采用改进粒子群算法的铣削参数优化研究

Optimization of Milling Parameters Using Improved Particle Swarm Optimization Algorithm

为了提高数控铣削加工的生产效率,降低生产成本,同时改善生产工件的加工质量,根据最优化思想,建立以铣削加工参数为优化变量,以铣削力、机床主轴转速和加工面粗糙度等为约束条件,以最短加工时间和最低生产成本为目标的优化函数。在标准粒子群算法的基础之上,引入惩罚函数,将多约束优化问题转变为无约束优化问题,改善了求解过程的复杂性;同时,针对粒子群算法容易陷入局部最优的问题,将其与模拟退火算法结合,增强粒子的全局搜索能力,改善粒子的局部收敛性。通过仿真实例验证了改进粒子群算法的有效性和优越性,改善了工件的加工时间与生产成本。

In order to improve the production efficiency ofNC milling,reduce production costs,and improve the processing quality of production parts.According to the optimization idea,the milling parameters are optimized as the optimization variables;the milling force,the spindle speed of the machine tool and the roughness of the machined surface are the constraints;the shortest processing time and the lowest production cost are the optimization function.Based on the standard particle swarm algorithm,the penalty function is introduced to transform the multi-constraint optimization problem into an unconstrained optimization problem,which reduce the complexity of the solution process.At the same time,the particle swarm optimization algorithm is easy to fall into the local optimal problem.The solution is combines simulated annealing algorithm to enhance the global search capability of the particles.Through simulate practical operation,the processing time and production cost have been reduced,and the effectiveness and superiority of the improved particle swarm optimization algorithm are verified.