Multi-objective optimum designs support device using mathematical process technique, its method and program

Abstract

The present invention discloses a multi-objective optimum designs support device using mathematical process technique, method and program thereof. A unit (101) calculates a plurality of sets of objective functions of sample sets of input parameters. A unit (102) approximates the objective functions using a polynomial on the basis of the calculation result of the unit (101). A unit (103) calculates a logical expression indicating a logical relation between arbitrary two or three objective functions, of the plurality of mathematically approximated objective functions as an inter-objective function logical expression by a QE method. A unit (104) displays areas that the values of the objective functions can take as usable areas according to the inter-objective function logical expression. Units (105) and 106 determine an optimum set of design parameters by limiting the sets of design parameters to corresponding sets of design parameters in the neighborhood of a Pareto boundary on the basis of the Pareto boundary of an objective function recognized from the usable areas displayed.

Description

technical field [0001] The present invention relates to a multi-objective optimal design assisting device used in the design of the slider shape of a hard disk and the like. Background technique [0002] With the increase in density / capacity of hard disks, the distance between the disk and the head has become smaller and smaller. A slider design with a small amount of flying change due to height difference and disk radial position is desired. [0003] Such as figure 1 As shown in 1601 in , the slider is installed on the lower part of the top of the actuator 1602, the actuator 1602 moves on the magnetic disk in the hard disk, and the position of the magnetic head is calculated based on the shape of the slider 1601. [0004] When determining the optimal shape of the slider 1601, it is necessary to minimize the flying height ( figure 1 1603), roll (1604) and pitch (1605) functions in (ie, so-called multi-objective optimization). [0005] In the past, instead of directly dea...

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Problems solved by technology

[0015] In addition, since only one point on the Pareto surface can be obtained at a time in minimizing the function value f, it is difficult to predict the optimal relationship between the objective functions, and this information cannot be fed back to the design, these are problems

[0016] Furthermore, in the prior art for calculating Pareto surfaces by numerical analysis methods, it cannot be solved when the available area is non-convex, and when the points (endpoints) that are sources in the calculation of Pareto surfaces are nearby , cannot carry out the algorithm operation smoothly, these are the problems

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