Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

Farmani, Mohammad Reza; Jaamialahmadi, Abdolrahman; Babaie, Meisam

doi:10.1007/s12206-011-0924-8

Cited by 28 publications

(23 citation statements)

References 14 publications

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“…The percentage error of resulting link inertia values defined as the objective function was found within ± 5 percent. Thus, the two-stage optimization formulation including the dynamic balancing and the dynamics of mechanism has been brought in shape synthesis of links.The benefit associated with the proposed method is that the links of balanced mechanism are of the uniform thickness while the force and inertia counterweights added to the original mechanisms in traditional methods (Berkof, 1973;Farmani et al, 2011;Berkof and Lowen, 1969)are of large thickness and radius compared to the original link parameters. Also, the proposed method doesn't require any pre-defined shapes or design domain to start withas suggested in (Farmani et al, 2011;Verschuure et al, 2007).The resulting stresses for links of the balanced mechanism can be calculated at the weakest sections under external loads.…”

Section: Discussionmentioning

confidence: 99%

Methodology of Optimal Link Shape Synthesis of Planar Mechanisms

Chaudhary¹

2017

IJRASET

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The optimization problem formulation for link shape synthesis for the optimally balanced simple and multiloop planar mechanisms is presented in this paper. The closed parametric curve is used to represent the link shape and its geometric and inertial properties are calculated using well known Green's theorem. The proposed optimization problem includes the equality constraints to keep the resulting inertial properties same as the inertial properties of the optimally balanced mechanisms. I.INTRODUCTION In this paper, the link shapes are synthesized for optimally balanced mechanism for the given motion. The link shapes satisfying kinematic and dynamic requirements are very crucial for the design of a mechanism and its performance. The shape synthesis using parametric curves like Hermit, Bezier and B-spline curves leads to computer-aided design (CAD) and manufacturing of the mechanism links. Through CAD modeling of the links using these curves; the design, production and functional details can beeasily transmitted between engineering and manufacturing operations. The CAD modeling of the links is also useful in analyzing the static and dynamic response of the designed mechanism. The real-time behavior of the mechanism is evaluated through computer simulation and thus it eliminates the need of the experimental tests for the actual mechanism. Therefore, the cost and time are saved to a great extent and any possible error is realized before manufacturing of the mechanism links.

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Section: Discussionmentioning

confidence: 99%

Methodology of Optimal Link Shape Synthesis of Planar Mechanisms

Chaudhary¹

2017

IJRASET

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“…Erkaya andUzmay (2009, 2013) used GA to investigate the effect of joint clearances on the dynamic properties of the planar mechanism. Using the concepts of physical pendulum and inertia counterweights, the optimization problem formulated for balancing of a planar four-bar mechanism is solved using multiobjective particle swarm optimization, and non-dominated sorting genetic algorithm II (Farmani et al, 2011). The teaching-learning-based optimization (TLBO) algorithm was used for the optimization of mechanical design problems such as springs, bearings, pulleys and gear train by Rao et al (2011).…”

Section: ) Learner Phase :Learning Through Interactionmentioning

confidence: 99%

Optimization Techniques for Engineering Design

Chaudhary¹

2017

IJRASET

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This paper discusses the popular evolutionary optimization technique, Genetic Algorithm (GA)and Teaching-learningbased Optimization (TLBO) algorithm. It also covers the definitions of various parameters used by these algorithms. I.INTRODUCTION Most of the engineering design problems are competing multi-objective problems for which the optimal values of the design variables are searched that optimize several objectivesfor a given set of constraints. The different methods available to formulate a multi-objective problem as a single objective problem are weighted global criterion method, weighted sum method, lexicographic method, weighted min-max method, exponential weighted criterion, weighted product method, goal programming methods, bounded objective function method, and physical programming (Marled and Arora, 2004).The weighted sum approach is more widely used in which a normalized objective function is formulated by assigning proper weighting factors to all the objectives. By selecting different values of the weighting factorsto objectives, the results are obtained as a set of optimum solutions and each solution in this set is a trade-off between the different objectives (Marled and Arora, 2010). A constrained optimization problem is considered more complex than that of an unconstrained problem. It finds a feasible solution that optimizes one or more mathematical functions in a constrained search space. The constrained optimization problem is transformed into an unconstrained optimization problem by modifying the objective function on the basis of the constraint violations After formulating the optimization problem, it can be solved by using either traditional or evolutionary optimization algorithms. The traditional or classical optimization algorithms are based on deterministic approach, i.e., they use gradient information of objective function with respect to the design variables and move from one solution to other following the specific rules. Depending on the starting solution these algorithmsmay end up with a local optimum solution. Therefore, one has to explore all local solutions; one of them is the global optimum solution. To improve the chances of getting the global optimum solution, a large set of randomly generated initial solutions is required for these algorithms. The global optimum solution is then found as the best of all local optimum solution provided by different instances of the algorithm.The popular methods in this category are quadratic programming, steepest descent method, linear programming, nonlinear programming, dynamic programming and geometric programming, etc. For the complex optimization problem having a large number of design variables and multiple local minimum solutions, these methods converge on the optimum solution near to the initial solution provided and thus produce local optimum solution (Marler and Arora, 2004;Mariappan and Krishnamurty, 1996).These techniques are generally not suitable for the optimization problems with (1) large number of constraints (2) large...

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“…A numerical problem of planar four-bar mechanism [11,27] as shown in Fig. 4 is solved using the method proposed in this paper.…”

Section: Planar Four-bar Mechanismmentioning

confidence: 99%

“…Particularly, four-bar mechanism may also be balanced by converting coupler into physical pendulum as did in [11,27]. To investigate the effect of this conversion, the cases (1) and (2) are repeated after making link #2 as the physical pendulum and reported as cases (3) and (4), respectively.…”

Section: Planar Four-bar Mechanismmentioning

confidence: 99%

Optimal dynamic balancing and shape synthesis of links in planar mechanisms

Chaudhary

2015

Mechanism and Machine Theory

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Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

Cited by 28 publications

References 14 publications

Methodology of Optimal Link Shape Synthesis of Planar Mechanisms

Methodology of Optimal Link Shape Synthesis of Planar Mechanisms

Optimization Techniques for Engineering Design

Optimal dynamic balancing and shape synthesis of links in planar mechanisms

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