S. Panda

Revolute manipulator workspace optimization: A comparative study

Robotic manipulators with three revolute families of positional configurations are very common in the industrial robots. The capability of a robot largely depends on the workspace of the manipulator apart from other parameters. In this work, an evolutionary optimization algorithm based on foraging behavior of Escherichia coli bacteria present in human intestine is utilized to optimize the workspace volume of a 3R manipulator. The proposed optimization method is subjected to some modifications for faster convergence than the original algorithm. Further, the method is also very useful in optimization problems in a highly constrained environment such as the robot workspace optimization. The test results are compared with standard results available using other optimization algorithms such as Differential Evolution, Genetic Algorithm and Particle Swarm Optimization. In addition, this work extends the application of the proposed algorithm to two different industrial robots. An important implication of this paper is that the present algorithm is found to be superior to other methods in terms of computational efficiency.

Revolute manipulator workspace optimization using a modified bacteria foraging algorithm: A comparative study

Robotic manipulators with three-revolute (3R) motions to attain desired positional configurations are very common in industrial robots. The capability of these robots depends largely on the workspace of the manipulator in addition to other parameters. In this study, an evolutionary optimization algorithm based on the foraging behaviour of the Escherichia coli bacteria present in the human intestine is utilized to optimize the workspace volume of a 3R manipulator. The new optimization method is modified from the original algorithm for faster convergence. This method is also useful for optimization problems in a highly constrained environment, such as robot workspace optimization. The new approach for workspace optimization of 3R manipulators is tested using three cases. The test results are compared with standard results available using other optimization algorithms, i.e. the differential evolution algorithm, the genetic algorithm and the particle swarm optimization algorithm. The present method is found to be superior to the other methods in terms of computational efficiency.

An Artificial Neural Network Model for a Diesel Engine Fuelled with Mahua Biodiesel

In this paper, an Artificial Neural Network (ANN) model is used to predict the different parameters of a diesel engine fuelled with the mixture of diesel and mahua biodiesel in different proportion. The data has been obtained from an experiment carried out in a twin cylinder diesel engine in different loading condition and different blending ratios of diesel and biodiesel. Two input data, i.e., engine load and blending ratio and five output data, i.e., Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), Smoke level, Carbon monoxide (CO), and Nitrogen Oxides (NOx) emissions have been considered for ANN modeling. The network used is back propagation, feed forward with multilayer perceptron having ten numbers of neurons in hidden layer with trainlm training algorithm being proposed. It has been observed that the prediction ability of the model is high as there is minimum difference between the predicted and the experimentally measured values.

An appropriate tool for optimizing the workspace of 3R robot manipulator

Robotic manipulators with three-revolute (3R) positional configurations are very common in the industrial robots (IRs). The capability of a robot largely depends on the workspace (WS) of the manipulator apart from other parameters. With the constraints in mind the optimization of the workspace is of prime importance in designing the manipulator. The present work aims at obtaining an optimal design of manipulators with three-revolute joints. The optimization problem is formulated considering the workspace volume as the objective function, while constraints are imposed to control the total area. Subsequently the problem is solved using Sequential quadratic programming (SQP, fminmax, goal attainment, constrained non linear minimization) and genetic algorithms (GAs) and a comparision is made. The four different optimization techniques were used to solve numerical example imposing same condition to demonstrate the efficiency of the optimization processes. Numerical example is presented to validate the proposed methodology.

Multi objective optimization of EDM process parameters using fuzzy TOPSIS method

With the increasing demands of high surface finish, development of newer hardest material and machining of complex shape geometries, conventional machining process are now being replaced by non-traditional machining processes. Electrical discharge machining (EDM) is one of the non-traditional machining processes which are based on thermoelectric energy between the work piece and an electrode. A pulse discharge between the work piece and the electrode occurs in a small gap removes the unwanted material from the work piece material through melting and vaporizing. The electrode and the work piece must have electrical conductivity to generate the spark. Surface roughnesses (SR), higher material removal rate (MRR) and lower tool wear rate (TWR) are of crucial importance in the field of machining processes. This paper summarizes the fuzzy TOPSIS technique, in order to optimize the cutting parameters in EDM for stainless steel of S304 grade. The objective of optimization is to attain the minimum tool wear rate, higher material removal rate and the best surface quality simultaneously. In this present study L9 orthogonal array has been used and the input parameters selected for optimization are flow rate of dielectric, peak current, pulse on time, pulse off time. For each experiment MRR, TWR and SR are measured. By using multi objective optimization technique, the optimal value for MRR, TWR and SR is obtained by using fuzzy TOPSIS techniques.

