Abstract
Machining distortion is a recurring problem in the machining of monolithic aircraft parts. This paper aims to study the machining distortion minimization of monolithic aircraft parts. Firstly, the energy principle of machining distortion was analyzed. Then, a rapid prediction model of the final part distortion for beam parts was proposed based on the equivalent stress, and the initial bending strain energy contained in the final part was used to characterize the bending distortion risk of the final part. Numerical simulation and milling experiments verified the effectiveness of the proposed prediction model. The relative error between the experimental and calculated results does not exceed 26.5%. Finally, the influence of initial residual stress fluctuation, part geometry and the part location on part distortion was analyzed from the energy point of view. The obtained results indicated that the expected final part distortion can be minimized by adjusting these three factors.
Highlights
Monolithic aircraft parts have been widely used in the aerospace industry, such as wing flange, fuselage frame, web, etc
The machining distortion was analyzed based on the energy principle
A rapid prediction model of machining distortion based on equivalent initial residual stress was proposed
Summary
Monolithic aircraft parts have been widely used in the aerospace industry, such as wing flange, fuselage frame, web, etc. These large aircraft components are often large in size, complex in structure and thin in wall thickness, so they are prone to distortion during processing [1]. The release and redistribution of initial residual stress in the blank is the main factor leading to machining distortion [3]. The relationship between initial residual stress and machining distortion has been extensively studied by means of theoretical analysis, finite element method (FEM) simulation and experimental verification. Gao et al [4] studied the relationship between the distortion and initial residual stress by establishing a semi-analytical model. Yang et al [5]
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