Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms  

作　　者：Sonali S.Patil Sujit S.Pardeshi Abhishek D.Patange 

机构地区：[1]Department of Mechanical Engineering,College of Engineering Pune,Pune,411005,India

出　　处：《Computer Modeling in Engineering & Sciences》2023年第7期177-199,共23页工程与科学中的计算机建模（英文）

摘　　要：In-process damage to a cutting tool degrades the surface􀀀nish of the job shaped by machining and causes a signi􀀀cant􀀀nancial loss.This stimulates the need for Tool Condition Monitoring(TCM)to assist detection of failure before it extends to the worse phase.Machine Learning(ML)based TCM has been extensively explored in the last decade.However,most of the research is now directed toward Deep Learning(DL).The“Deep”formulation,hierarchical compositionality,distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform eciently in a high-noise environment of cross-domain machining.With this motivation,the design of dierent CNN(Convolutional Neural Network)architectures such as AlexNet,ResNet-50,LeNet-5,and VGG-16 is presented in this paper.Real-time spindle vibrations corresponding to healthy and various faulty con􀀀gurations of milling cutter were acquired.This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form,i.e.,spectrogram.The model is trained,tested,and validated considering dierent datasets and showcased promising results.

关 键 词：Milling tool inserts health monitoring vibration spectrograms deep learning convolutional neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]