改善机床运动的五轴刀轨光顺方法

建立了五轴数控加工刀轨光顺优化模型,改善了线性插补方式下拐角过渡运动中的机床加速度情况。考虑了5个轴加速能力的差异,以轴加速度比,即轴实际达到的最大加速度与其允许最大加速度的比值,来衡量轴的加速度情况,以5个轴加速度比的平方平均数在整体上衡量机床的加速度情况,降低该值可提高机床运动平稳性。采用最短距离线法在初始刀轨的各切触点处建立了刀轴偏航角和俯仰角的可行域,使光顺后的刀轨满足残高和无过切要求。实例表明:该方法可有效光顺五轴数控加工刀轨,改善机床加速度情况,提高机床运动平稳性。      

基于EMD与LS-SVM的刀具磨损识别方法

针对刀具磨损声发射信号的非平稳特征和BP神经网络学习算法收敛速度慢、易陷入局部极小值等问题,提出了基于经验模态分解和最小二乘支持向量机的刀具磨损状态识别方法.首先对声发射信号进行经验模态分解,将其分解为若干个固有模态函数之和,然后分别对每一个固有模态函数进行自回归建模,最后提取每一个自回归模型的系数组成特征向量,特征向量被分为两组,一组用于对最小二乘支持向量机训练,另一组用于识别刀具磨损状态.试验结果表明:该方法能很好地识别刀具磨损状态,与BP神经网络相比具有更高的识别率.      

Modeling M(3000)F2 based on extreme learning machine

This paper presents an novel extreme learning machine (ELM)-based prediction model for the ionospheric propagation factor M(3000)F2 at Darwin station (12.4°S, 131.5°E; −44.5°dip) in Australia. The proposed ELM model is trained with hourly daily values of M(3000)F2 from the period 1998–2014 except 2001 and 2009. The hourly daily values of 2001 (high solar activity) and 2009 (low solar activity) are used for validating the prediction accuracy. The proposed ELM for modeling M(3000)F2 can achieve faster training process and similar testing accuracy compared with backward propagation neural network (BPNN). In addition, the performance of the ELM is verified by comparing the predicted values of M(3000)F2 with observed values and the international reference ionosphere (IRI −2016) model predicted values. Based on the error differences (the root mean square error (RMSE) and the M(3000)F2 percentage improvement values M(3000)F2_IMP(%)), the result demonstrates the effectiveness of the ELM model compared with the IRI-2016 model at hourly, daily, monthly, and yearly in high (2001) and low (2009) solar activity years. The ELM also shows good agreement with observations compared with the IRI during disturbed magnetic activity.