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Research on feedrate intelligent control method for machining efficiency

delete2025-06-30
delete0
PRE
AI
Y
Yeming Jiang
K
Kuo Liu
J
Jiadong Huang
D
Di Zhao
P
Pengxiang Gao
M
Mingyu Li
刘海波 cover
刘海波 (Haibo Liu)
Y
Yongqing Wang
DOI：10.1016/j.ymssp.2025.113037delete
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Abstract

Abstract

En 中文
Traditional constant feed machining methods have several limitations, including the lack of online adjustment capabilities for machining parameters and an over-reliance on operator experience. Additionally, for workpieces with uneven allowance and high removal rates, the efficiency of traditional constant feed machining is notably low. To address these issues, this paper investigates a feedrate adaptive control method aimed at improving machining efficiency. Firstly, we analyze the signal data collected by the system. Using spindle system power as the signal for machining state monitoring, we study the composition of the collected spindle system power signals based on the energy transfer relationship of the spindle system. On this basis, we design a preprocessing algorithm for the power data, incorporating the concept of the command domain to mark the power data collected when the spindle speed is not constant. Secondly, a feedrate adaptive control method based on fuzzy theory is proposed to achieve adaptive control under constant power constraints. The proposed algorithm is validated through simulations, demonstrating good convergence. Furthermore, we design a control parameter self-adjustment algorithm. By constructing a reference model of the cutting process, this algorithm enables real-time adjustment of the fuzzy control parameters, ensuring the controller optimally performs at each stage of the machining process. Finally, experiments were conducted on an aerospace workpiece under both traditional constant feed machining mode and the proposed feedrate adaptive control machining mode. The results show that the proposed method improves machining efficiency by 23.1% and maintains spindle system power near the target power. This study is significant for enhancing CNC machining efficiency and promoting the intelligent transformation of the equipment manufacturing industry.

Journal

Mechanical Systems and Signal Processing cover
Mechanical Systems and Signal Processing
IF:
8.9
Papers:
1.3W
Citations:
6.6W

Organization

No organization information available