Shape optimization of mobile phone folder module for structural strength

Lee, Gabseong; Park, Jeongseok; Choi, Donghoon

doi:10.1007/s12206-011-1212-3

Cited by 8 publications

(2 citation statements)

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“…As shown in Figure 9, the maximum von Mises stress evaluated for the structure using MGA with an interpolated continuous objective function is 49.930 MPa, which is very close to the upper limit of 50 MPa. As a result, it was revealed that the optimal position set of the ribs is (5,7,12,14), with thicknesses of 7.062 mm, 4.859 mm, 3.496 mm, and 7.778 mm, respectively. Considering the entire model from the results in Table 1, there was a 42.05% decrease in the rib weight from the weight of 0.195 Mg under the initial conditions to 0.113 Mg.…”

Section: Optimization Resultsmentioning

confidence: 99%

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Position and Thickness Optimization of Ribs for Ventilation Covering Using the Micro Genetic Algorithm with an Interpolated Smooth Objective Function

Kwon

Kim

et al. 2015

Mathematical Problems in Engineering

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Structures with supporting ribs are adopted in many fields of engineering. These ribs are attached to the main plate or shell to increase stiffness and reduce the stresses of a structure. Currently, much research in structural optimization has been devoted to size or thickness optimizations. In this study, the discrete positions of the ribs of a structure are optimized in addition to their thicknesses. The objective function, which is the total weight of a structure, is a continuous function with respect to the thickness of the ribs. However, it is a stepwise function of a dimensionless variable, which represents the set of positions of the ribs. Because of this stepwise objective function, the gradient method of optimization is not applicable. Therefore, we applied the micro genetic algorithm (MGA), which does not need derivatives of the objective function. To accelerate the rate of convergence, the stepwise objective function is interpolated to a smooth artificial objective function that does not alter the optimal solution.

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Section: Optimization Resultsmentioning

confidence: 99%

“…It changes design variables for an objective function, executes finite element analyses, conducts a test of the objective function, checks the constraint conditions, and repeats this process as necessary to find an optimal solution. The use of this package was previously demonstrated by Lee et al [7].…”

Section: Automatic Optimization Processor Using Pianomentioning

confidence: 97%