【摘要】 刀具在加工过程中会受到材料的挤压、摩擦、冲击与腐蚀等因素影响,导致切削刃出现崩刃、磨损等现象.这些现象使得工件尺寸出现偏差,严重时甚至会对机床和人员带来伤害.有效的刀具剩余使用寿命预测可以提高加工效率,保证加工精度,降低加工成本,因此具有重要的研究价值.针对反映刀具磨损程度的趋势性特征自学习提取与刀具剩余使用寿命预测问题,提出了基于深度学习与混合趋势粒子滤波的刀具剩余使用寿命预测方法.使用刀具未发生磨损的信号特征训练降噪自编码器,然后将刀具各磨损阶段下的信号特征输入训练好的降噪自编码器中,提取其重构误差作为单调性特征,为了解决样本数量不足带来的过拟合的问题,对原始样本进行了加噪处理.考虑到传统粒子滤波算法进行刀具剩余使用寿命预测的过程中无法自适应调整状态方程,提出混合趋势粒子滤波算法来实现刀具剩余使用寿命预测.采集刀具全寿命周期的切削力信号并进行处理与分析,分析结果证明了所提方法能够有效实现反映刀具磨损的趋势性特征自提取,该特征提取方法可以减少人为因素的影响,降低训练成本,同时,相比于传统粒子滤波,混合趋势粒子滤波算法对刀具剩余使用寿命预测精度更加准确可靠.更多还原

【Abstract】 Tools are affected by extrusion,friction,impact,and corrosion during machining,and these result in chipping and wearing of tools. These can cause deviations in the workpiece and cause damage to machines and personnel. Effective prediction of tools’ remaining useful life has important research value,as it can greatly improve the quality of workpiece,guarantee processing accuracy,and reduce processing costs. To realize self-extraction of features for tool wear and predict a tools’ remaining useful life,a method based on deep learning and hybrid trend particle filtering is proposed in this study. A neural network was trained using cutting force signal of a normal tool without wear,and reconstruction error was extracted as a monotonic feature. To solve the problem of over-fitting caused by insufficient sample size,noise was added to the original sample. To overcome traditional particle filtering algorithms’ inability to adaptively adjust state equation during a tools’ remaining useful life prediction process,a hybrid trend particle filter algorithm is proposed to realize the tool life prediction. Cutting force signals of a tools’ life cycle are collected for analysis. The experimental results prove that the proposed method can effectively achieve trend feature self-extraction. Moreover,the method can also effectively reduce the influence of human factors and reduce training cost. Furthermore,compared with the traditional particle filter,the hybrid trend particle filter algorithm is more accurate and reliable in predicting tools’ remaining useful life.更多还原