Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results.\ud\udIn this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model.
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机译:热误差通常被认为是造成CNC机床误差的最大因素,但是可以通过误差补偿有效地减少热误差。热误差补偿系统的性能取决于热误差模型的准确性和鲁棒性以及模型输入的质量。温度测量的位置必须对将影响机器结构的温度变化提供代表性的测量结果。传感器的数量及其位置并不总是直观的,识别最佳位置所需的时间通常令人望而却步,从而导致折衷和不良结果。\ ud \ ud本文提出了一种新型智能补偿系统,用于减少机床的热误差介绍了使用从热像仪获得的数据的方法。使用基于灰色模型GM(0,N)和模糊c均值(FCM)聚类方法的新颖模式,从热图像中识别出不同的关键温度点组。采用具有模糊c均值聚类的自适应神经模糊推理系统(FCM-ANFIS)来设计热预测模型。为了优化该方法,通过更改FCM-ANFIS模型的输入数量和隶属函数数量,并比较设计的相对健壮性,进行了参数研究。根据结果,具有4个输入和6个隶属函数的FCM-ANFIS模型就其预测能力的准确性而言达到了最佳性能。模型的残值小于±2μm,这表示机器上的热致误差降低了95%。最后,该方法被证明与人工神经网络(ANN)模型相比具有优势。
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