Abstract
Stability is the prerequisite of a milling operation, and it seriously depends on machining parameters and machine tool dynamics. Considering that the tool information, including the tool clamping length, feeding direction, and spatial position, has significant effects on machine tool dynamics, this paper presents an efficient method to predict the tool information dependent-milling stability. A generalized regression neural network (GRNN) is established to predict the limiting axial cutting depth, where the machining parameters and tool information are taken as input variables. Moreover, an optimization model is proposed based on the machining parameters and tool information to maximize the material removal rate (MRR), where the GRNN model is taken as the stability constraint. A particle swarm optimization (PSO) algorithm is introduced to solve the optimization model and provide an optimal configuration of the machining parameters and tool information. A case study has been developed to train a GRNN model and establish an optimization model of a real machine tool. Then, effects of the tool information on milling stability were discussed, and an origin-symmetric phenomenon was observed as the feeding direction varied. The accuracy of the solved optimal process parameters corresponding to the maximum MRR was validated through a milling test.
Highlights
Regenerative chatter in the milling process is a kind of self-induced vibration caused by time-delayed displacement feedback in the tool-workpiece system
Typical combinations of tool information including clamping length, feeding direction, and spatial position coordinates are determined by orthogonal experiment method to measure the tool point frequency response functions (FRFs) through impact testing; these tool point FRFs are used to calculate the limiting axial cutting depth under different combinations of machining parameters; on the basis, a generalized regression neural network (GRNN) model whose inputs are the machining parameters and tool information can be trained to predict the ap lim ; an optimization model for improving the machining efficiency is established by taking the GRNN model to represent the chatter stability constraint, and it can be solved to obtain the optimal combination of machining parameters and tool information
The ing speed and approximation ability compared with the backpropagation neural network dominant mode number is uncertain when the tool information changed, adding (BPNN) and radialnumerous basis function neural network (RBFNN), it is first adopted in this redifficulties in predicting the tool information-dependent modal parameters for search to establish recognizing the mathematical between the information the tool mapping point FRFsrelationship and further performing the tool milling stability analysis
Summary
Regenerative chatter in the milling process is a kind of self-induced vibration caused by time-delayed displacement feedback in the tool-workpiece system. Typical combinations of tool information including clamping length, feeding direction, and spatial position coordinates are determined by orthogonal experiment method to measure the tool point FRFs through impact testing; these tool point FRFs are used to calculate the limiting axial cutting depth (ap lim ) under different combinations of machining parameters; on the basis, a generalized regression neural network (GRNN) model whose inputs are the machining parameters and tool information can be trained to predict the ap lim ; an optimization model for improving the machining efficiency is established by taking the GRNN model to represent the chatter stability constraint, and it can be solved to obtain the optimal combination of machining parameters and tool information.
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