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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 04/2012; 53(5):711-732. · 1.10 Impact Factor
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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 04/2012; 48(1):307-324. · 1.10 Impact Factor
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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 04/2012; 7(2):190-198.
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Computer-Aided Design. 01/2011; 43:207-218.
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IJISTA. 01/2010; 9:121-149.
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ABSTRACT: The optimum robot structure design problem based on task specifications is an important one, since it has greater influence
on manipulator workspace design, vibrations of the manipulator during operation, manipulator efficiency in the work environment
and power consumption. In this paper, an optimization robot structure problem is formulated with the objective of determining
the optimal geometric dimensions of the robot manipulators considering the task specifications (pick and place operation).
The aim is to minimize torque required for motion and maximize manipulability measure of the robot subject to dynamic, kinematic,
deflection and structural constraints with link physical characteristics (length and cross-sectional area parameters) as design
variables. In this work, five different cross-sections (hollow circle, hollow square, hollow rectangle, C-channel and I-channel)
have been experimented for the link. Three evolutionary optimization algorithms namely multi-objective genetic algorithm (MOGA),
elitist nondominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) are used for the
optimum structural design of 2-link and 3-link planar robots. Two methods (normalized weighting objective functions and average
fitness factor) are used to select the best optimal solution. Two multiobjective performance measures namely solution spread
measure and ratio of non-dominated individuals are used to evaluate the Pareto optimal fronts. Two more multiobjective performance
measures namely optimiser overhead and algorithm effort, are used to find computational effort of optimization algorithm.
The results obtained from various techniques are compared and analyzed.
International Journal of Advanced Manufacturing Technology 02/2009; 41(3):386-406. · 1.10 Impact Factor
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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. · 1.10 Impact Factor
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Appl. Soft Comput. 01/2009; 9:159-172.
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Eng. Appl. of AI. 01/2009; 22:329-342.
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World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, 9-11 December 2009, Coimbatore, India; 01/2009
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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. · 1.10 Impact Factor
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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. · 1.10 Impact Factor
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Journal of Intelligent and Robotic Systems. 01/2008; 52:45-77.
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Robotica. 01/2008; 26:753-765.
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ABSTRACT: Force balancing is a very important issue in mechanism design and has only recently been introduced to the designing step of robotic mechanisms. In creating the best robot design, the statical balancing plays a vital role because it reduces the required motor power. To get a simple and more-effective control system, elimination or significant reduction of the gravity load at a powered joint is an important one. With utilization of these objectives an optimization problem is formulated. The average force on the gripper in the working area is taken as an objective function. The design variables are lengths of the links, angles between them and stiffness of springs. This paper describes the use of conventional and evolutionary optimization techniques such as Newton's method (NM), conjugate gradient method (CGM), Genetic Algorithm (GA), Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and differential evolution (DE) to solve the above problem. An industrial robot with 6-degree-of-freedom (6-DOF) (APR 20) is considered as a numerical example. The robot has a spring balancing system that has to be optimized. The existing optimization model is improved by adding two new variables. Also, a comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal parameters using the proposed DE algorithm. The methods used in this article can be applied to find out solutions for a wide range of similar problems without further simplifications.
Engineering Applications of Artificial Intelligence.
