ai tester used in industrial quality inspection
The application of ai tester in the field of industrial quality inspection is mainly reflected in the deep integration of AI+machine vision, which achieves defect detection automation through intelligent recognition algorithms and optimizes production processes. The following are specific application scenarios and technological advantages:
Application scenarios
Real time quality inspection of friction stir welding
The friction stir welding process widely used in fields such as new energy vehicles and aerospace is difficult to cope with complex weld defects using traditional detection methods. Through the AI ultrasonic detection system and based on the Yolov5 model, automatic defect recognition and process optimization were achieved within 0.1 seconds. The daily inspection capacity increased from 300 pieces to 3000 pieces, and the yield rate increased from 65% to 95%.
Production monitoring of composite materials
In the production of composite materials such as wind turbine blades, the AI ultrasonic testing system achieves real-time quality detection on the assembly line, increasing efficiency by 80% and the yield rate from 70% to 90%.
Full dimensional inspection of plastic bottles
Detect bottle cap abnormalities (tightness, damage), liquid level deviation, foreign objects inside the bottle, and dimensional accuracy to ensure the safety and consistency of beverage packaging.
Technical advantages
Intelligent recognition algorithm: Deep learning models can accurately identify defects such as micro scale cracks and pores, with an accuracy rate of 99.5%.
Dynamic adaptive detection: Real time optimization of parameters based on materials and working conditions to adapt to complex production environments.
Whole life cycle management: The detection results are associated with the production database to achieve a closed-loop system of quality traceability and process improvement.
Deployment Challenge
Industrial quality inspection faces problems such as complex component structures, diverse types of defects (such as small pores and scratches), and difficulty in data acquisition. It is necessary to combine multi feature imaging optical strategies and semi supervised algorithms to improve detection stability.
