基于灰色系统的机床热误差建模研究

Application of Grey System Model to Thermal Error Modeling on Machine Tools

针对机床加工过程中的热变形误差受多因素影响,变化趋势复杂,难以用常规预测方法进行有效预测的问题,该文提出了一种新的基于改进灰色系统的智能预测模型。该模型利用函数变换法改善灰色系统数据序列的光滑度,采用等维新陈代谢法克服了传统的灰色预测模型的不足,所建模型具备了输入数据动态更新的能力,预测更趋于合理。将该模型应用于工厂现场的一台数控车削加工中心进行热误差趋势的预测,从而实现热误差的补偿研究。研究表明,该模型的预测性能优于全数据GM(1,1)模型和新信息GM(1,1)模型,是运用灰色系统理论进行机床热误差补偿建模最理想的模型,具有优异的补偿功能,能够有效的提高机床加工精度。

Due to the fluctuation and complexity of thermal errors of machine tcols affected by various factors, it was difficult to use a single prediction method to accurately describe thermal errors＇ moving trend. So a new prediction model based on improved grey system was proposed. In this model, the degree of smoothness of the data sequence was improved by function transform, then metabolic method was used to improve the deficiency of traditional GM （1,1）, namely metabolic GM （1,1） was capable of dynamic refreshment of input data. This model was applied to the trend prediction of thermal errors in a spot CNC turning center, testing results showed that the prediction performance of metabolic GM （1,1） outperformed any one of traditional GM （1,1） and new information GM （1,1） and metabolic GM （1,1） was most optimal model in thermal error modeling among three GM （1,1） models.