Research On Forecasting Model Of Machining Dimension

Forecasting of machining dimension is the necessary requisite in machining on-line quality control, the key technique in realizing forecasting compensatory control. So it's very essential to find out a new forecasting modeling technique with high accuracy and high speed, and which can be applied in on-the-spot application.This essay, starting from the dynamic feature of machining process, made a careful analysis of the application of several common forecasting modeling finish size. On the basis of it, three forecasting model of machining dimension applicable to different occasions were put forward.Due to GM(1,1) model's not responding well to machining dimension sequence random and lack of forecasting accuracy, and on the basis of grey model theory, the essay focused on modeling dimension and background value 's effect on improving GM(1,1) model forecasting precision . By introducing the parameter of background value of GM(1,1), the general expression formula of background value was given. Based on that, GM(1,1) optimization was raised. The background value and dimension parameter optimization model aimed at minimizing mean absolute forecasting error was established. By taking into account the optimization features, the optimization of modeling dimension and parameter of background value was realized by using Genetic Algorithms. The corresponding software was drawn up by using Matlab.To improve GM(l,l)'s accuracy, the essay studied error correction method. Through analyzing the dynamic character of GM(1,1) error sequence and the theory of time sequence combination forecasting modeling, a kind of GM(1,1)-AR combination forecasting model based on GM(1,1) and time sequence model, in which GM(1,1) was used to carry out dynamic forecasting with the recursive compensation by the grey numbers of identical dimensions, and in which AR model built up by left-line was used to forecast and on-line correct GM(1,1) error.The essay also studied nonlinear forecasting model based on neural networks . Because of neural networks not responding well to size sequence tendency item, a kind of GM(1,1)-ANN combination forecasting modeling based on neural networks and GM(1,1) was given. To make combination model application into full play, the essay carried out non-linear forecast and correction on GM(l,l)model error by using neural networks.Genetic algorithms was used to left-line optimizing weights of neural networks. Toenhance the converging speed of genetic algorithms, real number code, normal mutation operator, steady-state genetic algorithms were used to improve expectation selecting method and matching group changeability alternate strategy was showed. The corresponding software was drawn up by using BC.A batch of samples were processed continuously on NC turing machine tool. Then, application analysis was made on three modeling methods. Experiment result showed that, comparing with GM(1,1) optimizing model with standard GM(l,l)model, model error was reduced by over 30%.Comparing GM(1,1)-ANN and GM(1,1)-AR combination model with Standard GM(1,1), model error was reduced by over 50%. These showed, the three models all have high accuracy.On the condition that GM(1,1) on-line modeling high speed was maintained, by optimization and combination, three new on-line forecasting modeling were put forward. According to its corresponding modeling mechanism, GM(1,1) optimizing model could be applied to general machining dimension modeling and forecasting; the two combination forecasting models could be applied to machining dimension forecasting modeling with strong random disturbance, in which GM(1,1)-ANN was more suitable to the application requirement of quality control in complicated non-linear machining process. In addition, this essay's research result can serve as a reference to forecasting modeling in other fields.