Working principle of fully automatic visual selection machine (2)

Working principle of fully automatic visual selection machine (2)

3. Analysis and identification

   Algorithm application: Use machine learning or deep learning algorithms to analyze images and determine whether items meet standards.

   Defect detection: Identify surface defects, dimensional deviations, and other issues.

4. Decision making and Classification

   Classification: Based on the analysis results, the system classifies items as qualified or unqualified.

   Decision output: Send the classification results to the executing agency.

5. Execute actions

   Sorting: Unqualified products are removed through robotic arms or conveyor belts, and qualified products continue to enter the next process.

   Record: The system records each test result for easy traceability and analysis in the future.

6. Feedback and optimization

   Feedback mechanism: The system continuously optimizes the algorithm based on the detection results to improve accuracy.

   Continuous improvement: Regularly update software and hardware to maintain efficient device operation.