计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 175-183.doi: 10.11896/jsjkx.210100101
胡德凤, 张晨曦, 汪世涛, 赵钦佩, 李江峰
HU De-feng, ZHANG Chen-xi, WANG Shi-tao, ZHAO Qin-pei, LI Jiang-feng
摘要： 刀具磨损的智能监测是影响现代机械加工业智能化发展进程的重要因素。在机械加工过程中,大多数机床通过使用传感器采集信号,从而建立刀具磨损与传感器信号之间的关系,在不中断加工过程的情况下中实现刀具的磨损预测,根据是否达到磨损阈值来判断是否自动换刀或报警以实现在线智能监控。能否从传感器信号中提取有效的特征信息,并且建立一个快速响应且精确的预测模型是一个亟待解决的问题。因此,针对上述问题,提出了一种基于深度信号处理和堆叠残差GRU的刀具磨损预测模型。在信号处理方面设计了BiGRU-Self Attention(BGSA)模块,利用双向门控循环单元和内部注意力机制来获取动态时序特征,反映出不同特征的影响程度,以提高建模效率。同时,提出堆叠残差GRU(Stacked-ResGRU)模型实现对刀具实时磨损值的预测,通过残差结构优化了网络结构,加快了模型的收敛速度。通过使用公开的数据集,对铣削过程中刀具的磨损状态预测进行实验研究,实验结果验证了该方法的可行性与有效性。通过对实验结果的对比分析可知,所提模型能够有效地表征刀具磨损程度,并大大减小了预测误差,取得了良好的预测效果。
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