For high-speed machining, the position commands from the trajectory-controlled algorithm should be as smooth as possible since even a small discontinuity in position command may lead to vibrations of the mechanical structure and the servo system. A simplified mechatronic model was proposed to analyze the impact of the trajectory-controlled algorithm on the performance of high-speed machining. The effects of the control loop parameters and natural frequency of mechanical structure on the vibration were also studied. Experimental tests on the vertical z-axis of a high-speed machining center indicate that the presented methodology is able to evaluate the evolution of the vibration due to the trajectory-controlled algorithm, and it is also helpful to choose the suitable controlled parameters for improving the mechatronic performance of high-speed machining.
In order to select the best possible design from various candidates based on structure configurations of traveling components, it is essential to evaluate the effects of traveling joints on dynamic behavior of large machine tools. Firstly, regression analysis and energy method were used to determine the nonlinear parameters of the metal-plastic joints. Then, a nonlinear receptance coupling approach was used to establish a simplified model of a large machine tool including nonlinear joints. The evaluations of the large machine tool were analyzed with different milling forces. The results show that the nonlinearity of traveling joints significantly influences the resonant frequency and the response amplitudes. Experimental verifications were performed on a prototype of a large machine tool for milling large gears.
The ability of acquiring and processing information in manufacturing influences agile of manufacturing system. According to the idea that networked field information processing is realized based on intelligent nodes of field-bus technology, distributed intelligent field information processing flow is researched to synthesize multiple functions such as information gathering, information processing, warning system and field control. The method of information represented and collected was put forward. The multilayer data fusion model of distributed intelligent field information processing is built. Then the distributed LonWorks fieldbus monitoring model (DLFMM) based on LonWorks fieldbus technology is established. A monitoring system of a rail vehicle automatic door factory is shown as an example to illustrate design strategy of monitoring system based on DLFMM. This monitoring system shows that the model of distributed intelligent field information processing, DLFMM and the extraction and representation of information flow discussed in this paper are reasonable and applicable.
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