In-process adaptive milling for large-scale assembly interfaces of a vertical tail driven by real-time vibration data

Abstract

Assembly interfaces, the joint surfaces between the vertical tail and rear fuselage of a large aircraft, are thin-wall components. Their machining quality are seriously restricted by the machining vibration. To address this problem, an in-process adaptive milling method is proposed for the large-scale assembly interface driven by real-time machining vibration data. Within this context, the milling operation is first divided into several process steps, and the machining vibration data in each process step is separated into some data segments. Second, based on the real-time machining vibration data in each data segment, a finite-element-unit-force approach and an optimized space–time domain method are adopted to estimate the time-varying in-operation frequency response functions of the assembly interface. These FRFs are in turn employed to calculate stability lobe diagrams. Thus, the three-dimensional stability lobe diagram considering material removal is acquired via interpolation of all stability lobe diagrams. Third, to restrain milling chatter and resonance, the cutting parameters for next process step, e.g., spindle speed and axial cutting depth, are optimized by genetic algorithm. Finally, the proposed method is validated by a milling test of the assembly interface on a vertical tail, and the experimental results demonstrate that the proposed method can improve the machining quality and efficiency of the assembly interface, i.e., the surface roughness reduced from 3.2 μm to 1.6 μm and the machining efficiency improved by 33%.