用基因算法实现切削参数的现场实时优化

Using genetic algorithm to realize optimization of cutting parameter real time in workplace

为了使金属切削加工中，切削参数能实现实时优化保证产品质量和设备效率，提出采用基因算法。它是基于生物进化理论的优化算法，对问题进行全局的、并行的启发式探索优化，因而可以防止收敛于局部最优解，且搜索效率优于其它方法；适用于具有多参数、多约束条件和多目标的切削参数优化。基因算法结合现场实际工况的反馈信息实现了实时优化，在任一不同的生产条件下均能达到最优值。

In metal cutting process, the optimization of cutting parameters is the key factor to improve the quality of products and raise the efficiency of equipment. Genetic algorithm (GA) is based on the theory of biological evolution. For optimizing the object it can search parallelly in overall range and with heuristic method, thus it can avoid to obtain local optimum solution and the search efficiency is higher than other methods. GA is suitable for solving the problem of multi factor, multi constraint, multi object. According to the feedback information in cutting process GA realizes real time optimization and the cutting parameter can be optimized in any different production environment.