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 betwee...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.展开更多
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-err...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.展开更多
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 r...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.展开更多
In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response...In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.展开更多
文摘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.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0231)Jiangsu Provincial Key Research and Development Program(No.BE2021060)The authors thank the Key Projects of Scientific Research in Colleges and Universities of Anhui Provincial Department of Education(No.KJ2021A0367).
文摘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.
文摘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.
基金Project(20091102110021)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.
