The usages of carbon-fiber reinforced polymer (CFRP) in aerospace, defense, and structural fields are increasing due to their excellent properties. However, the materials design, forming of material, machine tool and processing conditions are major tasks in manufacturing industries. Particularly, the micro feature making on macro-components using vertical machining center is a challenge nowadays. In this work, two different drill bits, such as high-speed steel (HSS) and solid carbide (SC) micro-drill, were used to make drilling on CFRP material. The performance of drills was evaluated by obtaining minimum delamination and stress in drilling by varying cutting velocity (CV), feed rate (FR), and air pressure (AP). Regression equations were formed according to the measured quality performance characteristics. The linear weighted method-based combined objective function algorithm and Genetic Algorithm was followed to multi-objective optimization. Besides, the most influencing factors were also identified and discussed using analysis of variance. The results explained that the SC micro-drill performance was better than HSS micro-drill. Also, the CV has the most eminent parameters followed by FR.

中文翻译：

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利用遗传算法优化碳纤维增强塑料钻孔工艺

碳纤维增强聚合物（CFRP）由于其优异的性能，在航空航天、国防和结构领域的应用正在增加。然而，材料设计、材料成型、机床和加工条件是制造业的主要任务。特别是使用立式加工中心在宏观部件上进行微观特征加工是当今的挑战。在这项工作中，两种不同的钻头，如高速钢 (HSS) 和整体硬质合金 (SC) 微型钻头，被用来在 CFRP 材料上进行钻孔。通过改变切削速度 (CV)、进给率 (FR) 和气压 (AP) 来获得最小的钻孔分层和应力，从而评估钻头的性能。根据测量的质量性能特征形成回归方程。采用基于线性加权法的组合目标函数算法和遗传算法进行多目标优化。此外，还使用方差分析确定和讨论了影响最大的因素。结果说明SC微钻性能优于HSS微钻。此外，CV 具有最突出的参数，其次是 FR。