The mathematical model on the temperature of the waved-edge is constructedaccording to Jaeger's theory of moving solid and based on the used temperature model of the flatinsert. It is possible to forecast the mill...The mathematical model on the temperature of the waved-edge is constructedaccording to Jaeger's theory of moving solid and based on the used temperature model of the flatinsert. It is possible to forecast the milling temperature through programming. The comparableexperiments have been done between the two new three-dimension groove inserts (waved-edge insert,great edge insert) and flat fake insert. The theoretic forecast is in good agreement with theexperimental result. According to the cutting conditions, the boundary condition of finite elementanalysis on cutting temperature field is established, and the three-dimensional temperature fieldsof inserts with grooves are analyzed by FEM, so as to offer a reference basis for the design andoptimization of insert grooves.展开更多
In-process damage to a cutting tool degrades the surfacenish of the job shaped by machining and causes a signicantnancial loss.This stimulates the need for Tool Condition Monitoring(TCM)t...In-process damage to a cutting tool degrades the surfacenish of the job shaped by machining and causes a signicantnancial 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 congurations 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.展开更多
基金National Natural Science Foundation of China(No.50075021)
文摘The mathematical model on the temperature of the waved-edge is constructedaccording to Jaeger's theory of moving solid and based on the used temperature model of the flatinsert. It is possible to forecast the milling temperature through programming. The comparableexperiments have been done between the two new three-dimension groove inserts (waved-edge insert,great edge insert) and flat fake insert. The theoretic forecast is in good agreement with theexperimental result. According to the cutting conditions, the boundary condition of finite elementanalysis on cutting temperature field is established, and the three-dimensional temperature fieldsof inserts with grooves are analyzed by FEM, so as to offer a reference basis for the design andoptimization of insert grooves.
文摘In-process damage to a cutting tool degrades the surfacenish of the job shaped by machining and causes a signicantnancial 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 congurations 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.
