基于卷积神经网络的车刀磨损研究

Research on Turning Tool Wear based on Convolutional Neural Network

车削加工过程中,刀具磨损是影响加工效率最重要的一个因素,在工件表面发生过度损坏之前,需要对刀具的磨损情况进行识别和及时更新,实现工件的高质量生产加工。提出了一种基于深度学习的刀具磨损状态识别方法,通过显微镜记录不同磨损阶段的车削刀具图像,并利用卷积神经网络提取输入图像不同磨损状态特征,为该模型选择合适的训练参数,实现切削刀具磨损的状态分类。实验表明,在对车刀的不同磨损状态进行分类时,准确率可达94.0%,可用于低成本识别车削加工过程中的刀具磨损状态。

In the process of turning processing,tool wear is one of the most important factors affecting machining efficiency,before excessive damage to the work piece surface,it is necessary to identify and update the wear of the tool in time to achieve high-quality production and processing of the work piece.In this paper,a tool wear state recognition method based on deep learning was proposed,which records the turning tool images at different wear stages through a microscope,and used the convolutional neural network to extract the different wear state features of the input images,and selected appropriate training parameters for the model to realize the state classification of cutting tool wear.Experiments showed that the accuracy rate could reach 94.0%when classifying different wear states of turning tools,which could be used to identify tool wear states in the turning process at low cost.