Research And Optimization On Support Array Of Flexible Detective Tooling For Composite Thin-walled Workpiece

The composite material has been widely used in the processing of large scale thin-wall workpieces in aviation industry due to its excellent properties.In order to ensure the assembly accuracy,the automatic and digital morphology detection of composite material is very important and necessary.However a kind of universal and simple flexible tooling platform is needed to conduct morphology detection.Therefore the design-optimization of flexible tooling is quite important both theoretically and practically.This paper takes the design-optimization of detective flexible tooling for composite material workpieces as background and focuses on the optimization of support array.In order to analyze the support and calibration state of workpiece,a widely used finite element analysis software ANSYS is introduced.The data interface and collaborative computing framework are developed to lay the software foundation of sequent optimization computation.Then the function of the support array is divided into two parts which are support and calibration.For the support function,the research group abstract this design-optimization problem as a combination optimization problem.Genetic algorithm is used to solve the optimization problem.In order to process the no-feasible solution,this paper presents a series strategies of abandon and replacement,single point modification and multiple point modification.The simulation results verify the effectiveness of these strategies to achieve the optimal design of supporting array.In order improve the efficiency of optimization further,the parallel modification strategy is proposed and the effective performance of the proposed method verifies its effectiveness.Based on the support function,the successive calibration is proposed aiming at the calibration of composite material.The position of calibration point is located by subtractive method and magnitude of calibration force is optimized through particle swarm optimization algorithm.Correspondingly the number of support for calibration can be determined.The simulation results show the effectiveness of this method.