Implementing AI Computer vision solution in a high speed production environment
Our customer - the largest manufacturer and supplier of wooden dowels for the furniture industry, manufacturer and supplier of wooden dowels for the furniture, toy and brush industries.
Business problem
In a demanding wooden dowel production environment, their biggest challenge was dealing with a large number of visual defects on a wide range of their products.
Existing production lines produced up to 1500 wooden dowels per minute.
Up to 30% of produced wooden dowels had visual defects.
Existing production lines required large mechanical or machinery changes to reduce defect rates.
PROJECT OBJECTIVES
Implement AI Computer vision solution without disrupting existing production lines or introducing big mechanical changes.
Find a non disruptive solution to change faulty ejection system of the existing solution.
Achieve false positive rates of <0.2% - overall number of defected dowels not recognized correctly.
Achieve false negative rates of <5% - overall number of dowels without defects being recognized and ejected as defected dowels.
Recognize defects as small as 2mm in size.
Create a versatile solution, capable of recognizing visual defects in 40 different dowel types.
Create a flexible solution with the ability to adjust accepted defect size for varying business needs.
Dowel defects
Our AI-Powered Solution
Cybernetics trained their AI models on thousands of dowel images in just 4 weeks.
These models performed real-time analysis with lightning speed—making decisions in as fast as 25 milliseconds per image.
Engineers enhanced the existing ejection mechanisms to allow our AI models to take control —no major machinery changes were required.
Our out of the box AI analytics system helped to identify types of defects, size of defects, defects per shift and other tendencies.
Results
Defect rate dropped dramatically from ~30% to 0.2%.
Each defect was identified with 99.8% accuracy.
No intervention with the existing system. The entire AI based detection system worked independently to the existing system.
The system met all performance targets: speed, accuracy, flexibility, all integrated seamlessly into the existing production flow.
We are excited about the results achieved, the improved quality of the production line, and the additional data analytics created. The system not only removes defective parts, but also collects data based on defect types, sizes, and shift statistics, allowing the business to make data-driven improvements.
Why This Matters
This case is a powerful example of smart manufacturing in action—where AI adds immense value by enhancing quality, minimizing waste, and enabling rapid adaptation to diverse product lines. It's a blueprint for how production floors can be revolutionized without tearing everything apart.
