• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.214-220
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• 2009

The spindle is the main component in machine tools. The static and dynamic stiffness of the spindle directly affect the machining productivity and surface integrity of the workpiece. The static and dynamic stiffness of the spindle depend on the shaft size, bearing arrangement, bearing span length, and so on. Therefore, the selection of shaft size and bearing span length are important to improve the spindle stiffness. This paper presents the determination of shaft size and bearing span length in spindle design step. In order to select the optimal bearing and built-in motor locations with constraint conditions, the extreme vertices design was applied. The results show that extreme vertices design is usable for spindle design with design constraints.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.53-56
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• 2000

The problem ( Ρ ) of optimizing a linear function d$\^$T/x over the set of efficient set for a multiple objective linear program ( Μ ) is difficult because the efficient set is nonconvex. There some interesting properties between the objective linear vector ｄ and the matrix of multiple objectives C and those properties lead us to establish criteria to solve ( Ρ ) with a linear program. In this paper we investigate a system of the linear equations C$\^$T/${\alpha}$=d and construct two linearly independent positive vectors ${\mu}$, ν such that ${\alpha}$=${\mu}$-ν. From those vectors ${\mu}$, ν, solving an weighted sum linear program for finding an efficient extreme point for the ( Μ ) is a way to get an optimal solution ( Ρ ). Therefore our theory gives an easy way of solving nonconvex program ( Ρ ) with a weighted sum linear program.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.212-215
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• 2008

In this paper, we develop the optimal path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. The MAKLINK is a set of edges which consist of the convex set. Some of the edges come from the edges of the obstacles. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1] through the experiment.