基于深度自编码器的大型龙门加工中心热误差建模方法

为提高热误差模型的预测能力，提出一种基于深度学习方法的数控机床热误差建模方法。利用模糊聚类法和灰色关联度分析法选取温度变量的热敏感点，采用深度自编码器（Stacked automatic encoder, SAE）网络从选出的输入样本中提取特征，构建特征集，然后使用遗传优化算法（Genetic optimization algorithm, GA）对BP神经网络参数进行寻优，从而提出一种基于SAE-GA-BP的数控机床热误差建模方法。以某大型龙门五面加工中心为实验对象，研究并选择了加工中心加工过程中的主要误差源——主轴热误差进行补偿，对主轴热误差深度学习模型和多元回归模型进行了分析对比。结果表明，在预测精度方面所提出的建模方法优于传统多元回归模型，从而验证了该建模方法的可行性和有效性。

Thermal Error Modeling Method Based on Stacked Auto-encoder for Large Gantry Fivesided Machining Center

A thermal error modeling method of NC machine tools based on deep learning method was proposed in order to improve the prediction ability of thermal error model. Fuzzy clustering method and grey relationship analysis method were used to select the sensitive points of temperature variables and the stacked automatic encoder (SAE) network was used to extract the features of the temperature variables from the selected input samples to construct the feature sets. Then, genetic optimization algorithm (GA) was used to optimize BP neural network parameters so as to propose a thermal error modeling method based on SAE-GA-BP neural network for NC machine tools. Taking a large gantry five sided machining center as the experimental object, the spindle thermal error of the large gantry five sided machining center was studied and selected as the main error source to achieve compensation in the machining process. The deep learning model of main shaft thermal error was compared with the multiple regression model. The experimental results showed that the proposed modeling method was better than the traditional multiple regression model in prediction accuracy of the thermal error of NC machine tools, which verified the feasibility and effectiveness of the proposed thermal error modeling method.