Early visual recognition was based on photography, which was not flexible enough and had a high error rate. Different manufacturers had different quality control requirements, making it impractical.

With the development of AI technology in recent years, Lichuang Hui has keenly discovered the application of AI technology in the automation production process of micro motors. By investing a large amount of research and development costs, we have solved the quality hazards caused by copper chips and copper sand in the gap of the rotor commutator after precision machining and dust cleaning. The detection reliability rate is over 99%, truly achieving online high-speed detection, equipped with a 10000 yuan high-speed industrial computer, and capable of cloud based online training and learning. We have also customized the detection points of poor quality (such as knife marks and stains) to achieve advanced technology industrialization.

Performance Characteristics

Photography: The product is photographed through CCD.

Calculation: Transfer the captured product images to an industrial computer for calculation, and use AI neural network algorithms for computation.

Analysis: Industrial computers convert photo data into computer language (numerical values).

Output: The software automatically compares the numerical values of the taken images with the range of values of good products, and issues the detection results.

Detailed inspection reports can be output according to requirements to trace quality issues at any time and help improve production processes.