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In the process of Heavy NC machine tool design, the combination of static, dynamic characteristics directly determines and influences stiffness, damping, machining precision and work efficiency, Therefore the research on the combination of the characteristics become extremely important to the success of machine tool design. This study mainly studies the dynamic and static characteristic parameters of heavy machine tool guide way joint. And put forward a practical machine tool combining surface analysis method according to the experimental verification. How to obtain the analysis and test of the dynamic parameters to the combination is introduced. It is presented that small linear guide as a scale model based on similarity theory. And make a research on the static and dynamic characteristics of small guide joint and the conclusion extended to heavy machine set of faces. And the conclusion is extended to the heavy machine set of faces. It is introduced the spring damping unit of combined surface characteristics simulation during a small test about guide way dynamic performance analysis. Thus it can more effective simulate influence of combination of surface characteristics to components of the overall structure.The study analyze the dynamic performance of machine tool bed rail section by the verification and treat extraction of the first five modes as the analysis and forecast of the models of guide-way joint dynamic performance. The results can be used as reference data about structure and design of the machine tool due to the similarity principle and error range.

Abstract:The choice of the process parameters in the conventional tube bending forming is often based on experience and adjusted by repeated bending tests. The method of constantly testing to adjust has seriously affected the production efficiency and increased production costs. In this paper, neural network is used to establish the intelligent prediction model of the pipe forming process parameters. The obtained datum from analytical calculations, numerical simulations and experiments then serve as the training samples and test samples of neural network training. By the trained neural network, the intelligent prediction for the main process parameters including the bending moment and the boost power can be executed. The test results show that the average relative error between the simulation output and target output of bending moment and boost power is less than 2%, and the predicted process parameters, i.e. bending moment and boost power, can be directly used for actual production.

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