Abstract
High-end computer numerical control (CNC) machine tools, recognized as the “mother machines”of the equipment manufacturing industry, have been identified by China as a strategic priority requiring accelerated breakthroughs, with intelligence being a critical development direction. Among their components, the spindle system plays a pivotal role in determining the machining performance and reliability of high-grade CNC machine tools, making it the undisputed core functional unit. Meanwhile, with the growing demand for high-performance machining in advanced manufacturing sectors such as aerospace, automotive, and energy, intelligent spindle technology—capable of state sensing and performance regulation— has become a key focus in the development of high-end CNC machine tools. As the critical component directly clamping and driving cutting tools, two core challenges must be addressed to advance high-performance and intelligent spindles:how to achieve online, accurate perception and evaluation of the cutting process and the spindle’s operational state to ensure machining stability and performance, and how to enable intelligent active control of the spindle to enhance machining quality under complex working conditions. In this context, the research team has developed a series of methods for state sensing, evaluation, and active vibration suppression tailored to intelligent spindles, addressing long-standing issues such as difficulties in state assessment and weak active control capabilities. Two types of intelligent electric spindle prototypes are developed, demonstrating capabilities in online state evaluation, chatter monitoring, and chatter suppression. These prototypes have been applied in select machine tool enterprises, providing theoretical foundations and enabling technologies for the development of intelligent spindles in China’s machine tool industry.
| Translated title of the contribution | Key Technologies and System Development of Intelligent Electric Spindles |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 104-114 |
| Number of pages | 11 |
| Journal | Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University |
| Volume | 59 |
| Issue number | 11 |
| DOIs | |
| State | Published - 2025 |
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