Gaussian process regression model incorporated with tool wear mechanism

Cutting tool condition directly affects machining quality and efficiency. In order to avoid severely worn tools used during machining process and fully release the remaining useful life in the meanwhile, a reliable evaluation method of remaining useful life of cutting tools is quite necessary. Due to the variation of cutting conditions, it is a challenge to predict remaining useful life of cutting tools by a unified model. In order to address this issue, this paper proposes a method for predicting the remaining useful life of cutting tools in variable cutting conditions based on Gaussian process regression model incorporated with tool wear mechanism, where the predicted value at adjacent moments is constrained to a linear relationship by the covariance matrix of Gaussian model based on the assumption of progressive tool wear process, so the wear process under continuous changing conditions can be modelled. In addition to that, the input feature space and the output of the model are also enhanced by considering the tool wear mechanism for improving prediction accuracy. Machining experiments are performed to verify the proposed method, and the results show that the proposed could improve the prediction of tool remaining useful life significantly.     

SINS/GNSS integrated navigation system based on maximum versoria filter

In the missile-borne Strapdown Inertial Navigation System/Global Navigation Satellite System (SINS/GNSS) integrated navigation system, due to the factors such as the high dynamics, the signal blocking by obstacles, the signal intefereces, etc., there always exist pulse interferences or measurement information interruptions in the satellite receiver, which make nonstationary measurement process. The traditional Kalman Filter (KF) can tackle the state estimation problem under Gaussian white noise, but its performance will be significantly reduced under non-Gaussian noises. In order to deal with the non-Gaussian conditions in the actual missile-borne SINS/GNSS integrated navigation systems, a Maximum Versoria Criterion Extended Kalman Filter (MVC-EKF) algorithm is proposed based on the MVC and the idea of M-estimation, which assigns a smaller weight to the anomalous measurements so as to suppress the influence of anomalous measurements on the state estimation while maintaining a relatively low calculation cost. Finally, the integrated navigation simulation experiments prove the effectiveness and robustness of the proposed algorithm.     

共轴刚性旋翼气动干扰及操纵特性分析

建立了一套基于高效配平策略和嵌套网格技术的共轴刚性旋翼气动数值分析方法。流场求解采用雷诺平均Navier Stokes方程，空间离散采用Roe monotone upstream centered schemes for conservation laws（MUSCL）格式，湍流模型为Spalart Allmaras模型；配平计算中引入“差量修正”策略。分析了共轴刚性旋翼模型在不同飞行状态下的气动干扰现象和操纵特性。结果表明:所建立的配平方法在同等精度下，比传统方法可显著节省计算时间；共轴刚性旋翼间距较小，其气动干扰明显强于普通共轴旋翼；悬停状态下，由于受上旋翼下洗流影响，下旋翼总距较大但拉力较小；小速度前飞时气动干扰引起下旋翼气流不对称性增大，双旋翼周期变距差也变大；采用适当的升力偏置量，可以提高共轴刚性旋翼气动效率。