Numerical Simulation And Process Parameter Optimization Of Machinable Ceramics Based On Multi-algorithm Fusion

Machinable ceramics had been applied in more and more fields in recent years due to their many excellent properties.In turning process,the reasonable selection of cutting parameters is the decisive factor to achieve high quality and efficiency.Therefore,the selection of turning parameters of machinable ceramics have become a problem worthy of study.Roughness is an important characteristic to measure the surface quality of workpiece,which directly affects the performance of workpiece and is an important factor to inspect the cutting quality of workpiece.Cutting temperature is an important characteristic to characterize the cutting state in the cutting process.Because of the poor thermal conductivity of fluorophlogopite ceramics,the cutting temperature of fluorophlogopite ceramics is the main reason for the thermal gradient and thermal stress of workpieces and cutters,leading to tool breakage and local workpiece breakage.Therefore,reasonable parameters should be selected in the process of machining,which could not only reduce the cutting temperature and roughness,but also ensure the machining efficiency.At present,the research on machinable ceramics can not meet the requirements of numerical simulation of cutting temperature and roughness in multi-objective optimization process.The aim of this paper is realizing Multi objective optimization of spindle speed,feed amount and cutting depth by establishing the orthogonal model of cutting temperature and roughness,targeted at cutting temperature,roughness and cutting efficiency.The main research contents are as follows:(1)Fluorophlogopite single factor and orthogonal turning experiments were designed and carried out.Cutting temperature and roughness were collected and processed.(2)The mutated artificial fish swarm algorithm was used to optimize the BP neural network.Based on the existing experiments,the single factor of cutting temperature and roughness was predicted.The reliability of prediction has been proved by experiments.(3)The SA-IAFSA algorithm is obtained by using the low-temperature simulated annealing to optimize mutation fish swarm algorithm.Based on the prediction and experimental results,SA-IAFSA algorithm was used to solve the problem.The single factor models of cutting temperature and roughness were established.Then,based on the common characteristics of the single factor models,the orthogonal models of cutting temperature and roughness were established.SA-IAFSA algorithm had higher solving accuracy than mutation fish swarm algorithm by comparison of solving accuracy.The reliability of the orthogonal models were proved by experiments.(4)Taking the cutting temperature,roughness and efficiency of machinable ceramics as optimization objectives,the objective function was constructed based on parallel combination.Mutation fish swarm algorithm optimized by low temperature simulated annealing was applied to multi-objective optimization of spindle speed,feed amount and cutting depth.The optimal process parameters were proved by experiments and comparative discussions.The method of multi-objective optimization for the process parameters of machinable ceramics was established,which would provide reference for the selection of process parameters of machinable ceramics in the future.