An adaptive control method of supporting force for fixtures in multi-process milling of thin-walled ring parts

In the milling of thin-walled ring parts (TWRPs), supporting fixtures play a critical role in suppressing machining-induced deflection and improving machining quality. The supporting force exerted by the support head directly influences the effectiveness of deflection control. However, in current practice, the supporting force is typically determined based on engineering experience, which often leads to inconsistent and suboptimal suppression of deflection. To address this issue, this paper proposes an adaptive supporting-force control method for the multi-process milling of TWRPs. First, a predictive equation for the optimal supporting force is established through an analysis of the support characteristics of a curved support head. Based on this model, a supporting-force control strategy that integrates real-time force regulation with inter-process iterative learning is developed, along with a continuous multi-process deflection simulation method. In addition, a force-adjustable supporting fixture and its pneumatic control system are designed to implement the proposed approach. Experimental results show that the proposed control method, when applied without iterative learning, can reduce the average deflection in the supported region by more than 80%. By further incorporating the iterative learning mechanism to update the supporting-force gain between successive machining processes, the average deflection reduction can be improved to 93%. These results demonstrate the effectiveness of the proposed method in suppressing machining deflection in thin-walled ring parts.