The cutting tool process became one of the uncommon thermal energy-based manufacturing methods used in aerospace and different electronics industries to create complex shapes on different metals and their alloys. This paper presents a genetic algorithm for optimizing wear rate and kerf width mostly during cutting tools of aluminum 6351 CO2. The experiments were based on the design of Box Behnken, which took into account three laser specifications for cutting process. Control of laser beams, cutting speeds and gas pressure. By reducing the surface roughness and kerf width, the optimum parameters for the laser cutting were determined. Our test results reveal that in solving optimization problems, the suggested genetic algorithm is efficient and efficient and can be incorporated into the intelligent production environment.

切削工具工艺成为航空航天和不同电子行业中用于在不同金属及其合金上创建复杂形状的不常见的基于热能的制造方法之一。本文提出了一种遗传算法，用于优化铝 6351 CO2 切削刀具的磨损率和切口宽度。实验基于 Box Behnken 的设计，该设计考虑了三种激光切割工艺规格。控制激光束、切割速度和气压。通过降低表面粗糙度和切口宽度，确定了激光切割的最佳参数。我们的测试结果表明，在解决优化问题时，所提出的遗传算法高效且高效，可以融入智能生产环境。