基于广义马尔科夫链模型的动柔度预测

动柔度对机械加工的稳定性和精度有重要的影响。对动柔度进行预测能为机床加工精度补偿和稳定性分析提供实际的指导作用。由于不确定性的存在会降低结果的可靠性与可信度，传统的马尔科夫链预测方法没有考虑不确定性问题。为了提高预测结果的可靠性与可信度，在马尔科夫链模型的基础上提出了一个广义马尔科夫链模型，并通过广义马尔科夫链模型对动柔度进行了预测。结果表明：提出的预测方法有一个好的预测性能。

Dynamic compliance prediction based on generalized Markov chain model

Dynamic compliance plays an important role in machining stability and accuracy. Dynamic compliance prediction can provide practical guidance for precision compensation and stability analysis of machine tool. It is well known that uncertainty will cause the lower accuracy and reliability. The traditional Markov chain prediction method cannot consider uncertainty problem. In this paper, a generalized Markov chain model is proposed to improve the accuracy and reliability of prediction. Dynamic compliance in machining process is predicted by the proposed model. The results show that the proposed prediction method has a good prediction performance.