Ben Wang, Hong Lu, Qi Liu, Shaojun Wang, Hengchen Pan, Jiashun Dai
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引用次数: 0
Abstract
Dual-drive feed system (DDFS) is widely used in computer numerical control (CNC) machine tools. In the process of machining, it is necessary to ensure the complete synchronization of the two axes of the feed system, otherwise it will affect the machining accuracy and shorten the life of the machine tool. Due to the structure error of the DDFS and the uneven distribution of the load on the two axes in the process of machining irregular workpiece, there are synchronization errors between the two axes. Therefore, it is of great significance to reduce the synchronization errors by studying the dual-drive synchronous control strategy. In this paper, fuzzy control is introduced into traditional PID synchronous control strategy. Compared with traditional PID control, fuzzy control has the characteristics of high robustness and high control performance. Firstly, the PID model of single-axis servo feed system is established. Then, the master-slave control strategy is selected as the dual-drive synchronous control strategy and the model of master-slave control strategy based on conventional PID (MSCS-CPID) is established. Next, the fuzzy PID control is introduced into the current loop of the servo feed system and the model of master-slave control strategy based on fuzzy PID (MSCS-FPID) is established. The simulation results of the MSCS-CPID and the MSCS-FPID show that the DDFS under the MSCS-FPID has faster response speed and smaller synchronization errors. Moreover, the DDFS under the MSCS-FPID has better synchronization performance after external interference. Experiment confirmed that the synchronization performance of the MSCS-FPID is better than that of the MSCS-CPID.
期刊介绍：
The Journal of Micro and Nano-Manufacturing provides a forum for the rapid dissemination of original theoretical and applied research in the areas of micro- and nano-manufacturing that are related to process innovation, accuracy, and precision, throughput enhancement, material utilization, compact equipment development, environmental and life-cycle analysis, and predictive modeling of manufacturing processes with feature sizes less than one hundred micrometers. Papers addressing special needs in emerging areas, such as biomedical devices, drug manufacturing, water and energy, are also encouraged. Areas of interest including, but not limited to: Unit micro- and nano-manufacturing processes; Hybrid manufacturing processes combining bottom-up and top-down processes; Hybrid manufacturing processes utilizing various energy sources (optical, mechanical, electrical, solar, etc.) to achieve multi-scale features and resolution; High-throughput micro- and nano-manufacturing processes; Equipment development; Predictive modeling and simulation of materials and/or systems enabling point-of-need or scaled-up micro- and nano-manufacturing; Metrology at the micro- and nano-scales over large areas; Sensors and sensor integration; Design algorithms for multi-scale manufacturing; Life cycle analysis; Logistics and material handling related to micro- and nano-manufacturing.
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