An approach to weight optimization of a spur gear

Lightweight is one of the most important criterion in the optimum design of gear set for motorsport and aerospace application. A tradeoff between optimum weight and failure modes of gear is a subject of interest for researchers and the industry. In the present work weight of a single-stage spur gear set is optimized. This nonlinear constrained optimization formulation has been solved by using differential evolution algorithm. A total of six design variables corresponding to gear geometry and material property are considered. The results obtained are compared with those of published heuristics like genetic algorithm, simulated annealing, and particle swarm optimization algorithm, respectively. The optimization is performed in such a way that the design variables satisfy all constraints at optimum solution. Apart from this, several constraints related to scoring are included in the optimization. The constraint violation study is performed to prioritize the constraints. The sensitivity analysis is carried out to see the effect of manufacturing tolerances of design variables on weight of the gear set. The optimality of the solution has been ensured through the convergence study. The optimization reveals that the reported results are also encouraging in terms of objective function values and CPU time. In addition, the optimum design variables obtained through the weight optimization of spur gear set are used for preparation of a CAD model. Then the stress analysis using finite element analysis is performed on the gear set to identify the critical stress region in the optimized gear set.

Optimum Design of Rolling Element Bearing

The primary objective of this research is to optimize the dynamic load capacity of a deep groove ball bearing. The dynamic load capacity is formulated as an objective function along with the prescribed geometric, kinematics and strength constraints. The non-linear constrained optimization problem is solved using particles swarm optimization (PSO). The algorithm incorporates the generalized method to handle mixed integer design variables and ranked based method of constraint handling. Encouraging results in terms of objective function value and CPU time are reported in this study. The optimum design result shows that the system life of an optimally designed roller element bearing is enhanced in comparisons with that of the current design without constraint violations. It is believed that the proposed algorithm can be applied to other roller element design applications.

A multi-objective workspace optimization of 3r manipulator using modified PSO

The manipulator capability of a robot largely depends on the workspace (WS) of the manipulator apart from other parameters. With the constraints in mind, the optimization of the workspace is of prime importance in designing the manipulator. The workspace of manipulator is formulated as a constrained optimization problem with workspace volume as objective function and workspace volume and maximum manipulator size as a multi-objective function. It is observed that the previous literature is confined to use of conventional soft computing algorithms only, while a new search modified algorithm is conceptualized and proposed here to improve the computational efficiency. The proposed algorithm gives a good set of geometric parameters of manipulator within the applied constrained limits for both mono and multi-objective optimization. The efficiency of the proposed approach to optimize the workspace of 3R manipulators is exhibited through two cases.

Optimization for Workspace Volume of 3R Robot Manipulator Using Modified Differential Evolution

Robotic manipulators with three-revolute (3R) family of positional configurations are very common in the industrial robots (IRs). The manipulator capability of a robot largely depends on the workspace (WS) of the manipulator apart from other parameters. With the constraints in mind, the optimization of the workspace is of prime importance in designing the manipulator. The workspace of manipulator is formulated as a constrained optimization problem with workspace volume as objective function. It is observed that the previous literature is confined to use of conventional soft computing algorithms only, while a new search modified algorithm is conceptualized and proposed here to improve the computational time. The proposed algorithm gives a good set of geometric parameters of manipulator within the applied constrained limits. The availability of such an algorithm for optimizing the workspace is important, especially for highly constrained environments. The efficiency of the proposed approach to optimize the workspace of 3R manipulators is exhibited through two cases.

Development of a tribological failure knowledge model

Many of the component failures occurring in service can be delayed by better incorporation of tribological principles into engineering design and maintenance. However, the concept of tribology has not yet penetrated successfully into the industry in general and there is an urgent need for the practical tribology design criteria and transference of tribological knowledge to the engineering designer and maintenance engineer. Knowledge based system offers great potential for effecting tribological knowledge transfer and promoting improved design practice and maintenance strategy. In this present work, the development and implementation of a tribological failure knowledge model (KM) for steam power plant equipment is reported. This failure KM assists the maintenance engineer and design engineer for evolution of an effective maintenance strategy and better, reliable, safe and productive design. The KM has been implemented using Protege 3.0 Beta, JDK 1.4 and Java Swing Package. The model is applied to steam turbine failure in a power plant application.