Micro-forging(MF)is a novel surface modification technology which is capable of smoothening and strengthening the workpiece surface simultaneously.Based on analysis of the mechanism and energy conversion of micro-forg...Micro-forging(MF)is a novel surface modification technology which is capable of smoothening and strengthening the workpiece surface simultaneously.Based on analysis of the mechanism and energy conversion of micro-forging process,an electromagnetically driven microforging system is developed.To further grasp the kinetic characteristic of the equipment,a simulation model is established and its accuracy is verified.With the help of simulation and experimental results,we propose an input voltage optimization method,which drives the micro-forging head moving in a uniform and stable way.In this study,the influence of MF on surface integrity of Ti-6 Al-4 V(TC4)is firstly reported.Experimental results show that MF treatment reduces surface roughness(Ra)and increases micro-hardness by 48%and 11.8%at most,respectively.Besides,a compressive stress layer with an amplitude of-1000 MPa and a depth of 0.8 mm is observed.This study analyzes the performance and reveals the potential of micro-forging technology,which lays a solid foundation for expanding its application in TC4 surface modification.展开更多
Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significa...Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.展开更多
基金the National Major Science and Technology Projects of China(No.2018ZX04005001-002)State Administration for Science,Technology and Industry for National Defense of China(No.DE0904)Shanghai Academy of Spaceflight Technology of China(No.SAST2018-055)。
文摘Micro-forging(MF)is a novel surface modification technology which is capable of smoothening and strengthening the workpiece surface simultaneously.Based on analysis of the mechanism and energy conversion of micro-forging process,an electromagnetically driven microforging system is developed.To further grasp the kinetic characteristic of the equipment,a simulation model is established and its accuracy is verified.With the help of simulation and experimental results,we propose an input voltage optimization method,which drives the micro-forging head moving in a uniform and stable way.In this study,the influence of MF on surface integrity of Ti-6 Al-4 V(TC4)is firstly reported.Experimental results show that MF treatment reduces surface roughness(Ra)and increases micro-hardness by 48%and 11.8%at most,respectively.Besides,a compressive stress layer with an amplitude of-1000 MPa and a depth of 0.8 mm is observed.This study analyzes the performance and reveals the potential of micro-forging technology,which lays a solid foundation for expanding its application in TC4 surface modification.
文摘Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.
