Eun-Ho Choi

Topology and parameter optimization of a foaming jig reinforcement structure by the response surface method

A dilemma in the foaming of inner polyurethane (PU) pieces for household refrigerators is that of keeping the production costs down without adversely affecting the dimension precision. One way to do this is to reduce the electric power consumption spent running the polyurethane foaming line in which a number of heavy foaming jigs are continuously circulating, by optimally designing the reinforcement structure of the jig. In this paper, topology optimization and parameter optimization utilizing the response surface method (RSM) are applied for the optimum design of the jig reinforcement structure, in order to minimize the total jig weight while securing the dimension precision of the foamed urethane case. Both the reliability of the approximated response surfaces and the validity of the proposed optimization procedure are verified through illustrative optimization experiments. In addition, it is confirmed that the proposed procedure provides us with an optimum reinforcement structure which remarkably reduces the total jig weight.

Initial Design Domain Reset Method for Genetic Algorithm with Parallel Processing

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation’s size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.

Optimum Shape Design of Gearbox Housing for 5MW Wind Turbines

The thickness optimization of the gearbox housing for 5MW wind turbine is carried out with the help of the efficient structure analysis model and the approximation model of objective function. Wind turbine gearbox is a complex structural system composed of a number of gear trains, shafts, bearing and gearbox housing, requiring a tremendous number of elements for the structural analysis and design. In this paper, an effective analysis and design model considering the tooth stiffness of helical gears is proposed. It enables to significantly reduce the total element number and the analysis time. Through the numerical optimization of housing thickness making use of the effective gearbox model and the approximate model of objective function, the total weight of the gearbox housing is minimized. It has been observed from the numerical experiment that the approximation model is reliable and the optimization result is acceptable and verified analysis.

A Sequential Approximate Optimization Technique Using the Previous Response Values

A general approximate optimization technique by sequential design domain(SDD) did not save response values for getting an approximate function in each step. It has a disadvantage at aspect of an expense. In this paper, previous response values are recycled for constructing an approximate function. For this reason, approximation function is more accurate. Accordingly, even if we did not determine move limit, a system is converged to the optimal design. Size and shape optimization using approximate optimization technique is carried out with SDD. Algorithm executing Pro/Engineer and ANSYS are automatically adopted in the approximate optimization program by SDD. Convergence criterion is defined such that optimal point must be located within SDD during the three steps. The PLBA(Pshenichny-Lim-Belegundu-Arora) algorithm is used to solve approximate optimization problems. This algorithm uses the second-order information in the direction finding problem and uses the active set strategy