To find the weak link of the structural stiffness is important to improve machine tool stiffness. However, how to overcome the static deformation with difficulty acquisition is a difficult problem in machine tool structure. The article takes the cantilever beam structure as a numerical example, the weak link is modeled as EA reduction in stiffness. Thorough finite element simulations are performed to assess the robustness and limitations of the method in several scenarios with single and multiple weaknesses. The sensors are used to acquire the acceleration data, the structural modal parameters are obtained by the singular value decomposition technique, and the dynamic characteristics are systematically reconstructed by using the modal state-space method to obtain static stiffness. Then, an identification method proposed by measured data and reconstructed data to identify the weak link of stiffness of the cantilever structure. Furthermore, the comparison of numerical and experimental results validate the correctness and effectiveness of this method. The research has certain practical engineering value and provides an accurate guidance for the optimization of machine tool stiffness.

No abstract

Investigating weak parts of the structure is one of the most important issues for improving the stiffness of the machine tool. However, studies show that overcoming the static deformation is a challenging problem in practical structures. In the present study, the dynamic hammer testing approach is applied to analyze the cantilever structure of the machine tool with elastic support. Accordingly, a new weakness index (WI) is proposed to identify weak parts of the cantilever structure with an elastic support. Then the cantilever beam with the elastic support is numerically simulated and weak parts are modeled as stiffness reduction. In this regard, finite element (FE) simulations are carried out to evaluate the effectiveness of the WI method in several scenarios with single and multiple weaknesses, including the noise case. In the combined structure of the tailstock and the bed of the machine tool, sensors are utilized to collect vibration data. Furthermore, the dynamic characteristics are calculated through the modal state-space method to obtain the stiffness data at zero-frequency. Then, weak parts of the structural stiffness are identified based on the weakness index. It is found that the FE simulations are in an excellent agreement with the experiment. Therefore, it is proved that the WI can accurately identify the weak parts of the machine tool cantilever structure.

The weak part of the stiffness of machine tool combined structure is the key to improve the stiffness of machine tool. To overcome the static deformation with difficulty acquisition, the paper chooses machine tool combined structure which can be equivalent to one-dimensional bar structure, and a weakness index (WI) is proposed to identify the weak part of the stiffness by means of the dynamic hammer test method. Based on the bar structure as a numerical example, the weak parts are modeled as EA reduction in stiffness while the mass is maintained at a constant value. Thorough finite element (FE) method simulations are performed to assess the robustness and limitations of the method in several scenarios with single and multiple weakness. On the crossbeam of gantry type machine tool, the sensors are used to collect vibration data, the structural modal parameters are obtained by singular value decomposition (SVD) technique, and the dynamic characteristics are systematically reconstructed by using modal state space method to obtain stiffness data at zero-frequency. Then, the weak part of the structural stiffness is identified by the weakness index. Finally, the comparison of FE simulations and experiment results are provided to illustrate the working of the method.

In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer train time of deep neural network, and the problem of gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed. Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors. Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU. Finally, the learned features are activated by softmax function and the recognition results are output. The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application. The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the F-value is better than other methods.