Are you C. Balamurugan?

Claim your profile

Publications (14)9.78 Total impact

  • K. Sivakumar, C. Balamurugan, S. Ramabalan
    [Show abstract] [Hide abstract]
    ABSTRACT: Concurrent tolerancing which simultaneously optimises process tolerance based on constraints of both dimensional and geometrical tolerances (DGTs), and process accuracy with multi-objective functions is tedious to solve by a conventional optimisation technique like a linear programming approach. Concurrent tolerancing becomes an optimisation problem to determine optimum allotment of the process tolerances under the design function constraints. Optimum solution for this advanced tolerance design problem is difficult to obtain using traditional optimisation techniques. The proposed algorithms (elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE)) significantly outperform the previous algorithms for obtaining the optimum solution. The average fitness factor method and the normalised weighting objective function method are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of the NSGA-II and MODE algorithms. Comparison of the results establishes that the proposed algorithms are superior to the algorithms in the literature.
    International Journal of Production Research - INT J PROD RES. 01/2011;
  • Source
    K. Sivakumar, C. Balamurugan, S. Ramabalan
    [Show abstract] [Hide abstract]
    ABSTRACT: Tolerance specification is an important part of mechanical design. Design tolerances strongly influence the functional performance and manufacturing cost of a mechanical product. Tighter tolerances normally produce superior components, better performing mechanical systems and good assemblability with assured exchangeability at the assembly line. However, unnecessarily tight tolerances lead to excessive manufacturing costs for a given application. The balancing of performance and manufacturing cost through identification of optimal design tolerances is a major concern in modern design. Traditionally, design tolerances are specified based on the designer’s experience. Computer-aided (or software-based) tolerance synthesis and alternative manufacturing process selection programs allow a designer to verify the relations between all design tolerances to produce a consistent and feasible design. In this paper, a general new methodology using intelligent algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection is presented. The problem has a multi-criterion character in which 3 objective functions, 3 constraints and 5 variables are considered. The average fitness factor method and normalized weighted objective functions method are separately used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.
    Computer-Aided Design. 01/2011; 43:207-218.
  • R. Saravanan, S. Ramabalan, C. Balamurugan, A. Subash
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed. KeywordsMulti-objective optimal trajectory planning–oscillating obstacles–elitist non-dominated sorting genetic algorithm (NSGA-II)–multi-objective differential evolution (MODE)–multi-objective performance metrics
    International Journal of Automation and Computing 05/2010; 7(2):190-198.
  • R. Saravanan, S. Ramabalan, P. Sriram, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a new novel trajectory planning method by using two evolutionary algorithms namely Elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for an autonomous robot manipulator (STANFORD robot) whose workspace includes several obstacles. The aim of the problem is to minimise a multicriterion cost function with actuator constraints, joint limits and obstacle avoidance constraints by considering dynamic equations of motion. Trajectories are defined by non uniform rational B-spline (NURBS) functions. This is a nonlinear constrained optimisation problem with 6 objective functions, 32 constraints and 288 variables. The multicriterion cost function is a weighted balance of transfer time, mechanical energy of the actuators, singularity avoidance, penalty function to guarantee the collision free motion, joint jerks and joint accelerations. All types of obstacles (fixed, moving and oscillating obstacles) are present in the workspace of the robot. The results obtained from NSGA-II and DE are compared and analysed.
    IJISTA. 01/2010; 9:121-149.
  • K. Sivakumar, C. Balamurugan, S. Ramabalan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new method based on evolutionary algorithms—Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE)—for the sensitivity-based conceptual design and the tolerance allocation for mechanical assemblies. The approach I of this paper moves the nominal values of non-critical dimensions to a less sensitive portion, and the approach II deals with the allocation of cost-based optimal tolerances. An improved optimization model that considers three objective functions (minimization of the deviation of critical dimensions, manufacturing cost, and the quality loss), eight constraints, and six variables is proposed. To show the effectiveness of the proposed methods, the stepped bar assembly is considered as a numerical example. The results obtained from NSGA-II and DE are compared and analyzed. The results show that the proposed methods are much effective, cost, and time saving than the ones considered in literature. KeywordsTolerance allocation-Sensitivity analysis and optimization-NSGA-II-DE
    International Journal of Advanced Manufacturing Technology 01/2010; 48(1):307-324. · 1.78 Impact Factor
  • K. Sivakumar, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper discusses a general methodology based on evolutionary algorithms (Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE)) for allocation of optimal tolerance for an over-running clutch assembly. New hybrid models are developed for obtaining the manufacturing costs of hub, rollers and cage. The aim of this paper is to find optimal tolerances values that give minimum combined objective function value, minimum Manufacturing Cost (MC), minimum Quality Loss (QL) function and maximum Number of Standard Deviations (Z<SUB align=right>NSD) by satisfying stack-up conditions using non-traditional optimisation techniques. The results obtained from various techniques are compared and analysed.
    IJCAT. 01/2010; 37:48-58.
  • S. Ramabalan, R. Saravanan, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a real-world test problem is presented and made available for the use of evolutionary multi-objective community. The generation of manipulator trajectories by considering multiple objectives and obstacle avoidance is a non-trivial optimisation problem. In this paper two multi-objective evolutionary algorithms viz., elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) algorithm are proposed to address this problem. Multiple criteria are optimised to two simultaneous objectives. Simulations results are presented for industrial robots with two degrees of freedom (Cartesian robot (PP) with two prismatic joints) and six degrees of freedom (PUMA 560 robot), by considering two objectives optimisation. Two methods (normalized weighting objective functions and average fuzzy membership function) are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.
    International Journal of Advanced Manufacturing Technology 02/2009; 41(5):580-594. · 1.78 Impact Factor
