A novel deflection control method for the asymmetric thin-walled component by optimizing the feed rate of the finishing process

Zhongxi Zhang, Longhao Wang, Shuaiqin Wang,Dinghua Zhang, Aituan Jiang


引用 0|浏览0
The complicated thin-walled components with asymmetric structure are extensively used in aerospace fields. The material of these parts is hard-to-machining (such as titanium alloy and superalloy) and the machining-induced residual stress (MIRS) is inevitable in each cutting process. The component is deformed easily after the MIRS is rebalanced, which has become one of the most important challenges for the manufacturing of these parts. To overcome this problem, a deflection control method for the asymmetric thin-walled component by optimizing the machining parameters of the finishing process is proposed, which is aimed at adjusting the distribution of MIRS and making the MIRS tends to be self-balanced. Firstly, the deflection of two typical thin-walled components, the thin-walled plate and the circular section plate, that caused by the symmetrically distributed MIRS is discussed in detail. The influence of the component structure on the deflection is revealed. Subsequently, the component is divided into different sub-regions and the optimization algorithm, includes the objective function and constraint, and is established to adjust the feed rate of each sub-region. To achieve the optimization, the mapping relationship between the deflection and the feed rate is established by combining the machining experiments and finite element method. And then, the method of adjusting the distribution of the MIRS based on the mapping relationship is presented, using which the component is divided into several sub-regions and the feed rate of each sub-region is optimized. Finally, two group machining experiments on the complex thin-walled blade are carried out. Experimental results illustrate that the proposed method can reduce the machining deflection obviously.
Thin-walled component,Asymmetric structure,Machining deflection,Residual stress,Feed rate optimization
AI 理解论文
Chat Paper