3D Vision Guides Loading-Car Nut

Video For The Project

In recent years, the traditional automobile industry has sought to upgrade, new energy vehicles have continued to develop, and the transformation of the automobile industry has spawned a large number of automation needs. Retrofitting with high standard requirements, complex process flow and a large variety of workpieces are long-term challenges faced by the automation of the automotive industry.

Various typical transformations, such as workpiece loading and unloading, positioning assembly, etc., have high requirements for technical indicators such as cycle time, precision, and stability.

The new-generation vision solution based on the Mixed AI physics engine can effectively cope with complex working conditions such as a large variety of workpieces, reflective surfaces, complex structures, disorderly stacking, and ambient light interference.

Project Description

In the nut automatic welding process, the feeding, positioning and welding of nuts are mostly carried out by hand or manually placed on the positioning tool, resulting in low welding efficiency and high production costs. The project uses the cross-dimensional intelligent 3D vision software system to guide the robot to orderly grab and place the four specifications of the welding parts on the loading table, and place the materials at the designated positions to complete the loading and unloading.

Project Specification

Beat: 1-2s
Accuracy: <=2mm, <=1 degree
Stability: >1000 times per day
Accuracy: >99.9%

Solution Advantage

The algorithm model is trained on the MixedAI platform, and workpieces in different environments are simulated in advance, which can effectively resist changes in the environment and viewing angle, with high recognition accuracy and good system stability.

Compatible with a variety of different types of workpieces, exclusive support provides CAD to quickly train new algorithm models, and supports flexible production changes.

Reduce labor intensity, improve welding efficiency, and reduce production costs.