Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF₀. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.

在高速铣削过程中，实时检测早期颤振是提高加工质量和材料去除率的重要策略。本文基于每转多次采样（MSPR）技术和二阶可靠性方法（SORM），提出了一种基于最大熵（MaxEnt）特征的可靠性模型方法，用于早期颤振的实时检测。为提高检测可靠性，采用MSPR采集多组每转一次采样数据（即MSPR数据），克服每转一次采样的缺点。提出的基于MaxEnt特征的可靠性模型方法在保证其可靠性的同时，解决了早期颤振的实时检测问题。通过使用具有 MaxEnt 特征的 SORM，故障风险函数 (FHF) 被估计为颤振指标。所提出的方法包括五个步骤。首先，设置先验参数。然后使用 MSPR 技术收集数据。接下来，计算一组作为颤振特征收集的数据的标准偏差，并通过应用带有 MaxEnt 特征的 SORM 来估计颤振指标 FHF。最后，基于估计的颤振指标FHF和阈值FHF，实现早期颤振的实时检测₀ . 所提出的方法应用于高速铣削过程。两个例子证明所提出的方法可以检测两种早期颤振：早期严重颤振和轻微无法忍受的颤振。