Method and device for determining parameters of 3D mold debugging

Abstract

The present invention provides a method and device for determining debugging parameters of a three-dimensional mold. The method includes: acquiring a photographed image corresponding to a target three-dimensional mold; inputting the photographed image into a three-dimensional mold defect detection model, and obtaining the output value of the three-dimensional mold defect detection model Defect information, the three-dimensional mold defect detection model is obtained by using the first sample image training, the first sample image contains the first defect labeling information, the shooting information of the first sample image and the captured image Similarly, the shooting information includes the installation position and lens angle of the shooting device; according to the shooting image and the intrinsic correlation model, the debugging parameter information corresponding to the defect information is determined. The embodiment of the present invention improves the processing efficiency.

Description

technical field [0001] The invention relates to the technical field of supercomputing, in particular to a method and device for determining parameters of three-dimensional mold debugging. Background technique [0002] The method of using three-dimensional molds to produce and process product parts is widely used in industrial production. Taking automobile panels as an example, they are processed by stamping and other methods using automobile panel molds. [0003] In order to obtain qualified product components, it is first necessary to design and manufacture a three-dimensional mold. The quality of the three-dimensional mold will directly affect the quality of the product parts. For example, if there are design or processing defects on the surface of the three-dimensional mold, it will directly lead to many problems such as rough surface and surface deformation of the product parts processed by the mold. [0004] Therefore, in the process of designing and manufacturing 3D ...

AI Technical Summary

Problems solved by technology

[0006] Relying on manual experience to identify defects and then debug parameters is inefficient. On the one hand, relying on manual experience requires training staff with identification and testing experience. The training period is long. When there are many detection and identification tasks, there will be insufficient staff. , which further leads to low efficiency. On the other hand, the speed and efficiency of manual identification and debugging parameters cannot be compared with the speed and efficiency of computer identification

Images