In this paper, the ram of boring and milling machining center is taken as the research object. A new method that hydraulic pull rods compensation is proposed to solve the problem of deformation compensation of long stroke ram of boring and milling machining center. Firstly, the method of finite element analysis is used to get the laws of ram deformation and the relationship curve between the ram deformation and the stroke of ram. Secondly, the preliminary calculation value of pull rods compensation force is derived based on the theoretical analysis of material mechanics. The relationship curve between compensation force and the stroke of ram is obtained by finite element analysis and polynomial least squares method. Finally, the analyzed results are as follows: the laws of ram deformation distribution is accurately predicted by the used method, the deflection error of the ram is well controlled,and the machining precision is significantly improved.
On the basis of summarizing the concept filtering methods in the current Ontology learning, a method of domain concept filtering in the semantic level based on combination of word embedding and conventional statistics was presented, which can identify low-frequency words well, and as far as possible to ensure universality. Through experimental contrast, the proposed approach was proved to have a higher accuracy rate than the ways based on statistics.
Ontology has be applied in many fields, such as data integration, system interoperability, etc. At present, there are many ontology extraction methods based on relational database, but they all assume that the database schema is at least in third normal form (3NF). A set of improved rules to extract ontology is presented in this paper. The ontology generated is described with the OWL language. Compared with other existing methods, this approach can be able to identify a variety of strategies about non-normalized design of relational databases and deal with them, while none of existing methods can identify all of them. The results show that our method provides a more accurate process of ontology learning from non-normalized relational database.
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