共查询到19条相似文献,搜索用时 78 毫秒
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数控机床具有非常高的加工精度与加工效率,是机械制造行业不可或缺的重要设施。数控机床在运行过程中,主轴位置会产生大量的热量,导致数控机床加工精度降低,为此,必须要尽可能消除主轴热误差。主轴热能主要来源于外部环境以及机床本身热能,其中电动机发热与轴承发热产生的热能难以有效去除,需要分析发热缘由并计算热量大小。为了有效控制主轴热误差,可以从改进结构并增强温度控制水平、额外增加热源与自身热能相平衡、构建热误差-温升模型三个方面进行。 相似文献
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本文提出了一种基于自组织原理的主轴热误差补偿策略,它只需根据对主轴热倾斜状态的定性测量结果即可进行定量误差补偿,从而可以大大降低对误差测量精度的要求及测量成本,同时各补偿力间的协调关系根据自组织原则自动建立,简化了补偿算法。经过对某型卧式加工中心主轴热误差进行的自组织仿真补偿,其主轴热倾斜误差减小了92%以上,热偏移误差减小了46%以上。 相似文献
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针对无线传感器热电耦合自供能存在的输出电量低和电压波动等问题,提出一种新型能量管理系统电路拓扑结构及基于最优时间的充放电控制策略,以保证自供能无线传感器在各种主轴转速条件下稳定工作。建立了机床主轴的热网络模型,分析了热发电能量管理系统输入特性。然后,设计了能量管理系统的多电容电路拓扑结构,并通过电容充放电时间参数的优化计算,获取最优的热发电平均输出功率。实验研究证实了在主轴不同转速下热发电构件及能量管理系统可以使无线传感器稳定的工作。对不同的电容充电和放电时间设置方案进行了比对,验证了最优时间控制策略的优越性。最后,利用热电耦合自供能无线传感器和传统有线传感器所监测的温度数据分别进行了主轴轴向热变形建模实验,结果表明:采用无线传感器可以监测有线传感器难以配置的主轴核心部件,从而获取和主轴热变形具有更高相关性的温度数据,使所建热变形预测模型的误差减少约40%以上。 相似文献
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为更精密地对机床主轴热误差进行预测,对主轴温度场和热变形进行机理分析,提出用热特性基本单元试验对初步理论模型进行修正从而得到最终模型的建模方法。根据主轴的尺寸和轴承参数对主轴温度场和热变形进行机理分析并确定初步理论模型,在不同起始温度下进行两组热特性基本单元试验,试验中采用温度传感器对主轴前后轴承及前端面的温度进行测量,采用位移传感器对主轴轴向热变形进行测量,得到主轴在升温和降温过程中温度场和热变形的特性数据,基于该数据对初步理论模型进行修正。在一台数控车床主轴上进行模型的试验验证,结果表明:该建模方法能同时对主轴升温和降温过程进行温度场和热变形的建模与预测,且具有高精度的优点,可用于各种数控机床主轴热变形的预测。 相似文献
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机床热误差是影响高精密机床加工精度的重要因素之一,而目前对机床热误差的分析比较少,以机床导轨为研究对象提出了一种结合有限元理论的导轨热误差确定方法,将数值模拟技术和实际测量实验相结合,利用实验测量数据修正有限元分析边界条件,从而得到准确的导轨热变形计算结果,证明了该热误差确定方法应用到实际机床导轨热误差确定和补偿方面的... 相似文献
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通过建立预测模型对机床热误差进行补偿,是有效解决热误差造成机床精度下降问题的常用方法。本文提出一种基于正则化的数控机床热误差自适应稳健建模算法,能够在建模过程中自适应选择温度敏感点(TSPs),并具有高预测精度和稳健性。首先基于结构风险最小化原则对热误差建模稳健性机理进行分析,进而利用正则化算法中LASSO解的稀疏性实现自适应TSP选择。然后基于不同实验条件的热误差数据,分析所提建模算法的预测效果,并与常用的多元线性回归、BP神经网络和岭回归算法进行比对分析。结果表明,本文所提建模算法具有最高的预测精度和稳健性,分别为5.22和1.69μm。最后,利用所建立的预测模型进行热误差补偿实验,以验证本文所提建模算法的实际补偿效果。 相似文献
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热误差严重影响着机床的加工精度,对机床关键部件进行热特性分析是开发精密机床的重要环节。通过测量包括数控机床的特殊位置温度和定位误差在内的热特性,研究了温升与定位误差之间的关系,提出了一种基于贝叶斯神经网络的热误差建模方法。通过K-means聚类和相关系数法来选择温度敏感点,可以有效地抑制温度测量点之间的多重共线性问题。结果表明:通过使用贝叶斯神经网络能提高机床88.015 9%的精度,比BP神经网络高出15.763 8%,与BP神经网络模型相比,贝叶斯神经网络具有更加优良预测性能。贝叶斯神经网络模型为降低机床热误差的影响提供了新思路。 相似文献
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Yang Li Wanhua Zhao Wenwu Wu Bingheng Lu Yubao Chen 《The International Journal of Advanced Manufacturing Technology》2014,72(9-12):1415-1427
Thermal error, especially the one caused by the thermal expansion of spindle in axial direction, seriously impacts the accuracy of the precision machine tool. Thermal error compensation based on the thermal error model with high accuracy and robustness is an effective and economic way to reduce the impact and enhance the accuracy. Generally, thermal error models are built only on temperatures at some points in the spindle system. However, the thermal error is also closely related to other working parameters. Through the theoretical analysis, the simulation, and the experimental testing in this paper, it is found out that thermal error is determined by multiple variables, such as the temperature, the spindle rotation speed, the historical spindle temperature, the historical thermal error, and the time lag between the present and previous times. In order to examine the performance of thermal error models based on multiple variables, two common methods are used for modeling—the multiple regression method and the back propagation network. The data for modeling are collected from experiments conducted on the spindle of a precision machine tool under various working conditions. The modeling results demonstrate that models established based on the multiple variables have better accuracy and robustness. It also turns out that data filtering before modeling can further improve the performance of the models. Therefore, models based on multiple variables with good accuracy and robustness can be very useful for the further thermal error compensation. In addition, by taking relative importance analysis of multiple variables based on standardized regression coefficients, the influence of each variable to the thermal error is revealed. The ranking of coefficients can also be used as a new criterion for the optimal temperature variable selection in the future research. 相似文献
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Compensation of machine tool thermal deformation in spindle axis direction based on decomposition method 总被引:2,自引:0,他引:2
Jiri Vyroubal 《Precision Engineering》2012,36(1):121-127
One of the fundamental areas in high precision cutting is represented by the machine's thermal state monitoring. Understanding of this state gives significant information about the overall machine condition such as proper performance of cooling system as well as software compensation of machine's thermal deformation during manufacturing. This paper presents a method focused on compensation of machine's thermal deformation in spindle axis direction based on decomposition analysis. The machine decomposition is performed with the help of specially developed measuring frame, which is able to measure deformation of machine column, headstock, spindle and tool simultaneously. Compensation is than calculated as a sum of multinomial regression equations using temperature measurement. New placements of temperature measurement like spindle cooling liquid or workspace are used to improve the accuracy of this calculation. Decomposition process allows describing each machine part's thermal dynamic more precisely than the usual deformation curve usually used one deformation curve for the complete machine. The residual thermal deformation of the machine is considerably reduced with this cheap and effective strategy. The advantage is also in the simplicity of presented method which is clear and can be used also on older machines with slower control systems without strong computing power. 相似文献
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