Thermal error modeling of machine tool based on dimensional error of machined parts in automatic production line

补偿(心理学) 机械加工 机床 计算机科学 热的 生产线 机械工程 维数(图论) 直线(几何图形) 近似误差 工程类 算法 控制理论(社会学) 人工智能 数学 几何学 控制(管理) 精神分析 纯数学 气象学 物理 心理学
作者
Hu Shi,Yao Xiao,Xuesong Mei,Tao Tao,Haitao Wang
出处
期刊:Isa Transactions [Elsevier]
卷期号:135: 575-584 被引量:11
标识
DOI:10.1016/j.isatra.2022.09.043
摘要

Thermally induced error has proven to be the major source of machining error for the machine tool working in a non-temperature-controlled workshop. Current research on thermal error modeling and compensation is implemented based on the measured thermal deformation of machine tool, and the data is obtained referring to spindle idling rather than metal cutting condition, resulting in the disadvantages of the established model with low adaptability to varying conditions. In this paper, a modeling and compensation method based on the dimensional error of the machined parts is proposed to address the issue and verified through machine tools in an automatic production line. Compared with the modeling based on thermal deformation, the method formulates a more direct relations between temperature rise and machining error. The thermal error modeling is carried out by measuring the dimension deviation of the inner hole diameter of the motor end cover and the screened representative temperature variables. Meantime, considering the temperature coefficient in model is difficult to converge when modeling with the single-day data, the unified modeling with the multi-day data is realized by improving the conventional multiple linear regression model. Finally, the generalized model used for thermal error compensation in x direction of the machine tool is obtained. The real-time compensation of the thermal error based on the established model is realized with the machine tool machining in mass production. The verification results show that this error modeling and compensation method can reduce the machining error of the end cover by more than 52%, irrespective of experiencing various complicated working conditions. Stability and robustness of the modeling are also validated through application on the other machine tool with the same configuration and real machining over seven days.
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