Research On 3D Model Multi-connected Domain Data Representation Optimization Method For Laser Array Printing

The one-side forming technology is the development direction of 3D printing in the future.It subverts the traditional dot-line forming,and the manufacturing efficiency can be increased by several tens to hundreds of times.When the one-side forming is performed,the laser array needs to have a very high resolution,as a result,the amount of data in the 3D geometric model is very large.For this purpose,the paper studies the representation optimization of cross-section multi-connected domain data,mainly including cross-section similarity judgment method based on model surface contour and intelligent block optimization algorithm based on sparse lattice,aiming at reducing the data storage of cross-section multi-connected domains and ensuring high resolution and high precision of printing.In the first chapter,the research background and significance of this paper are introduced.The research status of model feature recognition and sparse matrix optimization of multi-connected domain is described.The main research contents and structure framework of this paper are expounded.In the second chapter,the rasterization technique of multi-connected domain of 3D printing geometric model is studied.The optimization methods of holes and redundant points generated by STL model are discussed.The acquisition process of multi-connected domain of 3D printing geometric model is introduced.Through the method of rasterization,the multi-connected domain of the cross section is processed,and the rasterized lattice and sparse matrix which are easy to process are obtainedIn the third chapter,the method of judging the similarity of multi-connected domain based on the surface contour of the model is proposed.The method of minimum bounding box is used to solve the approximate central axis of the geometric model.he surface contour acquisition methods of the three models are introduced.The micro contour segments between adjacent layers are analyzed,the cross-section multi-connected domain coincidence degree of adjacent layers is obtained,and the cross-sectional connected domain matrix representation of each layer and the difference matrix representation of adjacent layers are determined.In the fourth chapter,a multi-connected domain intelligent block optimization algorithm based on sparse lattice is proposed.The probability and statistical expectation of special non-zero blocks in sparse matrix are analyzed,and the proportion of irregular non-zero blocks in sparse matrix is determined.The boundary points of irregular non-zero blocks are obtained,and intelligent block storage is performed for sparse matrices.Compared with the traditional CRS algorithm and BCRS algorithm,the intelligent block optimization algorithm is more advantageous.In the fifth chapter,the applicability of multi-connected domain similarity judgment method based on model surface contour and multi-connected domain intelligent block optimization algorithm based on sparse lattice in multi-connected domain data representation optimization is verified by the example of engine block model and porous metal mesh ring model.The sixth chapter summarizes the research content and innovation of the full text,and looks forward to the shortcomings and follow-up work in the paper.