Research On Detection Method Of Composite Material Thin-Walled Work Piece Morphology Based On Flexible Tooling

Carbon fiber composite materials with low density,high temperature,abrasion and other excellent properties are widely used in aviation,aerospace and other fields.The automatic detection technology for morphology of composite material thin-walled work piece is extremely important for the production of aircraft,so the automatic detection technology of thin-walled composite material work piece has important theoretical and practical significance.In the production field,there exist deviations between the deformed work pieces and the standard work pieces.Because of the tenacity of composite material products,proper adjustments can be applied to the work piece to judge if the product is qualified.Currently,the flexible tooling technology is developing rapidly with outstanding performance in application.This paper launches the research based on flexible tooling as detection platform for the automatic detection of composite material thin-walled work piece morphology.Depending on certain flexible support condition,this article first introduces the finite element analyze software ANSYS for composite material thin-wall parts deformation analysis and differential evolution algorithm.Then starting from the basic principle and design requirement of flexible tooling technology,with the composite material surface work piece supported by flexible tooling platform,the PID control method is used for composite material thin-walled work piece adjustment.To speed up PID control,the modeling of defonnation prediction by identifying ANSYS software key material properties parameters with experimental data is confirmed.Based on the prediction model,the adjustment process is summarized as optimization process and is solved by optimization algorithm.The simulation results show that,by taking optimized results as initial value,PID control is obviously improved in speed and effects.In order to improve the adjustment efficiency further,the adjustment method based on cases reasoning is confirmed.To make the most use of production data and adjustment experiments,the adjustment data in every time is stored as cases.This method is testified valid by experimental simulation results.