Design of material sorting control system based on machine vision

In the process of product manufacturing, it is often necessary to combine and assemble different types of materials to achieve final assembly manufacturing of the product. However, there are currently widespread problems such as disorderly placement and difficult identification of multiple types of components in products, which seriously hinder the improvement of production efficiency and quality. It is urgent to use intelligent detection to identify the characteristics of different component types and achieve precise sorting to break through production difficulties. This study is based on this type of problem, using a combination of theoretical analysis, experimental platform construction, and application verification method and the machine vision detection technology combining 2D and 3D is used to design a disordered material sorting control system and optimize the material sorting process. By conducting in-depth analysis of material dimensions and field of view, a material pose estimation model is obtained to provide accurate basis for hardware selection; By building a visual inspection system based on PLC control and combining it with a robotic arm to achieve automated control; Finally, combining functions such as 3D template matching and 2D color recognition, an image point cloud model is formed to complete the data collection of unordered material sorting. After multiple tests and optimizations, the system has successfully achieved precise recognition and analysis of characteristic elements such as color and height of different materials, obtaining accurate recognition data. The efficiency and accuracy of material sorting have been significantly improved, solving production problems such as inaccurate positioning and difficult recognition of different types of components. This provides a reliable technical solution for accurate recognition and sorting of multiple types of components in product production.