In machining processes, errors of rough in dimension, shape and location lead to changes in processing quantity, and the material of a workpiece may not be uniform. For these reasons, cutting force changes in machinin...In machining processes, errors of rough in dimension, shape and location lead to changes in processing quantity, and the material of a workpiece may not be uniform. For these reasons, cutting force changes in machining, making the machining system deformable. Consequently errors in workpieces may occur. This is called the error reflection phenomenon. Generally, such errors can be reduced through repeated processing while using appropriate processing quantity in each processing based on operator's experience.According to the theory of error reflection, the error reflection coefficient indicates the extent to which errors of rough influence errors of workpieces. It is related to several factors such as machining condition, hardness of the workpiece, etc. This non-linear relation cannot be worked out using any formula. RBF neural network can approximate a non-linear function within any precision and be trained fast. In this paper, non-linear mapping ability of a fuzzy-neural network is utilized to approximate the non-linear relation. After training of the network with swatch collection obtained in experiments, an appropriate output can be obtained when an input is given. In this way, one can get the required number of processing and the processing quantity each time from the machining condition. Angular rigidity of a machining system,hardness of workpiece, etc., can be input in a form of fuzzy values. Feasibility in solving error reflection and optimizing machining parameters with a RBF neural network is verified by a simulation test with MATLAB.展开更多
To obtain flow behavior and workability of 7055 aluminium alloy during hot deformation,hot compression tests at different temperatures and strain rates are conducted.True stress?strain curves of 7055 aluminium alloy u...To obtain flow behavior and workability of 7055 aluminium alloy during hot deformation,hot compression tests at different temperatures and strain rates are conducted.True stress?strain curves of 7055 aluminium alloy under different conditions are obtained and the flow stress increases with ascending strain rate and descending temperature.For Arrhenius constitutive equation,each material parameter is set as a constant,which will bring forth large error for predicting flow behavior.In this work,material parameters are fitted as a function of temperature or strain rate based on experimental results and a modified constitutive equation is established for more accurate prediction of flow behavior of 7055 aluminium alloy.The effects of temperature and strain rate on power dissipation and instability are analyzed to establish a processing map of 7055 aluminium alloy.The dominant deformation mechanism for microstructure evolution at different deformation conditions can be determined and high efficiency of power dissipation may be achieved from power dissipation map.Meanwhile,proper processing parameters to avoid flow instability can be easily acquired in instability map.According to the processing map,optimized processing parameters of 7055 aluminium alloy are temperature of 673?723 K and strain rate of 0.01?0.4 s^?1,during which its efficiency of power dissipation is over 30%.Finite element method(FEM)is used to obtain optimized parameter in hot rolling process on the basis of processing map.展开更多
In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems a...In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems above, a self-adaptive fuzzy controller with formulary rule （SAFCFR） is presented based on the dual feedbacks composed by gap electric signal and discharge-ratio statistics. To ensure the properties of self-optimizing and fast stabilization, the formulary rule was designed with a tuning factor. In addition, the fast-convergence algorithms were introduced to adjust control target center and output scale factor. In this way, the normal discharge ratio can tend to the highest value during micro-EDM process. Experimental results show that the proposed algorithms are effective in improving the servo-control performance. According to the drilling-micro-EDM experiments, the machining efficiency is improved by 20% through applying SAFCFR. Moreover, SAFCFR is a prompt way to optimize parameters of discharge-gap servo control.展开更多
文摘In machining processes, errors of rough in dimension, shape and location lead to changes in processing quantity, and the material of a workpiece may not be uniform. For these reasons, cutting force changes in machining, making the machining system deformable. Consequently errors in workpieces may occur. This is called the error reflection phenomenon. Generally, such errors can be reduced through repeated processing while using appropriate processing quantity in each processing based on operator's experience.According to the theory of error reflection, the error reflection coefficient indicates the extent to which errors of rough influence errors of workpieces. It is related to several factors such as machining condition, hardness of the workpiece, etc. This non-linear relation cannot be worked out using any formula. RBF neural network can approximate a non-linear function within any precision and be trained fast. In this paper, non-linear mapping ability of a fuzzy-neural network is utilized to approximate the non-linear relation. After training of the network with swatch collection obtained in experiments, an appropriate output can be obtained when an input is given. In this way, one can get the required number of processing and the processing quantity each time from the machining condition. Angular rigidity of a machining system,hardness of workpiece, etc., can be input in a form of fuzzy values. Feasibility in solving error reflection and optimizing machining parameters with a RBF neural network is verified by a simulation test with MATLAB.
基金Project(51175257)supported by National Natural Science Foundation of ChinaProject(BK20170785)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(BE2016179)supported by Science and Technology Planning Project of Jiangsu Province,ChinaProject(Kfkt2017-08)supported by Open Research Fund of State Key Laboratory for High Performance Complex Manufacturing,Central South University,China
文摘To obtain flow behavior and workability of 7055 aluminium alloy during hot deformation,hot compression tests at different temperatures and strain rates are conducted.True stress?strain curves of 7055 aluminium alloy under different conditions are obtained and the flow stress increases with ascending strain rate and descending temperature.For Arrhenius constitutive equation,each material parameter is set as a constant,which will bring forth large error for predicting flow behavior.In this work,material parameters are fitted as a function of temperature or strain rate based on experimental results and a modified constitutive equation is established for more accurate prediction of flow behavior of 7055 aluminium alloy.The effects of temperature and strain rate on power dissipation and instability are analyzed to establish a processing map of 7055 aluminium alloy.The dominant deformation mechanism for microstructure evolution at different deformation conditions can be determined and high efficiency of power dissipation may be achieved from power dissipation map.Meanwhile,proper processing parameters to avoid flow instability can be easily acquired in instability map.According to the processing map,optimized processing parameters of 7055 aluminium alloy are temperature of 673?723 K and strain rate of 0.01?0.4 s^?1,during which its efficiency of power dissipation is over 30%.Finite element method(FEM)is used to obtain optimized parameter in hot rolling process on the basis of processing map.
基金Supported by the National High Technology Research and Development Program of China （No. 2007AA04Z346） , the National Natural Science Foundation of China （ No. 50905094） and China Postdoctoral Science Foundation （ No. 20080440378, 200902097）.
文摘In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems above, a self-adaptive fuzzy controller with formulary rule （SAFCFR） is presented based on the dual feedbacks composed by gap electric signal and discharge-ratio statistics. To ensure the properties of self-optimizing and fast stabilization, the formulary rule was designed with a tuning factor. In addition, the fast-convergence algorithms were introduced to adjust control target center and output scale factor. In this way, the normal discharge ratio can tend to the highest value during micro-EDM process. Experimental results show that the proposed algorithms are effective in improving the servo-control performance. According to the drilling-micro-EDM experiments, the machining efficiency is improved by 20% through applying SAFCFR. Moreover, SAFCFR is a prompt way to optimize parameters of discharge-gap servo control.
