Principal Face-based Recognition Approach for Machining Features of Aircraft Integral Panels

Feature recognition aims at extracting manufacturing features with geometrical information from solid model and is considered to be an efficient way of changing the interactive NC machining programming mode.Existing recognition methods have some disadvantages in practical applications.They can essentially handle prismatic components with regular shapes and are difficult to recognize the intersecting features and curved surfaces.Besides,the robustness of them is not strong enough.A new feature recognition approach is proposed based on the analysis of aircraft integral panels＇ geometry and machining characteristics.In this approach,the aircraft integral panel is divided into a number of local machining domains.The machining domains are extracted and recognized first by finding the principal face of machining domain and extracting the sides around the principal face.Then the machining domains are divided into various features in terms of the face type.The main sections of the proposed method are presented including the definition,classification and structure of machining domain,the relationship between machining domain and principal face loop,the rules of machining domains recognition,and the algorithm of machining feature recognition.In addition,a robotic feature recognition module is developed for aircraft integral panels and tested with several panels.Test results show that the strategy presented is robust and valid.Features extracted can be post processed and linked to various downstream applications.The approach is able to solve the difficulties in recognizing the aircraft integral panel＇s features and automatic obtaining the machining zone in NC programming,and can be used to further develop the automatic programming of NC machining.

RESEARCH ON SONIC FATIGUE CALCULATION AND TEST FOR AIRCRAFT PANEL

The sonic fatigue life of the aluminium rectangular panel was calculated using the concise method[1], and the sonic fatigue test was conducted on progressive wave tube (PWT) test facility. A comparison was made between the results of calculation and test, and it shows reasonable agreement between these two results.

Process-based deep learning model:3D prediction method for shot peen forming of an aircraft panel

Shot peen-forming is a more precise method of forming aircraft panels than conventional methods.The traditional method of acquiring the process parameters relies mainly on prior theoretical knowledge and trial-and-error.Despite the finite element method’s ability to replace some experimentation,it still cannot realize the design of shot peen forming processes parameters of an aircraft panel based on a known contour.This study uses an innovative model-based deep learning approach to predict aircraft panel deformation and active design the shot peening parameters.The prediction time is less than 1 second,resulting in a significant reduction in computational time.The shot peen forming process parameters and the geometric structure characteristics of the aircraft panel are divided into independent channels to establish a high-dimensional feature map,which are used to train the deep learning model.The forming contours of the 2024-T351 high-strength aluminum alloy panel are predicted under different shot peening processes.In addition,the process parameters are designed according to the known contour of the forming process.To verify the precision of the proposed method,the designed shot peen forming process is used to manufacture a single curvature aircraft panel with a curvature radius of 3500 mm.There is good agreement between the forming contour and the theoretical design contour.The maximum deformation error is less than 1 mm and its mean error is 7.8%.The mean curvature radius error is 5.668%.The proposed method provides a new and practical reference to the precise design of the shot peen-forming process.

Slicing Recognition of Aircraft Integral Panel Generalized Pocket

To automatically obtain a machining area in numerical control （NC） programming, a data model of generalized pocket is established by analyzing aircraft integral panel characteristics, and a feature recognition approach is proposed. First, by reference to the practical slice-machining process of an aircraft integral panel, both the part and the blank are sliced in the Z-axis direction; hence a feature profile is created according to the slicing planes and the contours are formed by the intersection of the slicing planes with the part and its blank. Second, the auxiliary features of the generalized pocket are also determined based on the face type and the position, to correct the profile of the pocket. Finally, the generalized pocket feature relationship tree is constructed by matching the vertical relationships among the features. Machining feature information produced by using this method can be directly used to calculate the cutter path. The validity and practicability of the method is verified by NC programming for aircraft panels.