  • K. Sivakumar, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: Tolerance analysis of mechanical assemblies promotes concurrent engineering by bringing engineering requirement, manufacturing cost and quality together in a common model. To improve the tolerance design, the designer could first try to decrease the sensitive components by moving the nominal values to a less sensitivity portion. Second, the cost-based optimal machining tolerances are allocated. Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are used for minimising the critical dimensions deviation and manufacturing cost with optimal machining tolerances. A numerical example (Stacked blocks assembly) shows the superior nature of DE and NSGA-II algorithms.
    IJMTM. 01/2009; 18:15-33.
  • R. Saravanan, S. Ramabalan, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: Optimal trajectory planning for robot manipulators is always a hot spot in research fields of robotics. This paper presents two new novel general methods for computing optimal motions of an industrial robot manipulator (STANFORD robot) in presence of obstacles. The problem has a multi-criterion character in which three objective functions, a maximum of 72 variables and 103 constraints are considered. The objective functions for optimal trajectory planning are minimum traveling time, minimum mechanical energy of the actuators and minimum penalty for obstacle avoidance. By far, there has been no planning algorithm designed to treat the objective functions simultaneously. When existing optimization algorithms of trajectory planning tackle the complex instances (obstacles environment), they have some notable drawbacks viz.: (1) they may fail to find the optimal path (or spend much time and memory storage to find one) and (2) they have limited capabilities when handling constraints. In order to overcome the above drawbacks, two evolutionary algorithms (Elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) algorithm) are used for the optimization. Two methods (normalized weighting objective functions method and average fitness factor method) are combinedly used to select best optimal solution from Pareto optimal front. Two multi-objective performance measures (solution spread measure and ratio of non-dominated individuals) are used to evaluate strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimizer overhead and algorithm effort are used to find computational effort of NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analyzed.
    Eng. Appl. of AI. 01/2009; 22:329-342.
  • [Show abstract] [Hide abstract]
    ABSTRACT: A general new methodology using evolutionary algorithm viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for obtaining optimal tolerance allocation and alternative process selection for mechanical assembly is presented. The problem has a multi-criterion character in which 3 objective functions, 6 constraints and 11 variables are considered. The average fitness membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MOPSO are best for this problem.
    World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, 9-11 December 2009, Coimbatore, India; 01/2009
  • R. Saravanan, S. Ramabalan, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new general methodology based on the evolutionary algorithms—elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE)—for optimal trajectory planning of an industrial robot manipulator (PUMA560) by considering payload constraints. The aim is to minimize a multicriterion cost function with actuator constraints, joint limits, and payload constraints by considering dynamic equations of motion. Trajectories are defined by B-spline functions. This is a nonlinear constrained optimisation problem with five objective functions, 32 constraints, and 252 variables. The multicriterion cost function is a weighted balance of transfer time, total energy involved in the motion, singularity avoidance, joint jerks, and joint accelerations. A numerical example is presented for showing the efficiency of the proposed procedure. Also, the results obtained from NSGA-II and DE techniques are compared and analysed. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain optimal solutions using the proposed DE algorithm.
    International Journal of Advanced Manufacturing Technology 09/2008; 38(11):1213-1226. · 1.78 Impact Factor
  • R. Saravanan, S. Ramabalan, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents optimization procedures based on evolutionary algorithms such as the elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for solving the trajectory planning problem of intelligent robot manipulators with the prevalence of fixed, moving, and oscillating obstacles. The aim is the minimization of a combined objective function, with the constraints being actuator constraints, joint limits, and the obstacle avoidance constraint by considering dynamic equations of motion. Trajectories are defined by B-spline functions. This is a non-linear constrained optimization problem with six objective functions, 31 constraints, and 42 variables. The combined objective function is a weighted balance of transfer time, the mean average of actuator efforts and power, penalty for collision-free motion, singularity avoidance, joint jerks, and joint accelerations. The obstacles are present in the workspace of the robot. The distance between potentially colliding parts is expressed as obstacle avoidance. Further, the motion is represented using translational and rotational matrices. The proposed optimization techniques are explained by applying them to an industrial robot (PUMA 560 robot). Also, the results obtained from NSGA-II and DE are compared and analyzed. This is the first research work which considers all the decision criteria for the trajectory planning of industrial robots with obstacle avoidance. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions using the proposed DE algorithm.
    International Journal of Advanced Manufacturing Technology 03/2008; 36(11):1234-1251. · 1.78 Impact Factor
  • R. Saravanan, S. Ramabalan, C. Balamurugan
    [Show abstract] [Hide abstract]
    ABSTRACT: A general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.
    Robotica 01/2008; 26:753-765. · 0.88 Impact Factor
  • K. Sivakumar, C. Balamurugan, S. Ramabalan
    [Show abstract] [Hide abstract]
    ABSTRACT: Concurrent design of tolerances by considering both the manufacturing cost and quality loss of each component by alternate processes of the assemblies may ensure the manufacturability, reduce the manufacturing costs, decrease the number of fraction nonconforming (or defective rate), and shorten the production lead time. Most of the current tolerance design research does not consider the quality loss. In this paper, a novel multi-objective optimization method is proposed to enhance the operations of the non-traditional algorithms (Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) and systematically distribute the tolerances among various the components of mechanical assemblies. The problem has a multi-criterion character in which three objective functions, one constraint, and three variables are considered. The average fitness factor method and normalized weighted objective function method are used to select the best optimal solution from Pareto-optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto-optimal fronts. Two more multi-objective performance measures namely optimizer overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto-optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MOPSO algorithms are best for this problem. KeywordsTolerance design–Alternative manufacturing process selection–Evolutionary algorithms–Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II)–Multi-objective Particle Swarm Optimization (MOPSO)
    International Journal of Advanced Manufacturing Technology 53(5):711-732. · 1.78 Impact Factor