针对三维椭圆振动切削在精密光学元件加工过程中颤振会降低光学元件加工质量的问题, 对3D-EVC过程中的颤振进行研究。抑制颤振可看作切削参数的优化问题, 即利用智能优化算法寻优获得最佳切削参数以满足颤振抑制参数的要求。为此提出一种改进的人工蜂群算法, 应用于三维椭圆振动切削加工过程中以进行切削参数的优化, 从而抑制切削过程中的颤振现象。并且将蜜蜂进化策略同粒子群算法中算子移动的“认知”和“社会”两部分进行结合, 通过引入惯性权重来提高全局最优解的搜索能力及收敛性能; 并且建立了刀触点振动幅值、切削深度、进给量及主轴转速之间的数学模型, 最后应用改进人工蜂群(PABC)算法对理论模型进行参数优化; 实验结果显示, 通过参数优化后, 刀尖点振动幅值显著下降, 且工件表面粗糙度降低了70%。验证了利用该方法对3D-EVC过程中颤振现象进行抑制的有效性。

Aiming at the problem that the chatter of the Three-Dimensional Elliptical Vibration Cutting (3D-EVC) will reduce the quality of optical components . Therefore, the chatter phenomenon in 3D-EVC process need be suppressed. The chatter problem can be regarded as an optimization problem of cutting parameters. In other words, an intelligent optimization algorithm must be used to obtain the optimal cutting parameters to meet the requirements of the chatter suppression parameters. Therefore, a Particle-Artificial Bee Colony (PABC) algorithm is proposed to optimize the cutting parameters of 3D-EVC to suppress the chatter phenomenon. In addition, a bee evolutionary strategy is combined with the “cognition” and “social” operator movements in the particle swarm optimization algorithm. Furthermore, introducing the inertial weight can improve the search ability and convergence performance of the global optimal solution. A mathematical model of the vibration amplitude in the tool tip point is established, and the cutting depth, feed rate, and spindle speed are utilized for the 3D-EVC process. Finally, parameter optimization is implemented by the proposed method. Experimental results show that the vibration amplitude of the tool tip point decreased significantly, whereas the surface roughness of the workpiece was reduced by 70% after the PABC parameters were optimized. These results prove that the proposed method is effective at suppressing chatter in the 3D-EVC process.