing the values of dynamic quantities such as torque and jerk, with respect to a specific task. Furthermore,this procedure allows the execution of various tasks that require maximum system performance. By including obstacles avoidance ability to the robot skills, It is possible to improve the robot versatility, i.e., the robot can be used in a variety of operation conditions. In the present contribution, a study concerning the dynamic characteristics of serial robot manipulators is presented. An optimization strategy that considers the obstacle avoidance ability together with the dynamic performance associated with movement of robot is proposed. It results an optimal path planning strategy for a serial manipulator over time varying constrains in the robot workspace. This is achieved by using multi criteria optimization methods and optimal control techniques (NSGA-II). Numerical simulation results illustrate the interest of the proposed methodology and the present techniques can be useful for the design of robot controllers. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions using the proposed algorithms.

Keywords

Dynamics of Articulated System, Manipulator Optimal Control, Serial Robot Manipulator.

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R. Saravanan ¹, S. Ramabalan ², C. Balamurugan ³, A. Subash ²

Affiliations
1 Dept. of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, IN
2 J. J. College of Engg. and Technology, Trichy, IN
3 Dept. of Production Engg., J. J. College of Engg. and Technology, Trichy, IN

Abstract

This paper presents a new general method fo r computing the optimal motions of industrial robot manipulators in the presence of fixed and moving obstacles. The mathematical model considers the nonlinear manipulator dynamics, actuator constraints, joint limits and obstacle avoidance. The problem considered has 5 objective functions, 88 variables and 21 constraints. Two evolutionary algorithms such as Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) techniques have been used for the optimization. Given the initial and final configurations, the trajectory is defined using B-spline function and is obtained through off-line computation for on-line operation. The obstacles are considered as objects sharing the same workspace performed by the robot. The obstacle avoidance is expressed in terms of the distances between potentially colliding parts and the motion is represented using translation and rotational matrices. Numerical application involving an industrial manipulator (Adeptone XL robot) is presented. The results obtained from NSGA-II and DE are compared and analyzed. A comprehensive user-friendly general-purpose software package has been developed for the DE algorithm using VC++ to obtain the optimal solutions.

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R. Saravanan ¹, S. Ramabalan ², P. Sasikumar ³, P. Sriram ²

Affiliations
1 Dept. of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, Tamil Nadu, IN
2 J. J. College of Engg. and Technology, Trichy, Tamil Nadu, IN
3 Dept, of Production Engg., J. J. College of Engg. and Technology, Trichy, Tamil Nadu, IN

Abstract

This paper concerns with the use of intelligent techniques such as Genetic Algorithm (GA) and Differential Evolution (DE) to find optimum geometrical dimensions of a robot gripper. The problem is finding a combined objective function, which has five objectives, seven constraints and five variables. The objective functions are difference between maximum and minimum griping forces, force transmission ratio between gripper actuator and gripper ends, shift transmission ratio between gripper actuator and gripper ends, length of all elements of gripper and efficiency of gripper mechanism. The problem is dealt with three cases. A very original, new optimization model is derived and used. Also, a comprehensive user-friendly general-purpose software package has been developed to obtain the optimal parameters using the proposed DE algorithm.

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R. Saravanan ¹, S. Ramabalan ², M. Thilak ²

Affiliations
1 Dept. of Mechanical Engg., Kumaraguru College of Technology, Coimbatore, IN
2 J.J. College of Engg. and Technology, Trichy, IN

Abstract

The minimum-time path for a robot arm has been a long standing and unsolved problem of considerable interest. This paper presents two intelligent optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) for obtaining minimum time paths for robot manipulators. To illustrate the proposed methods, time optimal trajectories for a two-link IBM planar robot Is considered in this work. The results obtained using the proposed GA and PSO are compared with neural network results equations. It Is proved from this paper that the proposed intelligent optimization algorithms can be used successfully to find optimal travel time by approximating the robot inverse dynamics. Also both proposed GA and PSO methods give better results than the neural network.

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