Adaptive Machining Method Of Pitot Tube Based On Measured Model

The air-speed tube was welded and formed by high-temperature casting of the front and back cheek plates and other parts.After high-temperature welding,there was deformation and uneven distribution of weld seam and weld liquid residue on the profile surface.Due to the poor consistency of the parts,it is impossible to mass production,so the point cloud matching method is adopted to extract the amount of grinding for each workpiece.When the structured light is used to obtain the point cloud data and the design model to compare and extract the amount of the measured point cloud data and the design model for automatic processing,the accurate extraction of the registration and amount of the measured point cloud data and the design model is the key factor to determine the processing quality.At present,due to the limitations of the algorithm,the point cloud fine matching has a high requirement for the initial pose and is easy to converge to the local,which has a great impact on the quality of automatic processing.The automatic processing of products with different deformation also needs to introduce adaptive processing methods.Therefore,aiming at the accurate matching of point cloud and the accurate extraction of point cloud quantity,this paper studies the coarse matching of point cloud,the fine matching algorithm of point cloud based on random simulation and Pincus theorem optimization;an adaptive abrasive belt grinding and polishing method is proposed for the automatic abrasive belt grinding and polishing of the airspeed tube with irregular deformation.According to the multi-batch and small-batch processing characteristics of air-speed tube,a special control software for precision abrasive belt polishing of air-speed tube was developed.Firstly,in order to solve the problems of large amount of measured point cloud data and noise influence of point cloud data,in order to accurately extract the amount and accelerate the efficiency of fine matching,the point cloud is preprocessed such as noise reduction and coarse matching.In the coarse matching stage,the three-dimensional feature is extracted through the detection of key points and the construction of local coordinate system,and the three-dimensional transformation matrix is calculated to complete the initial matching of the measured point cloud data of the airspeed tube.Secondly,the ICP algorithm based on stochastic simulation and Pincus theorem optimization is studied.The sample average method is used to convert the global optimal solution in the closed interval into the limit integral.When the limit integral approaches infinitely to a certain value,the solution of the integral limit is obtained,so as to solve the problem that the classical ICP algorithm falls into local optimum in numerical iteration.The principal component analysis method is used to solve the distance from the point to the surface for the solution of the additive amount between point clouds,so as to complete the solution of the additive amount.Then the adaptive grinding and polishing method is proposed for the automatic machining of the airspeed tube,and the processing scheme design of the abrasive belt grinding and polishing of the airspeed tube is completed.The grinding trajectory is planned by the weld information on the airspeed tube.At the same time,the adaptive grinding and polishing method is studied and designed according to the inconsistency of the airspeed tube workpiece.Finally,according to the production process and software requirements,the framework and function modules of the processing software are reasonably designed and developed.At the same time,an automatic polishing experiment is carried out with a certain type of airspeed tube,from the acquisition and processing of point cloud to the matching and extraction of point cloud.Finally,the robot adaptive polishing is carried out to verify the superiority of the matching algorithm and the rationality of the processing software.