Improved Artificial Bee Colony Algorithm And Application In Thermal Deformation Of Electric Spindle

With the rapid development of intelligent algorithm,artificial bee colony algorithm shows a strong ability to find the best.Due to the increasing complexity of the optimization problem,the Artificial Bee Colony Algorithm has more and more defects such as easily falling into local optimum,slow convergence speed of the algorithm in the later stage of search,and poor ability to adapt to dynamic environment.Therefore,it is very important to improve the artificial bee colony algorithm to improve the optimization performance and wide application.On the other hand,thermal deformation is one of the key influencing factors of the machining accuracy of machine tool,while the coefficient of thermal expansion is the main parameter affecting thermal deformation.The coefficient of thermal expansion is affected by a variety of parameters and has a strong nonlinear relationship.At present,coefficient of thermal expansion is mostly determined by experience or experiment,which affects the calculation accuracy of thermal deformation.Therefore,how to correctly determine the coefficient of thermal expansion and accurately calculate thermal deformation plays an important role in improving the machining accuracy of machine tool.In view of the above problems,this paper conducted an improved study on the optimization performance of the bee colony algorithm and applied it in the model of thermal deformation.The specific content is as follows:1.The improvement of traditional artificial bee colony algorithm is studied.On the one hand,initial population establishment mechanism based on the reversal learning method is proposed to increase the global optimum value,so as to avoid the algorithm falling into local optimal value.The local optimal value is introduced into the neighborhood search formula to enhance the search ability near the global optimal value and speed up the global convergence.On the other hand,a dynamic optimization method rely on case-based reasoning technology is proposed.By comparing with historical cases in case library,search direction is adjusted in real time,so that the algorithm can better adapt to changes of environmental factors and strengthen dynamic optimization ability.Finally,10 standard test functions are used for simulation experiments.The simulation results show that the improved bee colony algorithm improves the optimization accuracy by about 2 times compared with the standard bee colony algorithm.The number of iterations reaching convergence accuracy is reduced by 10%-16%,and the dynamic optimization ability is strengthened.2.The application of improved bee colony algorithm is studied.Firstly,this paper analyzed the practical structure of motorized spindle and the motorized spindle thermal deformation mechanism and applied the improved artificial bee colony algorithm to thermal deformation calculation,combined with FEA software,proposed the coefficient of thermal expansion optimization based on improving artificial bee colony algorithm and the thermal deformation model based on improving artificial bee colony algorithm.3.Experimental research.To prove the effectiveness of the method proposed in this paper,the thermal deformation of the electric spindle at different speeds was studied which was used the performance test platform of the motorized spindle system of 100MD60Y4.Experimental results show that take the speed of 2000 rad/min as an example,based on the experience of the thermal deformation and thermal expansion coefficient of actual measured thermal deformation in the error of the X,Y,Z three directions were 0.17?m,0.47?m,1.48?m,based on the optimization of the thermal deformation and thermal expansion coefficient of actual measured thermal deformation in the error of the X,Y,Z three directions were 0.02?m,0.13?m,0.56?m.The method proposed in this paper can improve the calculation accuracy of thermal deformation.