基于深度信号处理和堆叠残差GRU的刀具磨损智能预测模型  

Intelligent Prediction Model of Tool Wear Based on Deep Signal Processing and Stacked-ResGRU

作　　者：胡德凤 张晨曦[1] 汪世涛 赵钦佩[1] 李江峰[1] HU De-feng;ZHANG Chen-xi;WANG Shi-tao;ZHAO Qin-pei;LI Jiang-feng(School of Software Engineering,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学软件学院,上海201804

出　　处：《计算机科学》2021年第6期175-183,共9页Computer Science

基　　金：上海市自然科学基金(20ZR1460500)。

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。Intelligent monitoring of tool wear is an important factor affecting the intelligent development of modern machinery industry.Most machining tools collect signals through sensors and establishes their relationship to obtain the tool wear prediction without interrupting the processing process.Automatic tool change or alarm will be carried out according to whether the wear threshold is reached to realize online intelligent monitoring.It is an urgent problem to extract effective feature information from sensor signals and build a model which can predict the tool wear quickly and accurately.Therefore,based on the above problems,this paper proposes a tool wear prediction method based on deep signal processing and Stacked-ResGRU.In signal processing,the BiGRU-Self Attention(BGSA)network combing bidirectional GRU and self-attention is designed to obtain dynamic temporal features and reflect the influence degree of different features,which can improve modeling efficiency.At the same time,the Stacked-ResGRU model is proposed to predict the real-time tool wear value,and the network structure is optimized by residual structure to accelerate the convergence speed of the model.Experimental studies for tool wear prediction in a dry milling operation are conducted to demonstrate the viability of this method.Through the experimental results and comparative analysis,the proposed method can effectively characterize the tool wear degree,greatly reduce the prediction errors,and achieve a precise prediction effect.

关 键 词：刀具磨损 信号处理 内部注意力 残差GRU 智能监控 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]