Reserach On Cutting Parameters Optimization Based On Model Predictive Control

Model predictive control method is an optimal control algorithm, which based on computer optimization and extracted from the production practice. It is widely used in lots of industrial production processes with its stronger capability to handle constraints, stronger robustness. It also can be applied to multi-input-multi-output systems conveniently. Along with development of model predictive control theory increasingly, and its application research has become more and more hot. Also, its field of applications is expanded continually. In this paper, model predictive control strategy with both receding horizon and decreasing horizon formulations is developed to deeply research and discuss multi-pass cutting parameters, some research results were obtained, the details as follows:In this paper, in order to solve the problem of multi-pass cutting parameters optimization, firstly, turning force measurement experiments designed to obtain relevant experimental data, then use simulation to verify the validity of the experimental data, and then the multiple linear regression method is used to determine the index of all parameters in the formula of cutting force, cutting force models eventually be applied to the multi-pass cutting parameter optimization model, which based on model predictive model strategy, and whose objective functions are productivity and the production cost of unit removal volume and constraints are related technical index. This model belongs to multi-objective, multi-variable, nonlinear optimization problem, a tolerant lexicographic method is used to handle the model which is solved by interior point method, and then model predictive control strategy is utilized to obtain the optimal cutting parameters including cutting depth, cutting feed and spindle speed in the optimal process. In the simulation analysis, comparing the optimization results obtained by different strategies, the strategy proposed in this paper is proved that it not only have lower complexity for solving, but also can obtain the optimal combination of cutting parameters of higher productivity and lower production.