实时振动数据驱动的薄壁件平铣工艺参数自适应优化

Real-time Machining Vibration Data Driven Milling Process Parameters Adaptive Optimization

为减小加工振动对薄壁件平铣(端面盘铣)加工质量及效率的影响,提出一种实时铣削振动数据驱动的平铣工艺参数自适应优化方法。首先根据再生效应原理建立薄壁件平铣颤振稳定性模型。其次将薄壁件平铣过程中前一个工步内的实测振动数据分为若干段,以此模拟其材料去除过程,对各段铣削振动数据进行分析,由有限元单位力法和优化STD法分别识别出薄壁件刚度和各材料去除阶段模态频率及阻尼比,并由此导出薄壁件单模态频响函数,将其代入颤振稳定性模型求解稳定域叶瓣图并做插值处理后即可确定包含材料去除信息的薄壁件三维颤振稳定域叶瓣图。基于此,以避免铣削颤振、共振和满足机床性能要求为约束条件,以材料去除率最大为目标,利用遗传算法计算薄壁件下一个工步较优的工艺参数,如此循环进行,直到完成薄壁件加工。最后,通过某型飞机垂尾薄壁装配界面平铣试验验证该方法的可行性和有效性。由试验结果可看出,采用优化后的加工工艺参数,能使薄壁装配界面粗加工过程表面粗糙度从Ra 3.2提升为Ra 1.6,加工效率提高33%。

In order to decrease the influence of machining vibration to the finishing quality and efficiency of the thin-walled parts, this paper proposed a real-time machining vibration data driven milling process parameters adaptive optimization method. Firstly, the chatter stability model is constructed according to the regeneration principle. Next, dividing the measured vibration data of one step into several segments to simulate the material remove process. The stiffness and modal parameters of thin-walled parts is calculated by finite element unit force method and optimized STD method to derive the frequency response function of thin-walled parts, which is used to calculate the 3 D stability lobe diagram. Then, taking the maximum material removal rate as the goal, the genetic algorithm is used to calculate the optimized process parameters of next process step considering to avoid the milling chatter and resonance. Repeat the above cycle again until the thin-walled parts milling process is completed. Finally, the feasibility and effectiveness of the method are verified by the assembly interface of aircraft cutting experiment. The experiment results proves that the method can not only shorten the assembly interface milling process time by 33%, but also improve the surface roughness from Ra 3.2 to Ra 1.6.