A Dynamic Method For Tool Life Evaluation In Nc Machining Based On Deep Learning

The abnormal status of cutting tools in NC machining will lead to unqualified or even scrapped parts,and unreasonable tool replacement will lead to waste of cost.The prediction of tool life can effectively avoid part quality problems caused by abnormal tool status and improve the tool utilization rate.However,the tool wear process is complicated,and the residual life of tool is difficult to be predicted accurately by the influence of working conditions.For the above questions,the problem of tool life prediction in NC machining is deeply studied in this thesis.The main work of this thesis is as follows:(1)Aiming at the problem that the high dimension of monitoring data is difficult to handle effectively,the feature extraction method of tool wear signals based on the time-frequency domain analysis and RBM is studied,and the related information in monitoring data is fully excavated.The dimension of tool wear related signals is effectively reduced by selection of sensitive feature and reduction by characteristic matrix,and the effective feature extraction of the signals is realized.(2)To solve the problem that the tool wear is difficult to obtain quickly and accurately,an online automatic measurement method of tool wear based on machine vision is proposed.The region of interest is located by tool feature lines accurately,and the original edge of tool is reconstructed by traversing the sliding window.The tool wear is calculated automatically after the tool wear area is extracted,which improves the accuracy and efficiency of direct measurement of milling wear.(3)In order to solve the problem that tool residual life is difficult to predict accurately in NC machining,a dynamic method of tool life prediction based on online learning is proposed.Based on the modified long-short term memory network model,the tool life prediction model is established.Model training is carried out with tool wear and residual life as sample labels,and the online learning module is integrated for the online update of model based on the measured data,thus realizing the prediction of tool life under variable working conditions.(4)Based on the above research,the tool life dynamic evaluation system was developed on LabVIEW,and experiments were designed to verify the method proposed in this thesis.