基于模态区间-马尔科夫链的动刚度预测

动刚度反映着数控加工过程中的稳定性,对加工精度有直接影响.对动刚度进行预测,预知动刚度的变化趋势,可为处理数控机床动刚度劣化问题提供指导作用.传统的马尔科夫链模型预测方法难以处理不确定性问题,将降低预测结果的可靠性.将模态区间数学理论应用于传统马尔科夫链中,提出一种基于模态区间-马尔科夫链模型的预测方法,以提高预测结果的可靠性.为了验证提出的预测方法的有效性,设计了一个数控机床相对激振实验.通过相对激振方法获取历史动刚度数据,采用模态区间-马尔科夫链模型对动刚度劣化趋势进行预测.结果表明:提出的预测方法有一个更好的预测精度.

Dynamic Stiffness Prediction Based on Modal Interval-Markov Chain

Dynamic stiffness plays an important role in stability and accuracy in CNC machining process.Dynamic stiffness prediction can foresee the trend of dynamic stiffness and provide guidance for solving degradation problem of dynamic stiffness of CNC machine tool.The traditional Markov chain prediction method can not consider uncertainty problem which will reduce reliability of the prediction result.A modal interval-Markov chain model is proposed to improve reliability of prediction,it is an extension of the traditional Markov chain.In order to verify the validity of the proposed prediction method,a relative excitation experimental is set up on CNC machine tool.Dynamic stiffness historical data is obtained by relative excitation method.Dynamic stiffness degradation trend is predicted by modal interval-Markov chain model.The results show that the proposed prediction method presents better prediction accuracy.