Research On Adaptive Layering Algorithm Based On Point Cloud Data In Rapid Prototyping

Layering technology is the key link of rapid prototyping technology.The quality of the layering algorithm will directly affect the layering efficiency and molding accuracy of the model.Aiming at the problem that the existing layering algorithm cannot effectively adjust the contradiction between the layering efficiency and the model accuracy,the data reduction algorithm,adaptive layering algorithm,and contour line fitting algorithms applied in the layering technology were studied.The data format processed by the existing molding machine is an STL(Standard Template Library)model.When the STL model is established,the point cloud data obtained must be triangulated,which will increase the layering error and reduce the layering efficiency.In view of this problem,this paper proposes an adaptive layering algorithm based on point cloud data.This algorithm directly processes point cloud data,which saves the modeling process and greatly improves the efficiency of layering.However,the point cloud data obtained by 3D scanning equipment will have a large number of redundant points,which will affect the accuracy of the model.In view of this problem,this paper conducts a point cloud data reduction algorithm research.Through research on several streamlining algorithms,it is found that several commonly used streamlining algorithms cannot effectively retain the feature points of the model.In order to solve this problem,this article decided to use a point cloud data streamlining algorithm based on the normal angle.It is learned that the algorithm can effectively retain the detailed characteristics of point cloud data.First,the current research status of the adaptive layered thickness adjustment algorithm is analyzed,and an algorithm for determining the optimal layer thickness by continuously adjusting the error size generated by the steps is proposed.By analyzing the cause of the error,the formula relationship between layer thickness and model error caused by the step effect is deduced.The pre-grouping process is performed by using the idea of quick sorting,which improves the efficiency of finding point cloud data in any interval at a later stage.Secondly,the current research status of contour extraction is introduced.By analyzing the existing contour fitting algorithm,a cubic B-spline algorithm is proposedfor contour extraction.In order to reduce the calculation error,this paper determines the neighborhood of the point cloud data,establishes a local coordinate system for the points in the neighborhood,calculates the vector of each point by the least squares method,and then obtains the boundary feature points,and finally uses the cubic B-spline.The algorithm performs curve fitting.Finally,in order to verify the practicability and significance of the research method,and also to promote the progress of the project,the paper applied the research method to deal with the test pieces,and realizedthe the research method of the paper.The layering efficiency indicates that the effect was better.