Papers by Keyword: Support

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Authors: Bing Hui Wu, Bao Jun Pang, Zong Quan Deng
Abstract: Support is not load bearing components, but also carrier of other component. To reduce the valid load, mass of the support should be low down under the condition of enough stiffness and strength. Topology optimization is employed here to solve the problem. The finite model must be built up according to the basic structure of analysis object in topology optimization. The supports are typical box-style part with thin-wall. Thus, solid model is adopted in ANSYS, and the element shell93 is employed here. Definition of optimization function based on linear-static analysis was employed here. Take maximum flexibility as the constrain conditions of the structure. And the optimality criterion (OC) method was adapted to the problem due to it is suitable for the problem which target is volume. Define the base plate, face and back of the support as the topology area separately. Main part of support is optimization with the variable density method. The results before and after the optimization are compared. When the topology form is unknown, the best topology relation of the structure in the initiate design stage of the whole product has very important meanings.
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Authors: Xu Dong Song, Jian Wei Mu, Rui Fang Feng, Zhan Zhi Qiu
Abstract: The calculation of Variable precision explicit region is an improved algorithm for constructing decision tree on the use of variable precision rough set model. For defects in the process of calculating the explicit region—in the process of calculating the explicit region, the more the number of attributes is, the greater the value of specific areas is, it puts forward the calculation algorithm that the number of attributes limits specific area. This algorithm enhances the accuracy of the calculation process. It can effectively reduce the trend that the more the classification of attributes is, the greater the greater the value of specific areas is. In the meanwhile, it also effectively improves the accuracy of the algorithm. By introducing the support and confidence, it simplifies the resulted tree, and improves the generalization ability of the tree. Finally, the validity of the method is verified through experimental analysis.
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