Browsing by Author "Hsiao, K. M."

Now showing 1 - 3 of 3

A new differential evolution algorithm with a combined mutation strategy for optimum synthesis of path-generating four-bar mechanisms
(2017-07-01)
Lin, W. Y.
;
Hsiao, K. M.
;
機械工程學系
;
Department of Mechanical Engineering
A one-phase synthesis method using heuristic optimization algorithms can solve the dimensional synthesis problems of path-generating four-bar mechanisms. However, due to the difficulty of the problem itself, there is still room for improvement in solution accuracy and reliability. Therefore, in this study, a new differential evolution (DE) algorithm with a combined mutation strategy, termed the combined-mutation differential evolution (CMDE) algorithm, is proposed to improve the solution quality. In the combined mutation strategy, the DE/best/1 operator and the DE/current-to-best/1 operator are respectively executed on some superior parents and some mediocre parents, and the DE/rand/1 operator is executed on the other inferior parents. Furthermore, the individuals participating in the three mutation operators are randomly selected from the entire set of parents. The proposed CMDE algorithm with the three different search modes possesses better population diversity as well as search ability than the DE algorithm. The effectiveness of the proposed CMDE algorithm is demonstrated using five representative problems. Findings show a marked improvement in solution accuracy and reliability. The most accurate results are obtained with an approximate combination ratio for the three mutation operators.
A new indexing motion program for optimum designs of Geneva mechanisms with curved slots
(2017-11-01)
Lin, W. Y.
;
Tsai, Y. H.
;
Hsiao, K. M.
;
機械工程學系
;
Department of Mechanical Engineering
A curved slotted Geneva mechanism can eliminate the adversely infinite angular jerks of the Geneva wheel and might reduce the peak angular acceleration of the Geneva wheel by using a proper indexing motion program. In the literature, the cycloidal, fifth-order polynomial and modified sine indexing motion programs are frequently used for curved slotted Geneva mechanisms. To achieve the better kinematic performance of the curved slotted Geneva wheel than that obtained using the above-mentioned indexing motion programs, a new indexing motion program based on the Hermite interpolating polynomial is proposed for an optimum design with the goals of minimizing the peak angular acceleration and eliminating the adversely infinite angular jerks. The domain of the indexing position function is divided into several segments. Each segment is termed an element, and both ends of each segment are termed nodes. The nodal values of the indexing position function and its derivatives are used as design variables. The position function for each element can be described using the Hermite interpolating polynomial and the design variables. The reason behind the use of the Hermite interpolating polynomial is that the design variables have the clear physical meanings. The four-level Hermite interpolating polynomial is used and two elements are sufficient to obtain the optimum results. In addition, the constraint regarding the radius of curvature of the profile of the inner slot is proposed to prevent sharp curvature of the profile of the inner slot. The findings show that there is a decline in the peak acceleration of the Geneva wheel with six curved slots for the optimum results obtained using the proposed indexing motion program by 33.4% and 24.3%, respectively, as compared with the cycloidal and modified sine indexing motion programs.
Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm
(2017-05-01)
Lin, W. Y.
;
Tsai, Y. H.
;
Hsiao, K. M.
;
機械工程學系
;
Department of Mechanical Engineering
An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.