3D solid mold design and model quality control based on machine learning algorithm

With the rapid development of computer technology, the two-dimensional design in the field of mechanical design gradually transits to three-dimensional design, so the mold also enters the era of three-dimensional design. However, there are still many problems in the corresponding extrusion die design, such as the feasibility of the design scheme, the expression of graphics, the edge of die failure and the treatment methods, etc., which have always been urgent problems to be solved. Therefore, taking "Three-dimensional structure modeling and software system development of extrusion die" as the research topic is of great significance for improving the design methods and methods of aluminum extrusion die, increasing the design efficiency, freeing designers from a lot of repetitive manual labor, making it possible for them to devote their time to product research and development, and laying the foundation for the subsequent aluminum extrusion die. In this paper, a 3D solid mold algorithm based on machine learning is proposed. The binary classification method is used to learn the manually segmented 3D solid mold data, and the boundary edge function is obtained. Then the learned boundary edge function is used to test the 3D solid mold outside the database to detect the candidate boundary edges and obtain the segmented boundary region.