基于遗传模拟退火算法的铣削用量优化

Selection of Optimal Machining Parameters for Milling Operations Using Genetic Simulated Annealing Algorithm

由于传统的切削用量选择方法的局限性,数控机床的功率不能被充分发挥,这使得数控加工这种高费用的加工手段变得更加昂贵。通过考虑机床、工件和刀具的实际约束来建立加工时间和加工成本的铣削用量数学模型,采用遗传模拟退火算法对铣削用量进行优化,实例表明遗传模拟退火算法优化铣削用量比采用遗传算法优化更迅速、更有效。

Numerical control machine power doesn＇t fully utilized because the traditional approach for selecting machining parameters is conservative. Hence, this produces higher cost. This paper proposes a optimization technique based on genetic simulated annealing algorithm（GSA） for determination of the cutting parameters in milling operations. In fact, some cutting constraints should be considered on equipment, work piece and tool. Mathematical model on machining time and cost were established. From the given example, It can be seen that GSA is more effective for optimizing the milling parameters than GA.