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A New Error Prediction Method for Machining Process Based on a Combined Model

机译：基于组合模型的加工过程误差预测新方法

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摘要

Machining process is characterized by randomness, nonlinearity, and uncertainty, leading to the dynamic changes of machine tool machining errors. In this paper, a novel model combining the data processing merits of metabolic grey model (MGM) with that of nonlinear autoregressive (NAR) neural network is proposed for machining error prediction. 'I he advantages and disadvantages of MGM and NAR neural network are introduced in detail, respectively. The combined model first utilizes MGM to predict the original error data and then uses NAR neural network to forecast the residual series of MGM. An experiment on the spindle machining is carried out, and a series of experimental data is used to validate the prediction performance of the combined model. The comparison of the experiment results indicates that combined model performs better than the individual model. The two-stage prediction of the combined model is characterized by high accuracy, fast speed, and robustness and can be applied to other complex machining error predictions.
机译：加工过程的特点是随机性，非线性和不确定性，从而导致机床加工误差的动态变化。本文提出了一种新模型，将代谢灰色模型（MGM）的数据处理优点与非线性自回归（NAR）神经网络的优点相结合，用于加工误差预测。 '分别详细介绍了MGM和NAR神经网络的优缺点。组合模型首先利用MGM预测原始误差数据，然后利用NAR神经网络预测MGM的残差序列。进行了主轴加工实验，并使用一系列实验数据验证了组合模型的预测性能。实验结果的比较表明，组合模型的性能优于单个模型。组合模型的两阶段预测具有准确性高，速度快和鲁棒性强的特点，可应用于其他复杂的加工误差预测。

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