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.\ud\udA 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 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.
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机译:热误差会严重影响CNC机床的精度。误差是由机器内部的热源或环境温度变化引起的机器元件的热变形引起的。可以通过避免误差或进行数值补偿来降低温度的影响。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鲁棒性。本文首先回顾了设计热误差模型的不同方法,然后着重于采用自适应神经模糊推理系统(ANFIS)设计两个热预测模型:将数据空间划分为矩形子空间的ANFIS(ANFIS-Grid模型)和通过使用模糊c均值聚类方法(ANFIS-FCM模型)进行ANFIS。灰色系统理论用于获得所有可能的温度传感器对机器结构热响应的影响等级。使用模糊c均值(FCM)聚类方法将热传感器的所有影响权重聚类为组,然后通过相关分析进一步减少组。\ ud \ ud使用小型CNC铣床的研究来提供训练为建议的模型提供数据,然后提供独立的测试数据集。研究结果表明,ANFIS-FCM模型在其预测能力的准确性方面具有优势,并且规则更少。该模型的残值小于±4μm。这种组合的方法可以提高热误差补偿系统的准确性和鲁棒性。
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