基于神经网络的高效强力复合铣床立柱优化设计

Optimizing Disc Milling Column of Efficient and Powerful Compound Milling Machine Based on Neural Network

针对整体叶盘高效强力复合数控铣床立柱刚性不足的问题,通过试切钛合金获取盘铣切削时的切削力,运用ABQUS有限元分析模块,计算立柱的动、静态特性,结合变量化分析技术,提取立柱的元结构和框架结构进行优化设计。以结构固有频率最高为优化目标,提出立柱结构的改进设计方案,并结合BP神经网络模型寻找最优设计变量。改进后的立柱结构方案与原型相比,固有频率明显提高。最后,应用该方法对整体叶盘高效强力复合数控铣床原理样机进行了分析,依据分析结果对原结构进行了改进,并进行了加工试验,结果表明,其动、静态特性有了较大的改善,验证了该方法的正确性和可行性。

In order to solve the problem of insufficient rigidity of the column of the disc milling column of efficient and powerful compound milling machine, the cutting force was obtained by cutting titanium alloy, the dynamic and static characteristics of the column were calculated by ABQUS finite element analysis module, and FEM is used to calculate the static and dynamic characteristics of the key structure of the column. Unit structure and frame structure are collected and optimized by variational analysis method. Several improved design schemes of the column structure are presented for the purpose of optimizing the structure’s natural frequency. BP neural network model is also put forward to find out the optimal design variable. Compared with the original one, the natural frequency of the optimized column structure increases observably. Finally, this method is applied to analyze the structure of principle prototype and the original column is optimized according to the computed results. Through machining experiments, the static and dynamic characteristics of the machine tool are improved greatly, and the correctness and feasibility of this theory are verified.