基于DRSN-BiLSTM模型的刀具磨损预测方法研究

doi:10.3901/JME.2024.24.066

摘要/Abstract

摘要： 数控机床刀具磨损的预测对于提高刀具加工的安全性和产品加工质量具有重要意义。随着传感技术进步，制造业中用于设备状态监测的传感器数据量呈爆炸式增长，这促使了以深度学习为中心的数据驱动方法在刀具磨损预测领域受到高度重视。然而，如何精准地识别和提取与刀具退化紧密相关的特征，进而充分利用这些信息以提高预测模型的性能，仍然是一个挑战。针对以上问题，研究了一种基于深度学习的刀具磨损预测方法，将多通道筛选机制应用到预测模型中，提出基于深度残差收缩-双向长短期记忆网络模型的刀具磨损预测方法。根据监测信号的波动程度选择与刀具退化高度相关的多个通道进行融合，利用卷积通道注意力机制融合多通道数据并高效挖掘各个通道的抽象特征信息，后建立双向长短期记忆回归模型提取特征与刀具磨损相关的时序信息对刀具磨损进行准确预测。通过试验验证了通道筛选机制的有效性与预测模型精度。

关键词: 刀具磨损, 多通道融合, 深度学习, 深度收缩残差网络, 双向长短时神经网络

Abstract: The prediction of tool wear in CNC machine tools is of great significance to improve the safety of tool processing and product processing quality. With advances in sensing technology, the amount of sensor data used for equipment condition monitoring in the manufacturing industry has exploded. This has led to a high priority for a deep learning-centric, data-driven approach in the field of tool wear prediction. However, it remains a challenge to accurately identify and extract features closely related to tool degradation and make the most of this information to improve the performance of predictive models. In order to solve the above problems, a tool wear prediction method based on deep learning was studied, and the multi-channel screening mechanism was applied to the prediction model, and a tool wear prediction method based on the deep residual shrinkage network-bidirectional long short term memory (DRSN-BiLSTM) model was proposed. According to the fluctuation degree of the monitoring signal, multiple channels related to the tool degradation height are selected for fusion, the convolutional channel attention mechanism is used to fuse the multi-channel data and efficiently mine the abstract feature information of each channel, and then the BiLSTM regression model is established to extract the time series information related to the tool wear to accurately predict the tool wear. Experiments verify the effectiveness of the channel screening mechanism and the accuracy of the prediction model.

Key words: tool wear, multi-channel fusion, deep learning, DRSN, BiLSTM

中图分类号:

TH17

史丽晨, 史炜椿, 王海涛, 李金阳, 刘亚雄. 基于DRSN-BiLSTM模型的刀具磨损预测方法研究[J]. 机械工程学报, 2024, 60(24): 66-74.

SHI Lichen, SHI Weichun, WANG Haitao, LI Jinyang, LIU Yaxiong. Tool Wear Prediction Based on DRSN-BiLSTM Model[J]. Journal of Mechanical Engineering, 2024, 60(24): 66-74.

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