The application of ANFIS prediction models for thermal error compensation on CNC machine tools
The application of ANFIS prediction models for thermal error compensation on CNC machine tools
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DOI：
10.1016/j.asoc.2014.11.012
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发表时间：
2015-02-01
影响因子：
8.7
通讯作者：
Fletcher, Simon
中科院分区：
文献类型：
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作者：
Abdulshahed, Ali M.;Longstaff, Andrew P.;Fletcher, Simon
热误差会对数控机床的精度产生重大影响。误差来自机器结构内的热源或环境温度变化引起的机器元件的热变形。温度的影响可以通过误差避免或数值补偿来减小。热误差补偿系统的性能主要取决于热误差模型及其输入测量的准确性和稳健性。本文首先回顾了热误差模型的不同设计方法，然后重点介绍了利用自适应神经模糊推理系统(ANFIS)设计的两种热预测模型：将数据空间划分为矩形子空间的ANFIS(ANFIS-GRID模型)和使用模糊c-均值聚类方法的ANFIS(ANFIS-FCM模型)。利用灰色系统理论，得到了所有可能的温度传感器对机械结构热响应的影响排序。利用模糊C-均值(FCM)聚类方法将温度传感器的所有影响权重聚为一组，然后通过相关性分析进一步约简这些组，并以一台小型数控铣床为例为所提出的模型提供训练数据，然后提供独立的测试数据集。研究结果表明，ANFIS-FCM模型具有预测精度高、规则数少的优点。该模型的残差值小于+/-4微米，这种组合方法可以提高热误差补偿系统的精度和鲁棒性。(C)2014年提交人。爱思唯尔出版公司(Elsevier B.V.)
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than +/- 4 mu m. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system. (C) 2014 The Authors. Published by Elsevier B.V.