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文章针对影响数控机床加工精度的温度误差进行研究分析,提出了一种基于蚁群算法优化BP神经网络的数控机床热误差补偿方法。文章详细阐述了蚁群算法和BP神经网络算法,以及蚁群算法优化BP神经网络过程,给出了数控机床热误差补偿硬件系统。以三轴联动卧式加工中心为例,在合理布置热传感器的同时,利用粗集理论相关知识方法,提取机床热误差补偿重要特征参数。运用蚁群算法优化BP神经网络建立精确较高和鲁棒性较强的热误差模型。通过实验仿真结果表明经补偿机床加工精度得到较大提升,补偿效果也明显优于BP神经网络以及PSO-BP神经网络,具有一定的理论意义和实用价值。
In this paper, the temperature error affecting the machining accuracy of NC machine tools is studied and analyzed, and a thermal error compensation method of CNC machine tools based on the optimization of BP neural network based on ant colony algorithm is proposed. The article elaborates ant colony algorithm and BP neural network algorithm, as well as ant colony optimization BP neural network process, gives a hardware error compensation system of CNC machine tool. Taking the three-axis horizontal machining center as an example, the thermal sensor is also properly arranged, and the important characteristic parameters of machine tool thermal error compensation are extracted by using the knowledge of rough set theory. The ant colony algorithm is used to optimize the BP neural network to establish the accurate and robust thermal error model. The experimental simulation results show that the machining accuracy of the compensated machine tool is greatly improved, and the compensation effect is obviously superior to the BP neural network and the PSO-BP neural network, which has certain theoretical significance and practical value.