基于狄利克雷混合模型的刀具磨损量在线估计

提出了一种基于狄利克雷混合模型的刀具磨损状态监测和磨损量估计的新方法。该方法将刀具磨损过程描述为磨损量的累积过程,通过对磨损增量的连续估计获得刀具当前的磨损量估计。首先对原始力信号进行特征提取,接着在不确定磨损增量状态数量的前提下采用狄利克雷混合模型对特征自动分类,然后利用吉布斯采样方法确定模型参数,最终得到描述力信号特征与磨损增量映射关系的刀具磨损状态混合模型。根据该混合模型以及当前的力信号信息即可完成刀具磨损量的在线估计。真实应用案例证明了该方法能自适应学习磨损状态并有效估计刀具的连续磨损值。

Tool wear on line estimation using a Dirichlet process mixture model

A new method based on Dirichlet Process Mixture Model (DPMM) is proposed for tool wear monitoring and tool wear estimation. This method describes the toolwear process as a wear accumulation process. Thus, the current tool wear is estimated by continuously estimating the wear increments. Firstly, the features are extracted from the raw force signals, and DPMM is used to classify these features automatically without determining the number of states of wear increments. Then, Gibbs sampling method is applied to identify the parameters of DPMM, which constructs the relationship between force signal features and wear increments. Based on the mixture model and on line force signals, the wear estimation can be achieved. A practical study demonstrates that the proposed method is capable of self adaptively learning wear increment states and effectively estimating the continuous tool wear.